Sensors and analog-to-information converters

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 93-96).Compressed sensing (CS) is a promising method for recovering sparse signals from fewer measurements than ordinarily used in the Shannon's sampling theorem [14]. Introducing the CS theory has sparked interest in designing new hardware architectures which can be potential substitutions for traditional architectures in communication systems. CS-based wireless sensors and analog-to-information converters (AIC) are two examples of CS-based systems. It has been claimed that such systems can potentially provide higher performance and lower power consumption compared to traditional systems. However, since there is no end-to-end hardware implementation of these systems, it is difficult to make a fair hardware-to-hardware comparison with other implemented systems. This project aims to fill this gap by examining the energy-performance design space for CS in the context of both practical wireless sensors and AICs. One of the limitations of CS-based systems is that they employ iterative algorithms to recover the signal. Since these algorithms are slow, the hardware solution has become crucial for higher performance and speed. In this work, we also implement a suitable CS reconstruction algorithm in hardware.by Omid Salehi-Abari.S.M

Degraded Visual Environment Tracker

Compressive Sensing (CS) has proven its ability to reduce the number of measurements required to reproduce images with similar quality to those reconstructed by observing the Shannon-Nyquest sampling criteria. By exploiting spatial redundancies, it was shown that CS can be used to denoise and enhance image quality. In this thesis we propose a method that incorporates an efficient use of CS to locate a specific object in zero-visibility environments. This method was developed after multiple implementations of dictionary learning, reconstruction, detection, and tracking algorithms in order to identify the shortcomings of existing techniques and enhance our results. We show that with the use of an over-complete dictionary of the target our technique can perceive the location of the target from hidden information in the scene. This thesis will summarize the previously implemented algorithms, detail the shortcomings evident in their outputs, explain our setups, and present quantified results to support its efficacy in the results section

Sparse machine learning methods with applications in multivariate signal processing

This thesis details theoretical and empirical work that draws from two main subject areas: Machine Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application of sparse machine learning methods to multivariate signal processing. In particular, methods that enforce sparsity will be employed for reasons of computational efficiency, regularisation, and compressibility. The methods presented can be seen as modular building blocks that can be applied to a variety of applications. Application specific prior knowledge can be used in various ways, resulting in a flexible and powerful set of tools. The motivation for the methods is to be able to learn and generalise from a set of multivariate signals. In addition to testing on benchmark datasets, a series of empirical evaluations on real world datasets were carried out. These included: the classification of musical genre from polyphonic audio files; a study of how the sampling rate in a digital radar can be reduced through the use of Compressed Sensing (CS); analysis of human perception of different modulations of musical key from Electroencephalography (EEG) recordings; classification of genre of musical pieces to which a listener is attending from Magnetoencephalography (MEG) brain recordings. These applications demonstrate the efficacy of the framework and highlight interesting directions of future research

Homotopy based algorithms for ℓ0\ell_0-regularized least-squares

Sparse signal restoration is usually formulated as the minimization of a quadratic cost function $\|y-Ax\|_2^2$, where A is a dictionary and x is an unknown sparse vector. It is well-known that imposing an $\ell_0$ constraint leads to an NP-hard minimization problem. The convex relaxation approach has received considerable attention, where the $\ell_0$-norm is replaced by the $\ell_1$-norm. Among the many efficient $\ell_1$ solvers, the homotopy algorithm minimizes $\|y-Ax\|_2^2+\lambda\|x\|_1$ with respect to x for a continuum of $\lambda$'s. It is inspired by the piecewise regularity of the $\ell_1$-regularization path, also referred to as the homotopy path. In this paper, we address the minimization problem $\|y-Ax\|_2^2+\lambda\|x\|_0$ for a continuum of $\lambda$'s and propose two heuristic search algorithms for $\ell_0$-homotopy. Continuation Single Best Replacement is a forward-backward greedy strategy extending the Single Best Replacement algorithm, previously proposed for $\ell_0$-minimization at a given $\lambda$. The adaptive search of the $\lambda$-values is inspired by $\ell_1$-homotopy. $\ell_0$ Regularization Path Descent is a more complex algorithm exploiting the structural properties of the $\ell_0$-regularization path, which is piecewise constant with respect to $\lambda$. Both algorithms are empirically evaluated for difficult inverse problems involving ill-conditioned dictionaries. Finally, we show that they can be easily coupled with usual methods of model order selection.Comment: 38 page

