Columbia University

John Wright

Peter R Kinget

Rabia Tugce Yazicigil

Tanbir Haque

Daniel Vincent Esposito

Glen Daniel O'Neil

Matthew Bajor

Han-wen Kuo

Anna E Dorfi

Mechanisms compressive sampling to detect direction of arrival (DoA) of a signal of interest (SoI), comprising: in each of a plurality of receiver paths, receiving the SoI and producing a received signal using an antenna; and using a modulator to: receive a modulator input signal (MIS) based on the received signal produced by the antenna in the path; modulate the MIS at multiple points in time (MPIT) based on different ones of a plurality of pseudo-random numbers; and produce a plurality of modulated output signals in response to the modulating of the MIS at the MPIT; summing across the receiver paths the one of the modulated output signals produced by each of the receiver paths for each of the MPIT, to produce a plurality of sum signals each corresponding to one of the MPIT; and performing a compressed sensing recovery algorithm to recover the DoA of the SoI.

Circuits and methods for using compressive sampling to detect direction of arrival of a signal of interest

The system is configured for performing scanning electrochemical microscopy via non-local continuous line probes. The continuous line probes include an insulating probe substrate, an insulating layer, and a conductive band electrode. The system includes a sample stage for positioning a sample substrate to be imaged so as to enable contact with the insulting probe substrate at an angle &#x398;. The continuous line probe is translated across the sample substrate and changes in the signal generated at the continuous line probe are identified to indicate the presence of features on the sample substrate. A plurality of scans are performed at different angles via rotating the sample stage or the continuous line probe, the results of which are combined and analyzed to produce an image of the sample substrate via compressed sensing reconstruction. The resulting image has comparable resolution to those produced via conventional scanning electrochemical microscopy processes, but in less scan time and with less complex scanning hardware.

Non-local scanning probes for scanning probe microscopy

Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (f) and a minimum frequency (f). To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal x(t)+jx(t). x(t) and x(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) g(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

Circuits and methods for detecting interferers

Mechanisms for interferer detection can detect interferers by detecting elevated signal amplitudes in one or more of a plurality of bins (or bands) in a frequency range between a maximum frequency (f) and a minimum frequency (f. To perform rapid interferer detection, the mechanisms downconvert an input signal x(t) with a local oscillator (LO) to a complex baseband signal xI(t)+jxQ(t). xI(t) and xQ(t) are then multiplied by m unique pseudorandom noise (PN) sequences (e.g., Gold sequences) gm(t) to produce m branch signals for I and m branch signals for Q. The branch signals are then low pass filtered, converted from analog to digital form, and pairwise combined by a pairwise complex combiner. Finally, a support recovery function is used to identify interferers.

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John Wright