Analog-to-information Converter Research Articles

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

Because sensing nodes typically have limited power resources, it is extremely important for signals to be acquired with high efficiency and low power consumption, especially in large-scale wireless sensor networks (WSNs) applications. An emerging signal acquisition and compression method called compressed sensing (CS) is a notable alternative to traditional signal processing methods and is a feasible solution for WSNs. In our previous work, we studied several data recovery algorithms and network models that use CS for compressive sampling and signal recovery. The results were validated on large data sets from actual environmental monitoring WSNs. In this paper, we focus on the hardware solution for signal acquisition and processing on separate end nodes. We propose the paradigm of an analog-to-information converter (AIC) based on CS theory. The system model consists of a modulation module, filtering module, and sampling module, and was simulated and analyzed in a MATLAB/Simulink 7.0 environment. Further, the hardware design and implementation of an improved digital AIC system is presented. We also study the performances of three different greedy data recovery algorithms and analyze the system power consumption. The experimental results show that, for normal environmental signals, the new system overcomes the Nyquist limit and exhibits good recovery performance with a low sampling frequency, which is suitable for environmental monitoring based on WSNs.

Analog-to-information converters in the wideband rf measurement for aerospace applications: current situation and perspectives

Currently, monitoring radio frequency (RF) emissions in the aerospace field is done by means of different techniques and methods. The RF measurements are carried out on ground stations and space stations (e.g., satellites). To allow a fast and efficient characterization of aerospace RF emissions, a significant interest of researchers has been on reducing the time needed for the Wideband Spectrum Sensing (WSS) [1]. The current state-of-the-art of A/D conversion of wideband signals coming from antenna systems is a wellknown boundary to that aim. Recently, compressed sampling (CS) and analog-to-information converters (AICs) have been proposed as a means to allow an efficient WSS [2]-[4]. A first survey about the challenges of WSS in the existing aerospace RF environment and a short introduction on signal acquisition for measurement purposes using AICs for CS was presented in [5].

A general sequential delay-Doppler estimation scheme for sub-Nyquist pulse-Doppler radar

Sequential estimation of the delay and Doppler parameters for sub-Nyquist radars by analog-to-information conversion (AIC) systems has received wide attention recently. However, the estimation methods reported are AIC-dependent and have poor performance for off-grid targets. This paper develops a general estimation scheme in the sense that it is applicable to all AICs regardless whether the targets are on or off the grids. The proposed scheme estimates the delay and Doppler parameters sequentially, in which the delay estimation is formulated into a beamspace direction-of- arrival problem and the Doppler estimation is translated into a line spectrum estimation problem. Then the well-known spatial and temporal spectrum estimation techniques are used to provide efficient and high-resolution estimates of the delay and Doppler parameters. In addition, sufficient conditions on the AIC to guarantee the successful estimation of off-grid targets are provided, while the existing conditions are mostly related to the on-grid targets. Theoretical analyses and numerical experiments show the effectiveness and the correctness of the proposed scheme.

Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.

Energy efficient EEG acquisition and reconstruction for a Wireless Body Area Network

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Previous Compressed Sensing (CS) based solutions to EEG telemonitoring over WBAN's could only reduce the communication cost. In this work, we propose a matrix completion based formulation that can also reduce the energy consumption for sensing. At the heart of the system is an Analog to Information Converter (AIC) implemented in 65nm CMOS technology. The pseudorandom clock generator enables random under-sampling and subsequent conversion by the 12-bit Successive Approximation Register Analog to Digital Converter (SAR ADC). AIC achieves a sampling rate of 0.5KS/s, an ENOB 9.54bits, FOM 187 fj/conv-step and consumes 69.66nW from 1V power supply. We test our method with state-of-the-art CS based techniques and find that the reconstruction accuracy of our method is significantly better and that too at considerably less energy consumption. Our method is also tested for post-reconstruction signal classification where it outperforms previous CS based techniques.