Fully-passive switched-capacitor techniques for high performance SAR ADC design

In recent years, SAR ADC becomes more and more popular in various low-power applications such as wireless sensors and low energy radios due to its circuit simplicity, high power efficiency, and scaling compatibility. However, its speed is limited by its successive approximation procedures and its power efficiency greatly reduces with the ADC resolution going beyond 10 bit. To address these issues, this thesis proposes to embed two techniques: 1) compressive sensing (CS) and 2) noise shaping (NS) to a conventional SAR ADC. The realization of both techniques are based on fully-passive switched-capacitor techniques. CS is a recently emerging sampling paradigm, stating that the sparsity of a signal can be exploited to reduce the ADC sampling rate below the Nyquist rate. Different from conventional CS frameworks which require dedicated analog CS encoders, this thesis proposes a fully-passive CS-SAR ADC architecture which only requires minor modification to a conventional SAR ADC. Two chips are fabricated in a 0.13 µm process to prove the concept. One chip is a single-channel CS-SAR ADC which can reduce the ADC conversion rate by 4 times, thus reducing the ADC power by 4 times. In many wireless sensing applications, multiple ADCs are commonly required to sense multi-channel signals such as multi-lead ECG sensing and parallel neural recording. Therefore, the other chip is a multi-channel CS-SAR ADC which can simultaneously convert 4-channel signals with a sampling rate of one channel’s Nyquist rate. At 0.8 V and 1 MS/s, both chips achieve an effective Walden FoM of around 5 fJ/conversion-step. This thesis also proposes a novel NS SAR ADC architecture that is simple, robust and low power for high-resolution applications. Compared to conventional ∆Σ ADCs, it replaces the power-hungry active integrator with a passive integrator which only requires one switch and two capacitors. Compared to previous 1st-order NS SAR ADC works, it achieves the best NS performance and can be easily extended to 2nd-order. A 1st-order 10-bit NS SAR ADC is fabricated in a 0.13 µm process. Through NS, SNDR increases by 6 dB with OSR doubled, achieving a 12- bit ENOB at OSR = 8. An improved version of a 2nd-order 9-bit NS SAR ADC is designed and simulated in a 40 nm process. The SNDR increases by 10 dB with OSR doubled, achieving a 14-bit ENOB at OSR = 16. At a bandwidth of 312.5 kHz, the Schreier FoM is 181 dB and the Walden FoM is 12.5 fJ/conversion-step, proving that the proposed NS SAR ADC architecture can achieve high resolution and high power efficiency simultaneously.Electrical and Computer Engineerin

