Coping with saturating projection stages in RMPI-based Compressive Sensing

5noThough compressive sensing hinges on extracting linear measurements from the signals to acquire, actual implementations introduce nonlinearities whose effect can be far from negligible. We here address the problem of saturation in the circuit blocks needed by a Random Modulation Pre-Integration architecture. © 2012 IEEE.partially_openopenMangia M.; Pareschi F.; Rovatti R.; Setti G.; Frattini G.Mangia, M.; Pareschi, F.; Rovatti, R.; Setti, G.; Frattini, G

Adapted Compressed Sensing: A Game Worth Playing

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing

Conversor configurável analógico para informação.

Nos conversores Analógicos Digitais (ADC) com frequência de conversão baseada no Teorema de Nyquist, o parâmetro básico para orientar a aquisição é a largura de banda do sinal. O tratamento da informação e a remoção da redundância são realizados após a representação digital obtida do sinal. A Amostragem Compressiva foi proposta como uma técnica de digitalização que explora a esparsidade do sinal em um determinado domínio, para capturar apenas seu conteúdo de informação, com uma taxa que pode ser menor do que a preconizada pelo Teorema de Nyquist. As arquiteturas em hardware para implementar a Amostragem Compressiva são chamadas de Conversores Analógicos para Informação (AIC). Os AIC propostos na bibliografia exploram a esparsidade do sinal em um determinado domínio, e por isso cada arquitetura é especifica para uma classe de sinais. Nesta tese propõe-se um AIC configurável, baseado em arquiteturas conhecidas, capaz de adquirir sinais de várias classes, alterando seus parâmetros de configuração. No trabalho desenvolveu-se um modelo computacional, que permite analisar o comportamento dinâmico do AIC, e dos parâmetros de hardware propostos, bem como foi feita a implementação física da arquitetura proposta. Verificou-se a adaptabilidade dessa arquitetura a partir dos resultados obtidos, pois foi possível fazer a aquisição de mais de uma classe de sinais.In analog-to-digital converters (ADC) based on Nyquist Theorem, the basic parameter to guide acquisition is the bandwidth of the signal. The information processing and redundancy removal are performed after the digital representation obtained from the signal. Compressed Sensing was proposed as a digitalization technique that exploits the sparsity of the signal in a given domain to capture only its information content, at a rate that may be lower than that advocated by the Nyquist Theorem. The hardware architectures to implement Compressed Sensing are called Analog to Information Converters (AIC). The AICs proposed in the bibliography exploit the sparsity of the signal in a given domain, and therefore each architecture is specific for a class of signals. This thesis proposes a configurable AIC, based on known architectures, capable of acquiring signals from several classes, changing its configuration parameters. A computational model was developed to analyze the dynamic behavior of AIC and proposed hardware parameters, as well as the physical implementation of the proposed architecture. It was verified the adaptability of the proposed architecture from the obtained results, since it was possible to perform the acquisition of more than one class of signals.Cape

Algorithms and Systems for IoT and Edge Computing

The idea of distributing the signal processing along the path that starts with the acquisition and ends with the final application has given light to the Internet of Things and Edge Computing, which have demonstrated several advantages in terms of scalability, costs, and reliability. In this dissertation, we focus on designing and implementing algorithms and systems that allow performing a complex task on devices with limited resources. Firstly, we assess the trade-off between compression and anomaly detection from both a theoretical and a practical point of view. Information theory provides the rate-distortion analysis that is extended to consider how information content is processed for detection purposes. Considering an actual Structural Health Monitoring application, two corner cases are analysed: detection in high distortion based on a feature extraction method and detection with low distortion based on Principal Component Analysis. Secondly, we focus on streaming methods for Subspace Analysis. In this context, we revise and study state-of-the-art methods to target devices with limited computational resources. We also consider a real case of deployment of an algorithm for streaming Principal Component Analysis for signal compression in a Structural Health Monitoring application, discussing the trade-off between the possible implementation strategies. Finally, we focus on an alternative compression framework suited for low-end devices that is Compressed Sensing. We propose a different decoding approach that splits the recovery problem into two stages and effectively adopts a deep neural network and basic linear algebra to reconstruct biomedical signals. This novel approach outperforms the state-of-the-art in terms of quality of reconstruction and requires lower computational resources

Coping with saturating projection stages in RMPI-based Compressive Sensing

Though compressive sensing hinges on extracting linear measurements from the signals to acquire, actual implementations introduce nonlinearities whose effect can be far from negligible. We here address the problem of saturation in the circuit blocks needed by a Random Modulation Pre-Integration architecture. To allow a fair a comparison with previous analysis, we rely on a model capturing the essentials of saturations in actual implementations while being able to reproduce more abstract settings considered in the literature. Based on this, we analyze some methods already proposed to cope with simplified saturation mechanisms, briefly discussing their underlying principles. Finally, we introduce a novel approach that takes into account the more realistic model and, at the cost of an almost negligible hardware overhead, is extremely effective in countering saturation effects

Coping with saturating projection stages in RMPI-based Compressive Sensing

Though compressive sensing hinges on extracting linear measurements from the signals to acquire, actual implementations introduce nonlinearities whose effect can be far from negligible. We here address the problem of saturation in the circuit blocks needed by a Random Modulation Pre-Integration architecture. To allow a fair a comparison with previous analysis, we rely on a model capturing the essentials of saturations in actual implementations while being able to reproduce more abstract settings considered in the literature. Based on this, we analyze some methods already proposed to cope with simplified saturation mechanisms, briefly discussing their underlying principles. Finally, we introduce a novel approach that takes into account the more realistic model and, at the cost of an almost negligible hardware overhead, is extremely effective in countering saturation effects