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lediefektivekotin

lorabutton

lorawanworkshop

LoRaWAN workshop code and experiments

modbuslora

motiondetector

noisesensorworkshop

opentripplanner

An open source multi-modal trip planner

randomthings

Test rig for SensorThings with JSON-LD

raspiansible

Ansible playbooks to install automatically various applications in Raspberry Pi

rpi-air-workshop

Instructions for crowdsourced air quality measurement RPi box

rpisensorbox

Reads sensors connected to a Raspberry Pi and exposes the readings as Bluetooth Smart (BLE) services and characteristics.

ruuvitag

RuuviTag expreiments

sensehel

Data portal and backend to manage dynamic consent in smart homes.

sensorexperiments

Various sensors tested to work with ESP/Arduino/Rasperry Pi etc.

serialforward

ESP-IDF serial forward. Exposing T-Beam GPS serial port to T-Beam usb serial. Can be used for ublox config in windows.

tft_infonaytto

verstaan ovessa oleva näyttöpurkki

trafficcounter

ttgo_lora32

Some sample code for the TTGO ESP32 Oled Lora board.

tykin_ohjaus

Tykin etäohjaus mqtt:llä

tykin_ohjaus_2

Musta Excelvan

uimarantasensori

Measure swimming water temperature!

urbanecoislands

Software created for Urban Eco Islands project

vesiviljely

visuexperiments

wasis

WASIS (Wildlife Animal Sound Identification System) is a public-domain software that recognizes animal species based on their sounds. From a partnership between Laboratory of Information Systems (LIS) and Fonoteca Neotropical Jacques Vielliard (FNJV) of the Institute of Biology of the University of Campinas (UNICAMP), the main goal of this project is to design a tool which supports multiple algorithms to help scientists and general public on the identification of species. And why is it important? Besides the curiosity itself of knowing which species is calling, we can possibly identify invasive species in a certain area, help on establish migratory patterns from sounds of different locations during a specific period of time, as well as support long duration recording analysis. The software architecture was designed to support multiple audio feature techniques that extract meaningful information of animal sounds, and classification algorithms that use these extracted data to match against respective audio information stored in the software data repository. The main purpose of these algorithms is to design a classification scheme that can best predict the classes/labels for unseen data (an audio file that we want to identify), process similar to the human brain ability to differentiate among a wide range of sounds and to assign them to previously heard sounds.