Sparse channel estimation based on compressed sensing theory for UWB systems

Català: L'estimació de canal en receptors wireless esdevé un factor determinant a l'hora de incrementar les prestacions dels sistemes sense fils per tal de satisfer les exigències cada vegades més elevades dels consumidors en quant a velocitats de transmissió i qualitat. En aquesta tesi es proposa explotar la "sparsity" que mostren els canals wireless per tal de millorar els clàssics sistemes d'estimació de canal mitjançant les noves teòries de Compressed Sensing. Així doncs, es proposa un nou model freqüencial de senyal on el canal i un nou algoritme de reconstrucció de senyals sparse que redueix la probabilitat de detecció de falsos camins de propagació millorant d'aquesta manera l'estimació de temps d'arribada.Castellano: En los últimos años, la revolución inalámbrica se ha convertido en una realidad. Wi-fi está en todas partes, impactando significativamente en nuestro estilo de vida. Sin embargo, las comunicaciones inalámbricas nunca tendrán las condiciones de propagación igual que los cables debido a las duras condiciones de la propagación inalámbricas. El canal de radio móvil se caracteriza por la recepción múltiple, eso es que la señal recibida no sólo contiene una camino de propagación, sino también un gran número de ondas reflejadas. Estas ondas reflejadas interfieren con la onda directa, lo que provoca una degradación significativa del rendimiento del enlace. Un sistema inalámbrico debe estar diseñado de tal manera que el efecto adverso del desvanecimiento multicamino sea reducido al mínimo. Afortunadamente, el multipath puede ser visto como diversidad de información dependiendo de la cantidad de Channel State Information (CSI) disponible para el sistema. Sin embargo, en la práctica CSI rara vez se dispone a priori y debe ser estimado. Por otro lado, un canal inalámbrico a menudo puede ser modelado como un canal sparse, en la que el retraso de propagación puede ser muy grande, pero el número de caminos de propagación es normalmente muy pequeño. El conocimiento previo de la sparsity del canal se puede utilizar eficazmente para mejorar la estimación de canal utilizando la nueva teoría de Compressed Sensing (CS). CS se origina en la idea de que no es necesario invertir una gran cantidad de energía en la observación de las entradas de una señal sparse porque la mayoría de ellas será cero. Por lo tanto, CS proporciona un marco sólido para la reducción del número de medidas necesarias para resumir señales sparse. La estimación de canal sparse se centra en este trabajo en Ultra-Wideband (UWB) porque la gran resolución temporal que proporcionan las señales UWB se traduce en un número muy grande de componentes multipath que se pueden resolver. Por lo tanto, UWB mitiga significativamente la distorsión de trayectoria múltiple y proporciona la diversidad multicamino. Esta diversidad junto con la resolución temporal de las señales UWB crear un problema de estimación de canal muy interesante. En esta tesis se estudia el uso de CS en la estimación de canal altamente sparse por medio de un nuevo enfoque de estimación basado en el modelo de frecuencial de la señal UWB. También se propone un nuevo algoritmo llamado extended Orthogonal Matching Pursuit (eOMP) basado en los mismos principios que el clásico OMP, con el fin de mejorar algunas de sus característica.English: In recent years, the wireless revolution has become a reality. Wireless is everywhere having significant impact on our lifestyle. However, wireless will never have the same propagation conditions as wires due to the harsh conditions of the wireless propagation. The mobile radio channel is characterized by multipath reception, that is the signal offered to the receiver contains not only a direct line-of-sight radio wave, but also a large number of reflected radio waves. These reflected waves interfere with the direct wave, which causes significant degradation of the performance of the link. A wireless system has to be designed in such way that the adverse effect of multipath fading is minimized. Fortunately, multipath can be seen as a blessing depending on the amount of Channel State Information (CSI) available to the system. However, in practise CSI is seldom available a priori and needs to be estimated. On the other hand, a wireless channel can often be modeled as a sparse channel in which the delay spread could be very large, but the number of significant paths is normally very small. The prior knowledge of the channel sparseness can be effectively use to improve the channel estimation using the novel Compressed Sensing (CS) theory. CS originates from the idea that is not necessary to invest a lot of power into observing the entries of a sparse signal because most of them will be zero. Therefore, CS provides a robust framework for reducing the number of measurement required to summarize sparse signals. The sparse channel estimation here is focused on Ultra-WideBand (UWB) systems because the very fine time resolution of the UWB signal results in a very large number of resolvable multipath components. Consequently, UWB significantly mitigates multipath distortion and provides path diversity. The rich multipath coupled with the fine time resolution of the UWB signals create a challenging sparse channel estimation problem. This Master Thesis examines the use of CS in the estimation of highly sparse channel by means of a new sparse channel estimation approach based on the frequency domain model of the UWB signal. It is also proposed a new greedy algorithm named extended Orthogonal Matching Pursuit (eOMP) based on the same principles than classical Orthogonal Matching Pursuit (OMP) in order to improve some OMP characteristics. Simulation results show that the new eOMP provides lower false path detection probability compared with classical OMP, which also leads to a better TOA estimation without significant degradation of the channel estimation. Simulation results will also show that the new frequency domain sparse channel model outperforms other models presented in the literature