Development of an acoustic modem using synthesizable microcontroller

In this thesis, an acoustic modem is developed on a digital programmable device for underwater communications. The system consists of a synthesize microcontroller as well the different elements necessaries for the modulation and demodulation of the signals. That should be developed for the design. The objective is to develop the system using a high-level hardware description language and to show the system’s functioning on a flexible platform (a programable logical device) with the perspective that, in the future, it can be implemented in a personalized integrated circuit and thus obtain a compact and energy efficient system

Desenvolupament d’un mòdem acústic mitjançant un microcontrolador sintetitzable

En aquest treball es desenvolupa un mòdem acústic per a comunicacions submarines sobre un dispositiu digital programable. El sistema està compost per un microcontrolador sintetitzable més els diferents elements necessaris per a la modulació i desmodulació dels senyals que cal desenvolupar per al disseny. L’objectiu és desenvolupar el sistema utilitzant un llenguatge d’alt nivell de descripció hardware i demostrar el funcionament del sistema sobre una plataforma flexible (un dispositiu lògic programable) amb la perspectiva que, en un futur, pugui ser implementat en un circuit integrat a mida i així obtenir un sistema compacte i de baix consum

Desenvolupament d’un mòdem acústic mitjançant un microcontrolador sintetitzable

En aquest treball es desenvolupa un mòdem acústic per a comunicacions submarines sobre un dispositiu digital programable. El sistema està compost per un microcontrolador sintetitzable més els diferents elements necessaris per a la modulació i desmodulació dels senyals que cal desenvolupar per al disseny. L’objectiu és desenvolupar el sistema utilitzant un llenguatge d’alt nivell de descripció hardware i demostrar el funcionament del sistema sobre una plataforma flexible (un dispositiu lògic programable) amb la perspectiva que, en un futur, pugui ser implementat en un circuit integrat a mida i així obtenir un sistema compacte i de baix consum

Virtual Sensing of Hauler Engine Sensors

The automotive industry is becoming more dependent on sustainable and efficient systems within vehicles. With the diverse combination of conditions affecting vehicle performance, such as environmental conditions and drivers' behaviour, the interest in monitoring machine health increases. This master thesis examines the machine learning approach to sensor reconstruction of hauler engine sensors for deviation detection applications across multiple domains. A novel proposal for sequence learning and modelling was by introducing a weighted difference of sequence derivatives. Impacts of including differences of derivatives assisted the learning capabilities of sequential data for the majority of the target sensors across multiple operating domains. Robust sensor reconstruction was also examined by using inductive transfer learning with a Long Short-Term Memory-Domain Adversarial Neural Network. Obtained results implied an improvement in using the Long Short-Term Memory-Domain Adversarial Neural Network, then using a regular Long Short-Term Memory network trained on both source and target domains. Suggested methods were evaluated towards model-based performance and computational limitations. The combined aspects of model performance and system performance are used to discuss the trade-offs using each proposed method

Virtual Sensing of Hauler Engine Sensors

The automotive industry is becoming more dependent on sustainable and efficient systems within vehicles. With the diverse combination of conditions affecting vehicle performance, such as environmental conditions and drivers' behaviour, the interest in monitoring machine health increases. This master thesis examines the machine learning approach to sensor reconstruction of hauler engine sensors for deviation detection applications across multiple domains. A novel proposal for sequence learning and modelling was by introducing a weighted difference of sequence derivatives. Impacts of including differences of derivatives assisted the learning capabilities of sequential data for the majority of the target sensors across multiple operating domains. Robust sensor reconstruction was also examined by using inductive transfer learning with a Long Short-Term Memory-Domain Adversarial Neural Network. Obtained results implied an improvement in using the Long Short-Term Memory-Domain Adversarial Neural Network, then using a regular Long Short-Term Memory network trained on both source and target domains. Suggested methods were evaluated towards model-based performance and computational limitations. The combined aspects of model performance and system performance are used to discuss the trade-offs using each proposed method