Real-Time Digital Modeling of Analog Circuitry for Audio Applications

The goal of this project was to develop a scalable digital signal processing platform capable of modeling analog audio circuits using state-space modeling techniques. Using circuit theory as a foundation, the analog models were built around time-domain solution of circuit analysis. The resultant platform was capable of indistinguishably modeling variable analog filter circuits, with order being only restricted by hardware capabilities. Various continuous-to-discrete time conversion methods were investigated to determine the optimal sounding and performing algorithm

Real-Time Electromagnetic Signal Processing: Principles and Illustrations

Real-time electromagnetic signal processing has recently appeared as a novel signal-processing paradigm to manipulate and control electromagnetic signals in real time directly in the analog domain. This has led to attractive alternatives to conventional digital techniques, which typically suffer from poor performances and high cost at microwave and millimeter wave frequencies. This novel paradigm is based on dispersion-engineered electromagnetic structures, and in this review chapter, two types of structures are presented and discussed in details: phasers and metasurfaces. While phasers are typically group delay engineered to manipulate and rearrange spectral components in the time domain, metasurfaces enhances these functionalities by providing spatial processing in addition to temporal processing. Two kinds of phasers are presented here: static and dynamic all-pass C-section phasers, and all-dielectric metasurface-based spatial phasers. Finally, two applications illustrating real-time signal processing are discussed: 2D beam scanning leaky-wave antenna for high-resolution spectrum analysis and a dispersion code multiple access (DCMA) system

WDM orthogonal subcarrier multiplexing

Electro-optical transceivers can be implemented employing all-analog signal processing in order to achieve low values of power consumption and latency. This paper shows that the spectral efficiency of such solutions can be increased by combining orthogonal multicarrier techniques and off-the-shelf microwave components. A real-time 108-Gbit/s experiment was performed emulating a wavelength division multiplexing (WDM) system composed of five optical channels. The optical carriers were provided by an externally injected gain switched optical frequency comb. Each optical channel transmitted a 21.6-Gbit/s orthogonal subcarrier multiplexing (SCM) signal that was modulated and demodulated in the electrical domain without the requirement for digital signal processing. The net data rate remained higher than 100 Gbit/s after taking into account forward error correction overheads. The use of orthogonally overlapping subchannels achieves an unprecedented spectral efficiency in all-analog real-time broadband WDM/SCM links

Analog Signal Processing Elements for Energy-Constrained Platforms

Energy constrained processing poses a number of challenges that have resulted in tremendous innovations over the past decade. Shrinking supply voltages and limited clock speeds have placed an emphasis on processing efficiency over the raw throughput of a processor. One of the approaches to increase processing efficiency is to use parallel processing with slower, lower resolution processing elements. By utilizing this parallel approach, power consumption can be decreased while maintaining data throughput relative to other more power-hungry architectures.;This low resolution / parallel architecture has direct application in the analog as well as the digital domain. Indeed, research shows that as the resolution of a signal processor falls below a system-dependent threshold, it is almost always more efficient to preform the processing in the analog domain. These continuous-time circuits have long been used in the most energy-constrained applications, ranging from pacemakers and cochlear implants to wireless sensor motes designed to run autonomously for months in the field.;Most audio processing techniques utilize spectral decomposition as the first step of their algorithms, whether by a FFT/DFT in the digital domain or a bank of bandpass filters in the analog domain. The work presented here is designed to function within the parallel, array-based environment of a bank of bandpass filters. Work to improve the simulation of programmable analog storage elements (Floating-Gate transistors) in typical SPICE-based simulators is presented, along with a novel method of harnessing the unique properties of these Floating-Gate (FG) transistors to extend the linear range of a differential pair. These improvements in simulation and linearity are demonstrated in a Variable-Gain Amplfier (VGA) to compress large differential inputs into small single-ended outputs suitable for processing by other analog elements. Finally, a novel circuit composed of only six transistors is proposed to compute the continuous-time derivative of a signal within the sub-banded architecture of the bandpass filter bank

Comparison of Digital Signal Processing Approaches for Subcarrier Multiplexed 5G and Beyond Analog Fronthaul

Analog fronthaul transport architectures with digital signal processing at the end stations are promising as they have the potential to achieve high spectral efficiencies, increased flexibility and reduced latency. In this paper, two digital techniques for frequency domain multiplexing/de-multiplexing large numbers of channels are contrasted: one operates on the pre-Inverse Fast Fourier Transform (IFFT) “frequency-domain” samples while the other does so on the post-IFFT “time-domain” samples. Performance criteria including computational complexity and sampling rate requirements are used in the comparison. Following modeling and simulation of the techniques, implemented within a radio-over-fiber transport architecture, error vector magnitude performance estimates are obtained. These results show that each technique has performance advantages under specific channel transport scenarios

Digitally Interfaced Analog Correlation Filter System for Object Tracking Applications

