I am studying the new version of google analytics 4 and I am interested of the predictive metric of churn.
How does it works?
What features is the algorithm based on? how is it calculated?
Related
I have an application that I have distribute on google play store. Can I check the average network bandwidth data of the application collected from the user? Average KB/s the application usually use?
What I have check but I cannot find such a report
Google Play Console
Firebase Console Performance Monitoring (There are network request data but it is to detail per session per request)
So far I think I can utilize the Firebase data or doing some manual measurement on my device myself, but I think it is inefficient and not accurate enough.
Any idea where can I got report like that in google play console or firebase? Or any other SDK or solution I can use to get network bandwidth report?
For information I use flutter to develop the application.
In the Ionic framework, is there a way to read the current cellular signal strength & other data (currently connected tower, band, etc..) via the Ionic APi?
Thanks!
Ionic is a frontend framework so no you can't read low level network related data via its API. Also if you are referring to ionic-native, thats only a wrapper around the APIs exposed by different cordova plugins to provide some convenience features such as autocomplete, promise-callbacks and change-detection out of the box.
What you can do is searching for a cordova-plugin which can deliver this kind of information (for example: cordova-plugin-signal-strength (Android only)). Another possibility is to create your own plugin and implement the native parts yourself.
I am a medical student working this summer to create a wearable device for children with cerebral palsy. The device should be capable of streaming via bluetooth LE to ios or android app, and I am hoping to have two bluetooth capable sensors each streaming (realtime) data samples (size: 3 float values per sample) at around 50 Hz (or the highest frequency possible). I do see that there is a react native library for BLE (https://github.com/innoveit/react-native-ble-manager), however I am concerned that I may run into performance issues if I go the route of developing with react native instead of native ios/android. Could you please either explain what route you think is best or link me some resources which may help? Main questions I have are 1. Is this possible? 2. What BLE library do you recommend 3. Will I be sacrificing on BLE real time data streaming performance by going with react native? THANK YOU VERY MUCH
I'm developing navigation system for my university as some kind of research activity. I'm using SVGKit to display floor plans. And now I need to provide user locationing service for navigation and tracking. So here's my questions:
1) Do I need some special hardware installed in university (Cisco MSE for example, or some cheaper analogues), or I can apply some software/technologies to our current hardware for server-side user location determining? If I do, what equipment do I need for it? I mean, it would be one unit for the whole university, or one per each floor, or what?
2)
Q: Why doesn't the Redpin iPhone client conform to the iPhone SDK
Agreement? A: Apple does not provide a public API to retrieve WiFi
data. In order to get the iPhone client working we had to use a
private API, which is disallowed by the iPhone SDK Agreement.
(c) http://redpin.org/faq.html Does it mean that RedPin is unacceptable in AppStore, so I can't use it?
3)Does Navizon I.T.S. requires some specific hardware equipment except standart routers?
Thank you all, maybe you can offer me better solutions, I hope. Thanks in advance.
Indoor positioning is a very vast field and many different solutions are available which all use a different combination of hardware/software. Some need no specific hardware to work, others need a very expensive infrastructure to be put in place. In the end, it all depends on the accuracy you are trying to achieve. Here are the most common solutions used, I ordered them by the type of technology used:
Wifi: two main techniques are used here, trilateration and fingerprinting. Both do not require specific hardware if your uni already has deployed access points (APs). Trilateration converts signal strength to distance and then intersect circles (almost exactly like GPS). In general this has poorish accuracy and you need to know the exact position of APs for it to work. Fingerprinting is a pattern matching technique where you first build a wireless map of the environment and then match the measurement against this map.
Bluetooth: same techniques as above can be used with Bluetooth nodes. Of course, there's less Bluetooth nodes than Wifi so you might need to deploy some extra nodes for it to be accurate enough. Same accuracy as Wifi (roughly 5 meters)
Dead reckoning: uses an accelerometer, gyroscope and compass to calculate the speed of heading of the user. Needs to be initialized and calibrated regularly by another absolute positioning technique. Subject to drift so accuracy degrades quickly over time. Upside is its very cheap, no extra hardware or initial survey phase are needed.
UWB: very accurate techniques based on time of flight measurements. Requires expensive hardware for both transmitter and receiver. You can achieve cm accuracy with this but it's probably not what you're after
This is still an field of research so it's not that easy to find something that just works. I suggest contacting the IT department of your university, if they run a Cisco system, I know some of them provide some sort of positioning capabilities but I don't have much details.
As for your iPhone question, any app that accesses the private API to access Wifi measurements will be rejected by the App store, so you won't be able to publish anything that relies on Wifi. You can still use it for research purpose though, you'll just have to figure out the code yourself as there's no official documentation (some unofficial doc is out there though)
Good luck!
What is the best way to get ocropus running on iOS and/or android?
I'm interested in using Ocropus to digitize some content on mobile devices. I'm largely interested in using a trained 'language' model to make predictions on the device. Training will occur offline and off device. I know a few people have got tesserect running on mobile devices, but I'm unable to find much information on doing the same with Ocropus. I'd greatly appreciate a slice of your collective wisdom in an effort to avoid wasting days taking the wrong path.
Would it be easier to just prototype the algorithm using the scripts, then grab the specific c++ code of interest and include it directly in my application. Or best to compile as a static/dynamic library?
It would be better setting up a simple web service that uses Ocropus or any OCR library for that matter. Then have your smartphone application make requests to the web service. OCR is a CPU intensive process, so it's appropriate to move it off of the phone.