Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Processor evaluation for low power frequency converter product family

Tässä työssä tutkitaan markkinoilla olevia tai lähitulevaisuudessa markkinoille saapuvia prosessoreja käytettäväksi pienitehoisissa taajuusmuuttajissa. Tutkimuksen tarkoitus on selvittää prosessorin sopivuutta sovellukseen, jossa hinta on merkittävä tekijä. Tutkimuksessa esitettyjen vaatimusten perusteella houkuttelevimmat prosessorit otetaan tarkempaan tutkimukseen. Tarkemman selvityksen jälkeen vaatimuksia teknisesti mahdollisimman tarkasti vastaavat prosessorit pyydettiin valmistajalta testattavaksi. Testaaminen suoritettiin lopulta viidelle eri prosessorille, joista kaksi perustui samaan ytimeen. Testaamisen tavoitteena on selvittää prosessorin sopivuus käyttökohteeseensa. Sopivuus testattiin suorittamalla prosessoreissa taajuusmuuttajakäyttöä mallintavaa testikoodia. Tuloksina testikoodin ajamisesta saatiin tietyissä aliohjelmissa kulutettu aika sekä kulutetut kellosyklit. Suorituskyvyn lisäksi testaukseen kuului prosessorikohtaisen kääntäjän aikaansaaman koodin koko. Aliohjelmat sisälsivät sekä aritmeettisia, että loogisia operaatioita, joiden kombinaationa mahdollisimman hyvä sopivuus saatiin selvitettyä.The aim of this thesis is to study processors to be used in a low power frequency converter. Processors under investigation must be currently or in the near future in the market. The purpose is to examine suitability of a processor to an application in which price is an essential factor. The requirements presented in this study will determine which processor will be reviewed more closely. After a precise review, processor vendors was asked to provide as corresponding device as possible to a test. Testing was accomplished eventually with five different processors of which two were based on a same core. The aim of the testing was to investigate suitability of the processors to their target task. Suitability was tested by executing code that models frequency converter application. As a result, spent time and clock cycles are presented in certain functions. In addition to performance, the testing included evaluation of the size of the output code the compilers created. Functions under test consisted of a combination of arithmetic and logic operations that was used to interpret the suitability of the processor

Rocket Cam: Low Frequency Analog Transmission of Digital Video

The camera module provides data for improving models of dynamic events on Orbital ATK Corp. rockets and aids in troubleshooting, if necessary. Video images provide a valuable addition to the strain, vibration, shock, and acoustic data used for modeling dynamic events, such as stage separations. The cameras can record a duration of video data suitable for capturing a dynamic event and of high enough quality to aid in its modeling. The module readily integrates into the rocket’s current analog data collection systems. The project has further relevance to any other application that necessitates video data transmission over similar limited-bandwidth, analog data channels. Though errorless data transmission was not achieve, over 99% of the digital by bytes transmitted where recovered to within 99% accuracy. This level of error is not suitable for compressed data. However, the primary sources of error can potentially be resolved by adaption to a more permanent prototype platform

A Neural Network Framework for Small Microcontrollers

This paper presents a lightweight and compact library designed to perform convolutional neural network inference for microcontrollers with severe hardware limitations. A review of similar open source libraries is included and an experiment is developed to compare their performance on different microcontrollers. The proposed library shows at least a 9 times improvement over the implementation of Google Tensorflow Lite with respect to memory usage and inference time.Workshop: WASI – Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informátic

Exploring opportunities in TinyML

Internet of Things (IoT) has acquired useful and powerful advances thanks to the Machine Learning (ML) implementations. But the implementation of Machine Learning in IoT devices with data centers has some serious problems (data privacy, network bottleneck, etc). Tiny Machine Learning (TinyML) arose in order to have an independent edge device executing the ML program without the necessity of any data center. But there is still the need for high performance computers to train the ML model. But, can this situation improve? This project goes through TinyML and two TinyML techniques capable to train the ML model on-device (what we call TinyML On-Device Learning or TinyODL): TinyML with Online-Learning (TinyOL) and Federated Learning (FL). We study both techniques in a theoretical analysis and try to develop one TinyODL app.Internet of Things (IoT) ha obtingut uns forts avantatges molt usables gràcies a les implementacions del Machine Learning (ML). Però la implementació del Machine Learning en dispositius IoT utilitzant centres de dades porta una sèrie de problemes a tenir en compte (privacitat de les dades, el coll d'ampolla de la xarxa, etc.). Tiny Machine Learning (TinyML) va sorgir amb l'objectiu de tenir dispositious IoT independents executant el programa d'ML sense la necessitat d'un centre de dades. Però encara hi ha la necessitat de fer servir ordinadors d'alta potència per poder entrenar el model d'ML. Així i tot, es pot millorar aquesta situació? Aquest projecte estudia el TinyML i dues de les seves tècniques, del que anomenem TinyML On-Device Learning o TinyODL, capaces d'entrenar el model d'ML en el mateix dispositiu (on-device learning): TinyML with Online Learning (TinyOL) i Federated Learning (FL). S'estudien les dues tècniques des d'una anàlisi teòrica i provem de desenvolupar una aplicació TinyODL.Internet of Things (IoT) ha obtenido unas muy buenas y usables mejoras gracias a las implementaciones del Machine Learning (ML). Pero la implementación de Machine Learning en dispositivos IoT utilizando centros de datos conlleva una serie de problemas a tener en cuenta (privacidad de los datos, el cuello de botella de la red, etc.). Tiny Machine Learning (TinyML) surgió con el objetivo de tener dispotivios IoT independientes ejecutando el programa de ML sin la necesidad de un centro de datos. Pero aún existe la necesidad de usar ordenadores de alta potencia para poder entrenar el modelo de ML. Aún así, se puede mejorar esta situación? Este proyecto estudia el TinyML y dos de sus técnicas, de lo que llamamos TinyML On-Device Learning o TinyODL, capaces de entrenar el model de ML en el mismo dispotivio (on-device learning): TinyML with Online Learning (TinyOL) y Federated Learning (FL). Se estudian las dos técnicas desde un anáisis teórico y probamos de desarrollar una aplicación TinyODL

Development of PCI Embedded Card Useful for Microcontroller Trainer

Not availabl