Advanced correlation filters have been employed in a wide variety of image processing and pattern recognition applications such as automatic target recognition and biometric recognition. Among those, object recognition and tracking have received more attention recently due to their wide range of applications such as autonomous cars, automated surveillance, human-computer interaction, and vehicle navigation.Although digital signal processing has long been used to realize such computational systems, they consume extensive silicon area and power. In fact, computational tasks that require low to moderate signal-to-noise ratios are more efficiently realized in analog than digital. However, analog signal processing has its own caveats. Mainly, noise and offset accumulation which degrades the accuracy, and lack of a scalable and standard input/output interface capable of managing a large number of analog data.Two digitally-interfaced analog correlation filter systems are proposed. While digital interfacing provided a standard and scalable way of communication with pre- and post-processing blocks without undermining the energy efficiency of the system, the multiply-accumulate operations were performed in analog. Moreover, non-volatile floating-gate memories are utilized as storage for coefficients. The proposed systems incorporate techniques to reduce the effects of analog circuit imperfections.The first system implements a 24x57 Gilbert-multiplier-based correlation filter. The I/O interface is implemented with low-power D/A and A/D converters and a correlated double sampling technique is implemented to reduce offset and lowfrequency noise at the output of analog array. The prototype chip occupies an area of 3.23mm2 and demonstrates a 25.2pJ/MAC energy-efficiency at 11.3 kVec/s and 3.2% RMSE.The second system realizes a 24x41 PWM-based correlation filter. Benefiting from a time-domain approach to multiplication, this system eliminates the need for explicit D/A and A/D converters. Careful utilization of clock and available hardware resources in the digital I/O interface, along with application of power management techniques has significantly reduced the circuit complexity and energy consumption of the system. Additionally, programmable transconductance amplifiers are incorporated at the output of the analog array for offset and gain error calibration. The prototype system occupies an area of 0.98mm2 and is expected to achieve an outstanding energy-efficiency of 3.6pJ/MAC at 319kVec/s with 0.28% RMSE

Time-based noise-shaping techniques for time-to-digital and analog-to-digital converters

In this dissertation, time-based signal processing techniques and their applications in oversampling and noise-shaping data converters are examined. These techniques demonstrate the ability to shift the burden of high performance analog circuits from the compressed voltage-domain to the augmented time-domain. First, the potential of high order noise-shaping and phase-domain feedback in time-to-digital converters (TDCs) is explored. A prototype phase reference, second-order continuous-time delta-sigma TDC for sensor applications was fabricated in 90nm CMOS and achieves 64 dB dynamic range in 1MHz signal bandwidth. Second, an ultra-high performance oscillator-based delta-sigma modulator architecture is investigated. The proposed circuit is a third-order continuous-time PLL-Based Delta-Sigma Modulator with simulated 77 dB SNDR in 40MHz signal bandwidth with OSR of 16, and is fabricated in 65nm CMOS

Xampling: Signal Acquisition and Processing in Union of Subspaces

We introduce Xampling, a unified framework for signal acquisition and processing of signals in a union of subspaces. The main functions of this framework are two. Analog compression that narrows down the input bandwidth prior to sampling with commercial devices. A nonlinear algorithm then detects the input subspace prior to conventional signal processing. A representative union model of spectrally-sparse signals serves as a test-case to study these Xampling functions. We adopt three metrics for the choice of analog compression: robustness to model mismatch, required hardware accuracy and software complexities. We conduct a comprehensive comparison between two sub-Nyquist acquisition strategies for spectrally-sparse signals, the random demodulator and the modulated wideband converter (MWC), in terms of these metrics and draw operative conclusions regarding the choice of analog compression. We then address lowrate signal processing and develop an algorithm for that purpose that enables convenient signal processing at sub-Nyquist rates from samples obtained by the MWC. We conclude by showing that a variety of other sampling approaches for different union classes fit nicely into our framework.Comment: 16 pages, 9 figures, submitted to IEEE for possible publicatio

Cardinal Exponential Splines: Part II—Think Analog, Act Digital

By interpreting the Green-function reproduction property of exponential splines in signal processing terms, we uncover a fundamental relation that connects the impulse responses of allpole analog filters to their discrete counterparts. The link is that the latter are the B-spline coefficients of the former (which happen to be exponential splines). Motivated by this observation, we introduce an extended family of cardinal splines—the generalized E-splines—to generalize the concept for all convolution operators with rational transfer functions. We construct the corresponding compactly-supported B-spline basis functions, which are characterized by their poles and zeros, thereby establishing an interesting connection with analog filter design techniques. We investigate the properties of these new B-splines and present the corresponding signal processing calculus, which allows us to perform continuous-time operations, such as convolution, differential operators, and modulation, by simple application of the discrete version of these operators in the B-spline domain. In particular, we show how the formalism can be used to obtain exact, discrete implementations of analog filters. Finally, we apply our results to the design of hybrid signal processing systems that rely on digital filtering to compensate for the nonideal characteristics of real-world analog-to-digital (A-to-D) and D-to-A conversion systems