Design and Exploration of Low-Power Analog to Information Conversion Based on Compressed Sensing

The long-standing analog-to-digital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compressed Sensing (CS) [1], [2], [3] is a new emerging signal acquisition/compression paradigm that offers a striking alternative to traditional signal acquisition. CS states that a signal having a sparse representation in some dictionary of waveforms can be recovered from a small number of linear projections of that signal, thus enabling efficient sensing, sampling and compression. Interestingly, by merging the sampling and compression steps, CS also removes a large part of the digital architecture and might thus considerably simplify analog-to-information (A2I) conversion devices. This so-called ”analog CS”, where compression occurs directly in the analog sensor readout electronics prior to analog-to-digial conversion, could thus be of great importance for applications where bandwidth is moderate, but computationally complex, and power resources are severely constrained. A promising example is embedded e-health monitoring devices, which must precisely operate in these conditions. Unfortunately, there are very few system wide implementations of CS including an analog front-end that could serve as reference design for these applications. In our previous work [4], we quantified and validated the potential of digital CS systems for real-time and energy-efficient electrocardiogram (ECG) compression on resource-constrained sensing platforms. In this paper, we review the state-of-the-art implementations of CS-based signal acquisition systems and perform a complete system level analysis for each implementation to highlight their strengths and weaknesses regarding implementation complexity, performance and power consumption. Then, we introduce the Spread Spectrum Random Modulator Pre-Integrator (SRMPI), which is a new design and implementation of a CS based A2I read-out system that uses spread spectrum techniques prior to random modulation in order to produce the low rate set of digital samples. Finally, we experimentally built an SRMPI prototype to compare it with state-of-the-art CS-based signal acquisition systems, focusing on critical system design parameters and constraints, and show that this new proposed architecture offers a compelling alternative, in particular for low power and computationally-constrained embedded systems.

Compressive Sampling Strategies for Multichannel Signals

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerous areas of signal processing are: i) due to their structural properties, natural signals typically have sparse representations in properly chosen orthogonal bases, ii) the number of linear non-adaptive measurements required to acquire high-dimensional data with CS is proportional to the signal’s sparsity level in the chosen basis. In multichannel signal applications the data of different channels is often highly correlated and therefore the unstructured sparsity hypothesis deployed by the classical CS theory results in suboptimal measurement rates. Meanwhile, the wide range of applications of multichannel signals and the extremely large and increasing flow of data in those applications motivates the development of more comprehensive models incorporating both inter and intra-channel data structures in order to achieve more efficient dimensionality reduction. The main focus of this thesis is on studying two new models for efficient multichannel signal compressed sensing. Our first approach proposes a simultaneous low-rank and joint-sparse matrix model for multichannel signals. As a result, we introduce a novel CS recovery scheme based on Nuclear-l2/l1 norm convex minimization for low-rank and joint-sparse matrix approximation. Our theoretical analysis indicates that using this approach can achieve significantly lower sampling rates for a robust multichannel data CS acquisition than state-of-the-art methods. More remarkably, our analysis confirms the near-optimality of this approach: the number of CS measurements are nearly proportional to the few degrees of freedom of such structured data. Our second approach introduces a stronger model for multichannel data synthesized by a linear mixture model. Here we assume that the mixture parameters are given as side information. As a result, multichannel data CS recovery turns into a compressive source separation problem, where we propose a novel decorrelating scheme to exploit the knowledge of mixture parameters for a robust and numerically efficient source identification. Our theoretical guarantees explain the fundamental limits of this approach in terms of the number of CS measurements, the sparsity level of sources, the sampling noise, and the conditioning of the mixture parameters. We apply these two approaches to compressive hyperspectral image recovery and source separation, and compare the efficiency of our methods to state-of-the-art approaches for several challenging real-world hyperspectral datasets. Note that applications of these methods are not limited to hyperspectral imagery and it can have a broad impact on numerous multichannel signal applications. As an example, for sensor network applications deploying compressive sampling schemes, our results indicate a tight tradeoff between the number of available sensors (channels) and the complexity/cost of each sensor. Finally, in a different multichannel signal application, we deal with a simple but very important problem in computer vision namely the detection and localization of people given their multi-view silhouettes captured by networks of cameras. The main challenge in many existing solutions is the tradeoff between robustness and numerical efficiency. We model this problem by a boolean (non-linear) inverse problem where, by penalizing the sparsity of the solution, we achieve accurate results comparable to state-of-the-art methods. More remarkably, using boolean arithmetics enables us to propose a real-time and memory efficient approximation algorithm that is mainly rooted in the classical literature of group testing and set cover.