Segment-sliding reconstruction of pulsed radar echoes with sub-Nyquist sampling

It has been shown that analog-to-information conversion (AIC) is an efficient scheme to perform sub-Nyquist sampling of pulsed radar echoes. However, it is often impractical, if not infeasible, to reconstruct full-range Nyquist samples because of huge storage and computational load requirements. Based on the analyses of AIC measurement system, this paper develops a novel segment-sliding reconstruction (SegSR) scheme to effectively reconstruct the Nyquist samples. The SegSR performs segment-by-segment reconstruction in a sliding mode and can be implemented in real time. An important characteristic that distinguishes the proposed SegSR from existing methods is that the measurement matrix in each segment satisfies the restricted isometry property (RIP) condition. Partial support in the previous segment can be incorporated into the estimation of the Nyquist samples in the current segment. The effect of interference introduced from adjacent segments is theoretically analyzed, and it is revealed that the interference consists of two interference levels with different impacts to the signal reconstruction performance. With these observations, a two-step orthogonal matching pursuit (OMP) procedure is proposed for segment reconstruction, which takes into account different interference levels and partially known support of the previous segment. The proposed SegSR scheme achieves near-optimal reconstruction performance with a significant reduction of computational loads and storage requirements. Theoretical analyses and simulations verify its effectiveness.

Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing.

We report the design and implementation of an Analog-to-Information Converter (AIC) based on Compressed Sensing (CS). The system is realized in a CMOS 180 nm technology and targets the acquisition of bio-signals with Nyquist frequency up to 100 kHz. To maximize performance and reduce hardware complexity, we co-design hardware together with acquisition and reconstruction algorithms. The resulting AIC outperforms previously proposed solutions mainly thanks to two key features. First, we adopt a novel method to deal with saturations in the computation of CS measurements. This allows no loss in performance even when 60% of measurements saturate. Second, the system is able to adapt itself to the energy distribution of the input by exploiting the so-called rakeness to maximize the amount of information contained in the measurements. With this approach, the 16 measurement channels integrated into a single device are expected to allow the acquisition and the correct reconstruction of most biomedical signals. As a case study, measurements on real electrocardiograms (ECGs) and electromyograms (EMGs) show signals that these can be reconstructed without any noticeable degradation with a compression rate, respectively, of 8 and 10.

Compressed sensing based multi-rate sub-Nyquist sampling system

Signal sampling is a vital component in modern information technology. As the signal bandwidth becomes wider, the sampling rate of analog-to-digital conversion (ADC) based on Shannon-Nyquist theorem is more and more high and may be beyond its capacity. However the analog to information converter (AIC) based on compressed sensing (CS) is designed to sample the analog signals at a sub-Nyquist sampling rate. A new multi-rate sub-Nyquist sampling (MSS) system was proposed in this article, it has one mixer, one integrator and several parallel ADCs with different sampling rates. Simulation shows the signals can be reconstructed in high probability even though the sampling rate is much lower than the Nyquist sampling rate.

Under-Sampling of PPM-UWB Communication Signals Based on CS and AIC

Pulse position modulation ultra-wideband (PPM-UWB) is a new technology developed for high-speed wireless communication. Nonetheless, the sampling of PPM-UWB communication signal is limited by its ultra-wide bandwidth. According to the compressed sensing (CS) theory, the original signal can theoretically be under-sampled by a projection matrix with fewer rows than columns. However, the multiplication of matrix and signal demands that the received signal should already be sampled completely. The random matrix cannot be realized using hardware, and the process of random under-sampling is also uncontrollable. To address these problems, we propose a practical under-sampling method for the PPM-UWB communication signal based on CS theory and analog-to-information conversion (AIC) technology. Random matrix is replaced with AIC in the CS measuring projection stage, and the entire structure of AIC can be constructed using hardware. Whether or not the system matrix of AIC can satisfy the restricted isometry property is verified with the Johnson---Lindenstrauss lemma. Finally, a detection platform is constructed for the PPM-UWB communication signal based on CS and AIC, and it is considered for implementation. Although the proposed method cannot guarantee the precise reconstruction of a target vector, it can still be applied to under-sample the PPM-UWB communication signal practically. An analysis of performance results demonstrates the validity and applicability of the proposed method.

