4 citations · 7 across the 6 of their papers we have counts for
7 papers
The Continuous-Time RC-Chain ADC
Hampus Malmberg, Fredrik Feyling
An amplifier-less continuous-time analog-to-digital converter consisting of only passives, comparators, and inverters is presented. Beyond simplicity, the architecture displays sig…
A Control-Bounded Quadrature Leapfrog ADC
Hampus Malmberg, Fredrik Feyling, Jose M. de la Rosa
In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated. A band-pass analog-to-digital converter is considered as an appl…
Quadrature Control-Bounded ADCs
Hampus Malmberg, Fredrik Feyling, Jose M de la Rosa
In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated by considering band-pass analog-to-digital conversion. We show ho…
Calibrating Control-Bounded ADCs
Hampus Malmberg, Till Mettler, Thomas Burger +2
The paper considers the calibration of control-bounded analog-to-digital converters. It is demonstrated that variations of the analog frontend can be addressed by calibrating the d…
Binary Control and Digital-to-Analog Conversion Using Composite NUV Priors and Iterative Gaussian Message Passing
Raphael Keusch, Hampus Malmberg, Hans-Andrea Loeliger
The paper proposes a new method to determine a binary control signal for an analog linear system such that the state, or some output, of the system follows a given target trajector…
Control-Bounded Analog-to-Digital Conversion: Transfer Function Analysis, Proof of Concept, and Digital Filter Implementation
Hans-Andrea Loeliger, Hampus Malmberg, Georg Wilckens
Control-bounded analog-to-digital conversion has many commonalities with delta-sigma conversion, but it can profitably use more general analog filters. The paper describes the oper…
On Sparsity by NUV-EM, Gaussian Message Passing, and Kalman Smoothing
Hans-Andrea Loeliger, Lukas Bruderer, Hampus Malmberg +2
Normal priors with unknown variance (NUV) have long been known to promote sparsity and to blend well with parameter learning by expectation maximization (EM). In this paper, we adv…