Analysis and application of digital spectral warping in analog and mixed-signal testing

Spectral warping is a digital signal processing transform which shifts the frequencies contained within a signal along the frequency axis. The Fourier transform coefficients of a warped signal correspond to frequency-domain 'samples' of the original signal which are unevenly spaced along the frequency axis. This property allows the technique to be efficiently used for DSP-based analog and mixed-signal testing. The analysis and application of spectral warping for test signal generation, response analysis, filter design, frequency response evaluation, etc. are discussed in this paper along with examples of the software and hardware implementation

G0^0 Electronics and Data Acquisition (Forward-Angle Measurements)

The G$^0$ parity-violation experiment at Jefferson Lab (Newport News, VA) is designed to determine the contribution of strange/anti-strange quark pairs to the intrinsic properties of the proton. In the forward-angle part of the experiment, the asymmetry in the cross section was measured for $\vec{e}p$ elastic scattering by counting the recoil protons corresponding to the two beam-helicity states. Due to the high accuracy required on the asymmetry, the G$^0$ experiment was based on a custom experimental setup with its own associated electronics and data acquisition (DAQ) system. Highly specialized time-encoding electronics provided time-of-flight spectra for each detector for each helicity state. More conventional electronics was used for monitoring (mainly FastBus). The time-encoding electronics and the DAQ system have been designed to handle events at a mean rate of 2 MHz per detector with low deadtime and to minimize helicity-correlated systematic errors. In this paper, we outline the general architecture and the main features of the electronics and the DAQ system dedicated to G$^0$ forward-angle measurements.Comment: 35 pages. 17 figures. This article is to be submitted to NIM section A. It has been written with Latex using \documentclass{elsart}. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment In Press (2007

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Handheld-Impedance-Measurement System with seven-decade capability and potentiostatic function

This paper describes design and test of a new impedance-measurement system for nonlinear devices that exhibits a seven-decade range and works down to a frequency of 0.01 Hz. The system is specifically designed for electrochemical measurements, but the proposed architecture can be employed in many other fields where flexible signal generation and analysis are required. The system employs an unconventional signal generator based on two pulsewidth modulation (PWM) oscillators and an autocalibration system that allows uncertainties of less than 3% to be obtained over a range of 1 kΩ to 100 GΩ. A synchronous demodulation processing allows the noise superimposed to the low-amplitude input signals to be made negligibl

A Polyphase Multipath Technique for Software-Defined Radio Transmitters

Transmitter circuits using large signal swings and hard-switched mixers are power-efficient, but also produce unwanted harmonics and sidebands, which are commonly removed using dedicated filters. This paper presents a polyphase multipath technique to relax or eliminate filters by canceling a multitude of harmonics and sidebands. Using this technique, a wideband and flexible power upconverter with a clean output spectrum is realized in 0.13-mum CMOS, aiming at a software-defined radio application. Prototype chips operate from DC to 2.4 GHz with spurs smaller than -40 dBc up to the 17th harmonic (18-path mode) or 5th harmonic (6-path mode) of the transmit frequency, without tuning or calibration. The transmitter delivers 8 mW of power to a 100-Omega load (2.54 Vpp-diff voltage swing) and the complete chip consumes 228 mW from a 1.2-V supply. It uses no filters, but only digital circuits and mixer

Reliable Linear, Sesquilinear and Bijective Operations On Integer Data Streams Via Numerical Entanglement

A new technique is proposed for fault-tolerant linear, sesquilinear and bijective (LSB) operations on $M$ integer data streams ($M\geq3$), such as: scaling, additions/subtractions, inner or outer vector products, permutations and convolutions. In the proposed method, the $M$ input integer data streams are linearly superimposed to form $M$ numerically-entangled integer data streams that are stored in-place of the original inputs. A series of LSB operations can then be performed directly using these entangled data streams. The results are extracted from the $M$ entangled output streams by additions and arithmetic shifts. Any soft errors affecting any single disentangled output stream are guaranteed to be detectable via a specific post-computation reliability check. In addition, when utilizing a separate processor core for each of the $M$ streams, the proposed approach can recover all outputs after any single fail-stop failure. Importantly, unlike algorithm-based fault tolerance (ABFT) methods, the number of operations required for the entanglement, extraction and validation of the results is linearly related to the number of the inputs and does not depend on the complexity of the performed LSB operations. We have validated our proposal in an Intel processor (Haswell architecture with AVX2 support) via fast Fourier transforms, circular convolutions, and matrix multiplication operations. Our analysis and experiments reveal that the proposed approach incurs between $0.03\%$ to $7\%$ reduction in processing throughput for a wide variety of LSB operations. This overhead is 5 to 1000 times smaller than that of the equivalent ABFT method that uses a checksum stream. Thus, our proposal can be used in fault-generating processor hardware or safety-critical applications, where high reliability is required without the cost of ABFT or modular redundancy.Comment: to appear in IEEE Trans. on Signal Processing, 201