Frequency-domain characterization of random demodulation Analog-to-Information Converters

<p>The paper aims at proposing test methods for Analog-to-Information Converters (AICs).</p><p>In particular, the objective of this work is to verify if figures of merit and test methods, currently defined in standards for traditional Analog-to-Digital Converters, can be applied to AICs based on the random demodulation architecture.</p><p>For this purpose, an AIC prototype has been designed, starting from commercially available integrated circuits. A simulation analysis and an experimental investigation have been carried out to study the additional influencing factors such as the parameters of the reconstruction algorithm. Results show that standard figures of merit are in general capable of describing the performance of AICs, provided that they are slightly modified according to the proposals reported in the paper. In addition, test methods have to be modified in order to take into account the statistical behavior of AICs.</p>

Energy efficient acquisition and reconstruction of EEG signals.

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Previous Compressed Sensing (CS) based solutions to EEG tele-monitoring over WBAN's could only reduce the communication cost. In this work, we propose a matrix completion based formulation that can also reduce the energy consumption for sensing. We test our method with state-of-the-art CS based techniques and find that the reconstruction accuracy from our method is significantly better and that too at considerably less energy consumption. Our method is also tested for post-reconstruction signal classification where it outperforms previous CS based techniques. At the heart of the system is an Analog to Information Converter (AIC) implemented in 65nm CMOS technology. The pseudorandom clock generator enables random under-sampling and subsequent conversion by the 12-bit Successive Approximation Register Analog to Digital Converter (SAR ADC). AIC achieves a sample rate of 0.5 KS/s, an ENOB 9.54 bits, and consumes 108 nW from 1 V power supply.

A 12 pJ/Pixel Analog-to-Information Converter Based 816 × 640 Pixel CMOS Image Sensor

Analog-to-information converters (AICs) take advantage of the limited information bandwidth in high-frequency signals to improve the energy efficiency of front-end data converters. High-resolution image sensors often convey limited information due to the spatial redundancy between neighboring pixels. This paper proposes a mixed-signal AIC which compresses each nonoverlapping 4 × 4 pixel block in a 816 × 640 pixel prototype active-pixel sensor (APS) imager. It combines an energy-efficient charge-pump bit-image processor (BIP) with an area-efficient successive-approximation-register-single-slope (SAR-SS) hybrid analog-to-digital converter (ADC) via a charge-transfer-amplifier (CTA). The AIC is fully dynamic and consumes no static power. The ADC's capacitor array doubles as a computational device for parts of the compression algorithm which reduces its sampling rate by a factor of four. The compressed data contains direct edge information and can be decoded by a very simple receiver. The fabricated prototype consumes 12 pJ per pixel at 111 fps in the image compression mode and 48 pJ per pixel at 28.7 fps in raw data mode (9 b per pixel) under the same clock rate. To the best of our knowledge, this is the most energy-efficient compressive CMOS image sensor ever reported in the literature, thanks to the proposed AIC.

Compressive Circulant Matrix Based Analog to Information Conversion

Compressive Sampling is an attractive way implementing analog to information conversion (AIC), of which the most successful hardware architecture is modulated wideband converter (MWC). Unfortunately, the MWC has high hardware complexity owing to high degree of freedom of the random waveforms constructing the measurement matrix. To reduce the complexity, in this letter, we present a novel Compressive Circulant Matrix based AIC (CCM-AIC) generating random waveforms by cyclic shift of a special sequence with unit amplitude and random phase in frequency domain. Theoretical analysis shows this scheme is optimal for signals sparse in frequency. CCM-AIC outperforms MWC and is more robust. Simulations classify the above analysis.

Effects of ADC nonlinearity on the spurious dynamic range performance of compressed sensing.

Analog-to-information converter (AIC) plays an important role in the compressed sensing system; it has the potential to significantly extend the capabilities of conventional analog-to-digital converter. This paper evaluates the impact of AIC nonlinearity on the dynamic performance in practical compressed sensing system, which included the nonlinearity introduced by quantization as well as the circuit non-ideality. It presents intuitive yet quantitative insights into the harmonics of quantization output of AIC, and the effect of other AIC nonlinearity on the spurious dynamic range (SFDR) performance is also analyzed. The analysis and simulation results demonstrated that, compared with conventional ADC-based system, the measurement process decorrelates the input signal and the quantization error and alleviate the effect of other decorrelates of AIC, which results in a dramatic increase in spurious free dynamic range (SFDR).

