Radio interference analysis tool based on GNU Radio

Avoiding interference is one of the main challenges in radio communications. Interference hunting is usually done with costly instruments. In this work, a cost efficient portable tool is described for interference analysis that is based on software defined radio. A generic radio board implements the RF front end, while flexible signal processing is carried out on a personal computer. The proposed tool analyses spectrum characteristics spanning from 70 MHz to 6 GHz band, detects radio interference signals and helps to identify the type of radio technology used by the source transmitter.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Utilizing Machine Learning for Signal Classification and Noise Reduction in Amateur Radio

In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments. In this paper, we explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations. We investigate the feasibility and effectiveness of employing supervised and unsupervised learning algorithms to automatically differentiate between desired signals and unwanted interference, as well as to reduce the impact of noise on received transmissions. Experimental results demonstrate the potential of machine learning approaches to enhance the efficiency and robustness of amateur radio communication systems, paving the way for more intelligent and adaptive radio solutions in the amateur radio community

Improved detection techniques in autonomous vehicles for increased road safety

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.A futura adoção em massa de Veículos Autônomos traz um potencial significativo para aumentar a segurança no trânsito para ambos os motoristas e pedestres. Como reportado pelo Departamento de Transportes dos E.U.A., cerca de 94% dos acidentes de trânsito são causados por erro humano. Com essa realidade em mente, a indústria automotiva e pesquisadores acadêmicos ambicionam alcançar direção totalmente automatizada em cenários reais nos próximos anos. Para tal, algorit- mos mais precisos e sofisticados são necessários para que os veículos autônomos possam tomar decisões corretas no tráfego. Nesse trabalho, é proposta uma técnica melhorada de detecção de pedestres, com um aumento de precisão de até 31% em relação aos benchmarks atuais. Em seguida, de forma a acomodar a infraestrutura de trânsito já existente, avançamos a precisão na detecção de placas de trânsito com base em Redes Neurais Convolucionais. Nossa abordagem melhora substancialmente a acurácia em relação ao modelo-base considerado. Finalmente, ap- resentamos uma proposta de fusão de dados precoce, a qual mostramos surpassar abordagens de detecção com um só sensor e fusão de dados tardia em até 20%.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The future widespread use of Autonomous Vehicles has a significant potential to increase road safety for drivers and pedestrians alike. As reported by the U.S. Department of Transportation, up to 94% of transit accidents are caused by human error. With that reality in mind, the auto- motive industry and academic researches are striving to achieve fully automated driving in real scenarios in the upcoming years. For that, more sophisticated and precise detection algorithms are necessary to enable the autonomous vehicles to take correct decisions in transit. This work proposes an improved technique for pedestrian detection that increases precision up to 31% over current benchmarks. Next, in order to accommodate current traffic infrastructure, we enhance performance of a traffic sign recognition algorithm based on Convolutional Neural Networks. Our approach substantially raises precision of the base model considered. Finally, we present a proposal for early data fusion of camera and LiDAR data, which we show to surpass detection using individual sensors and late fusion by up to 20%

Inferring Cognitive Load using Wireless Signals

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging

Inferring Cognitive Load using Wireless Signals

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging