Semi-supervised Convolutional Neural Networks for Identifying Wi-Fi Interference Sources

We present a convolutional neural network for identifying radio frequency devices from signal data, in order to detect possible interference sources for wireless local area networks. Collecting training data for this problem is particularly challenging due to a high number of possible interfering devices, difficulty in obtaining precise timings, and the need to measure the devices in varying conditions. To overcome this challenge we focus on semi-supervised learning, aiming to minimize the need for reliable training samples while utilizing larger amounts of unsupervised labels to improve the accuracy. In particular, we propose a novel structured extension of the pseudo-label technique to take advantage of temporal continuity in the data and show that already a few seconds of training data for each device is sufficient for highly accurate recognition.Peer reviewe

Langattoman anturijärjestelmän suunnittelu hissin kunnonvalvontaan

The aim of this thesis is to design, implement and validate a prototype sensor system for wirelessly monitoring the condition of an elevator. One purpose of such system is long-term monitoring of the elevator, which could help detecting emerging issues in advance. The system could also be used by maintenance personnel as a real-time troubleshooting tool, assisting in device commissioning and maintenance situations, for example. Due to requirements set by the use case and the elevator environment, a wireless, battery operated device is required. A significant part of this thesis focuses on aspects of selecting a suitable wireless technology, as the choice has a large impact on the performance of the system. Different wireless solutions are researched and compared, and a technology using the sub-GHz frequency bands is selected for the prototypes. The prototype sensor is designed based on the choice of the wireless technology. The hardware and software of the sensor nodes, as well as a PC tool for collecting data, are presented. The performance of the sensor nodes and the functionality of the whole sensor system is tested using a batch of manufactured prototype devices. The prototypes and the selection of the wireless technology are considered successful. Minor improvements to the design of the prototypes are presented at the end of the thesis.Tässä työssä suunnitellaan, toteutetaan ja arvioidaan hissin kunnonvalvontaan tarkoitetun langattoman anturijärjestelmän prototyyppi. Järjestelmän käyttötarkoituksena on sekä hissin pitkäaikainen kunnonvalvonta, jonka avulla voidaan havaita orastavia vikoja etukäteen, että mahdollisuus käyttää laitetta reaaliaikaisena vianetsintätyökaluna, jota huoltohenkilöstö voisi käyttää apunaan esimerkiksi hissin käyttöönoton ja huoltotilanteiden yhteydessä. Käyttökohteen asettamien vaatimusten takia on tarpeen suunnitella langaton, paristokäyttöinen anturi. Merkittävä osa tästä työstä käsittelee langattoman teknologian valintaan vaikuttavia tekijöitä, sillä valinnalla on suuri vaikutus järjestelmän suorituskykyyn. Työssä kartoitetaan ja vertaillaan erilaisia langattomia ratkaisuita, joista prototyyppilaitteeseen valitaan sub-GHz-taajuuksia käyttävä langaton teknologia. Anturilaitteen prototyyppi suunnitellaan valitun langattoman teknologian pohjalta. Anturilaitteen elektroniikka, ohjelmisto, ja PC-tietokoneelle toteutettu datankeräystyökalu esitellään. Anturien suorituskyky sekä koko järjestelmän toiminta testataan valmistetuilla prototyyppikappaleilla. Prototyyppi arvioidaan toimivaksi ja langattoman teknologian valinta onnistuneeksi. Työn lopussa esitetään pieniä parannusehdotuksia prototyypin suunnitteluun

Design And Implementation Of An Autonomous Wireless Sensor-Based Smart Home

The Smart home has gained widespread attentions due to its flexible integration into everyday life. This next generation of green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. It can integrate diversified physical sensed information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. Although smart homes are gaining popularity due to their energy saving and better living benefits, there is no standardized design for smart homes. In this thesis, a smart home design is put forward that can classify and predict the state of the home utilizing historical data of the home. A wireless sensor network was setup in a home to gather and send data to a sink node. The collected data was utilized to train and test a classification model achieving high accuracy with Support Vector Machine (SVM). SVM was further utilized as a predictor of future home states. Based on the data collection, classification and prediction models, a system was designed that can learn, run with minimal human supervision and detect anomalies in a home. The aforementioned attributes make the system an asset for senior care scenarios