Why Analog-to-Information Converters Suffer in High-Bandwidth Sparse Signal Applications

In applications where signal frequencies are high, but information bandwidths are low, analog-to-information converters (AICs) have been proposed as a potential solution to overcome the resolution and performance limitations of high-speed analog-to-digital converters (ADCs). However, the hardware implementation of such systems has yet to be evaluated. This paper aims to fill this gap, by evaluating the impact of circuit impairments on performance limitations and energy cost of AICs. We point out that although the AIC architecture facilitates slower ADCs, the signal encoding, typically realized with a mixer-like circuit, still occurs at the Nyquist frequency of the input to avoid aliasing. We illustrate that the jitter and aperture of this mixing stage limit the achievable AIC resolution. In order to do so, we designed an end-to-end system evaluation framework for examining these limitations, as well as the relative energy-efficiency of AICs versus high-speed ADCs across the resolution, receiver gain and signal sparsity. The evaluation shows that the currently proposed AICs have no performance benefits over high-speed ADCs. However, AICs enable 2-10X in energy savings in low to moderate resolution (ENOB), low gain applications.

Research on Performance of Analog to Information Converter

Compressive Sensing (CS) Theory enables sampling discrete signals with quite lower sampling rate compared with traditional Nyquist sampling rate and guaranteeing faithful reconstruction. Based on CS theory, Analog-to-Information Conversion (AIC) was proposed to process continuous-time signal. In this paper, the framework of Analog-to-Information Converter is composed by a pseudo-random demodulator, a low pass analog filter and a low speed sampler. And we mainly discuss the damage on the signal recovery produced by lower and higher orders of filter impulse response.

A Compressed Sensing Analog-to-Information Converter With Edge-Triggered SAR ADC Core

This paper presents the design and implementation of an analog-to-information converter (AIC) capable of Nyquist and compressed sensing modes of operation. The core of the AIC is a 10-bit edge-triggered charge-sharing SAR ADC with a figure of merit (FOM) of 55 fJ/conversion-step and Nyquist-sampling rate of 9.5 Msample/s. The integration of a pseudorandom clock generator enables compressed sensing operation via random sampling and subsequent asynchronous successive approximation conversion by the core ADC. The AIC allows complete reconstruction of a spectrum consisting of sparse single tones or sparse frequency bands using compressed sensing algorithms based on ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization as well as ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,2</sub> regularization, which exploits group sparsity. Implemented in 90 nm CMOS, the prototype SAR ADC core achieves a maximum sample rate of 9.5 MS/s, an ENOB of 9.3 bits, and consumes 550 μW from a 1.2 V supply. Measurement results of the AIC demonstrate an effective bandwidth of 25 MHz, which is 5 × greater than Nyquist-sampling rate with an improved effective FOM of 12.2 fJ/conversion-step for signals with sparse frequency support.

An adaptive measurement scheme based on compressed sensing for wideband spectrum detection in cognitive WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors

This work introduces the use of compressed sensing (CS) algorithms for data compression in wireless sensors to address the energy and telemetry bandwidth constraints common to wireless sensor nodes. Circuit models of both analog and digital implementations of the CS system are presented that enable analysis of the power/performance costs associated with the design space for any potential CS application, including analog-to-information converters (AIC). Results of the analysis show that a digital implementation is significantly more energy-efficient for the wireless sensor space where signals require high gain and medium to high resolutions. The resulting circuit architecture is implemented in a 90 nm CMOS process. Measured power results correlate well with the circuit models, and the test system demonstrates continuous, on-the-fly data processing, resulting in more than an order of magnitude compression for electroencephalography (EEG) signals while consuming only 1.9 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu$</tex></formula> W at 0.6 V for sub-20 kS/s sampling rates. The design and measurement of the proposed architecture is presented in the context of medical sensors, however the tools and insights are generally applicable to any sparse data acquisition.

Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Compressive Wide-Band Spectrum Sensing

This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology. At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance. In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.