EPFL2012

Low-Power Circuit and System Design for Epileptic Seizure Detection at High Spatiotemporal Resolution

Mahsa Shoaran

Smart and miniaturized implantable microsystems with diagnostic and therapeutic capabilities are becoming increasingly important for patients suffering from neurological disorders such as epilepsy. Recent developments in microfabrication technology have provided new insights into seizure generation at an unprecedented spatial scale. Based on these findings, designing powerful acquisition systems capable of probing the wide-range spatiotemporal activities within the brain holds a great promise to improve the quality of life of epileptic patients. It is expected that future neural recording interfaces will integrate hundreds of acquisition channels at relatively high sampling rates, making it crucial to perform sophisticated signal conditioning tasks within the sensors. A small size of the implantable system is critical to minimize potential clinical issues associated with implantation. The total power consumption should be minimized to avoid heat generation inside the brain. Substantial improvements in neural implant safety and longevity are needed to translate existing multisite neural recording systems into technology suitable for long-term use in patients. As a major technological barrier, the high overall data rates of digitized neural signals collected by dense electrode arrays can drastically increase the power consumption of the wireless transmission module. Consequently, extensive system-level design improvement is needed to meet the requirements of the implantable device, while preserving the high-resolution monitoring capability. In this context, we target the exploration and development of novel methods and tools for more accurate monitoring of the electrical activity of the epileptic brain during seizures. A precise focus localization and accurate early seizure detection are two major goals. Low-power circuit and system design techniques for data acquisition, compression and seizure detection in multichannel cortical implants are presented. In this context, the first fully-integrated circuit that addresses the multichannel compressed-domain feature extraction is proposed. Circuit challenges and techniques to optimize the neural amplifier topology at the frontend of the signal acquisition chain, in terms of power consumption, die area and noise are discussed. Then, compressive sensing is utilized as the main data reduction method in the proposed system. The existing microelectronic implementations of compressive sensing are applied in a single-channel basis. Therefore, these topologies incur a high power consumption and large silicon area. To overcome these issues, a new multichannel measurement scheme and an appropriate recovery scheme are proposed, which encode the whole array into a single compressed data stream. This technique circumvents the need to place one analog-to-digital converter inside each channel, which results in a significant area saving. Taking benefit of the area-efficient implementation of compressive sensing, the number of recording units implantable on the cortex which satisfy the energy constraints of the system is scaled up by a factor equal to the compression ratio. Finally, a new feature extraction method applied on the compressed signal of several channels is proposed. The spatial evolution of the intracranial EEG signals recorded by the adjacent electrodes and the corresponding behaviour in the compressed domain are exploited for detecting seizure onset.

EPFL2015

Compressive Multiplexing of Ultrasound Signals

Marcel Arditi, Adrien Georges Jean Besson, Dimitris Perdios, Jean-Philippe Thiran, Yves Wiaux

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been developed such as sparse arrays, mechanical scanning, multiplexing and micro-beamforming, which permit to achieve 3D imaging with existing 2D imaging systems but with a degradation in image quality. We propose a novel multiplexing method which relies on compressed-sensing (CS) principles to significantly reduce the number of coaxial cables. We exploit the compressive multiplexer (CMUX) introduced for radio-frequency signals to multiplex US signals in the probe head. The CMUX considers a set of signals as inputs, modulates them with chipping sequences and sums them to form a single output. On the reconstruction side, we propose two methods: one solving a CS-based problem exploiting sparsity of US signals in a pulse-stream model (CS-PS) and another one solving a least-squares problem in the Fourier domain based on bandlimited signal properties of US signals. We demonstrate through simulations and in vivo experiments that the proposed techniques lead to high-quality reconstruction with significantly fewer coaxial cables, up to 12x less with CS-PS.