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In this paper we describe the hardware evolution of analog circuits performing signal separation tasks using JPL's Stand-Alone Board-Level Evolvable System (SABLES).
A fiber-optic displacement sensor with temporally separated signal and reference channels is proposed. The sensing technique used is based on determining the relative amplitude of the signal and reference pulses that together form a double pulse. The relative amplitude of the pulses in the double pulse is a function of the displacement. A setup to generate the double pulse with individual pulses of 5-ns duration and 10-ns delay is described. A novel signal processing technique, used to determine the relative amplitude of such pulses in the double pulse by analyzing different portions of the signal spectrum, is explained. Experimental data are also provided.
In this work, imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper.
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Replacement for dichroic plate reduces noise. Besides being easier to install, flange is less expensive to fabricate. Most important, the flange reduces antenna contribution to system noise; whereas, dichroic plate increases noise temperature by 2 or 3 degrees.
Variance of signal-estimation error minimized. Mathematical technique extracts best estimates of signal component from periodic digital samples of signal plus noise. Technique combines Kalman- and smoothingfilter algorithms to minimize mean-square estimation error based on past, present, and predicted samples of signal plus noise. Technique useful in image analysis and other applications involving processing of noisy signals.
This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to data acquisition from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN). The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals have not yet been tested using a decoding method.
In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation Keywords: Independent Component Analysis, Kurtosis, Higher order statistics.
In this paper, we introduce a new algorithm for blind source signal separation for post-nonlinear mixtures. The mixtures are assumed to be linearly mixed from unknown sources first and then distorted by memoryless nonlinear functions. The nonlinear functions are assumed to be smooth and can be approximated by polynomials. Both the coefficients of the unknown mixing matrix and the coefficients of the approximated polynomials are estimated by the gradient descent method conditional on the higher order statistical requirements. The results of simulation experiments presented in this paper demonstrate the validity and usefulness of our approach for nonlinear blind source signal separation.
A method of estimating phase drifts of microwave signals distributed to, and transmitted by, antennas in an array involves the use of the signals themselves as phase references. The method was conceived as part of the solution of the problem of maintaining precise phase calibration required for proper operation of an array of Deep Space Network (DSN) antennas on Earth used for communicating with distant spacecraft at frequencies between 7 and 8 GHz. The method could also be applied to purely terrestrial phased-array radar and other radio antenna array systems. In the DSN application, the electrical lengths (effective signal-propagation path lengths) of the various branches of the system for distributing the transmitted signals to the antennas are not precisely known, and they vary with time. The variations are attributable mostly to thermal expansion and contraction of fiber-optic and electrical signal cables and to a variety of causes associated with aging of signal-handling components. The variations are large enough to introduce large phase drifts at the signal frequency. It is necessary to measure and correct for these phase drifts in order to maintain phase calibration of the antennas. A prior method of measuring phase drifts involves the use of reference-frequency signals separate from the transmitted signals. A major impediment to accurate measurement of phase drifts over time by the prior method is the fact that although DSN reference-frequency sources separate from the transmitting signal sources are stable and accurate enough for most DSN purposes, they are not stable enough for use in maintaining phase calibrations, as required, to within a few degrees over times as long as days or possibly even weeks. By eliminating reliance on the reference-frequency subsystem, the present method overcomes this impediment. In a DSN array to which the present method applies (see figure), the microwave signals to be transmitted are generated by exciters in a signal-processing center, then distributed to the antennas via optical fibers. At each antenna, the signals are used to drive a microwave power-amplifier train, the output of which is coupled to the antenna for transmission. A small fraction of the power-amplifier-train output is sent back to the signal-processing center along another optical fiber that is part of the same fiber-optic cable used to distribute the transmitted signal to the antenna. In the signal-processing center, the signal thus returned from each antenna is detected and its phase is compared with the phase of the signal sampled directly from the corresponding exciter. It is known, from other measurements, that the signal-propagation path length from the power-amplifier-train output port to the phase center of each antenna is sufficiently stable and, hence, that sampling the signal at the power-amplifier-train output port suffices for the purpose of characterizing the phase drift of the transmitted signal at the phase center of the antenna
Chirped fiber Bragg gratings (CFBGs) are robust diagnostic sensors that are widely used to track detonation-driven and shock wave propagation. CFBGs are inscribed with a linearly chirped periodic index of refraction changes that alter the Bragg wavelength along the length of the probe. The light return of each individual Bragg element is captured by a detector at a unique time to map the full reflected spectrum. The CFBG spectrum is measured with a dispersive Fourier transform of the reflected light that temporally stretches the spectrum to increase spatial resolution and make a one-to-one map of the wavelength on a time axis. Here, we propose an improvement of CFBG temporal resolution by incorporating two co-linear laser pulses with orthogonal polarization states and a 5 ns time offset. The two separate signals were split and tracked by two separate detectors. An oscilloscope captured good separation in the signals, and two separate spectrograms were generated and interleaved in the post-processing of the data. This novel technique doubled the CFBG temporal resolution and led to a doubled location resolution. As a proof-of-concept of this technique, the resolution improvement was compared between standard CFBG measurements and the two polarization states method on a position-sensitive CFBG sensor. CFBG resolution doubling will advance sensor capabilities and will have a direct impact on improving capture and analysis in dynamic, high-explosive experiments.
To better prepare for future changes in the water cycle, it is vital to understand the regional effects of anthropogenic climate change and internal climate variability on precipitation. With existing methods, there is uncertainty regarding the regional patterns of these climate processes. We propose using machine learning to decompose seasonal total and extreme precipitation (calculated by Risser et. al., 2019) into components caused by each of the following signals: anthropogenic forcing, internal variability, and modes of natural climate variability (such as El Niño/ Southern Oscillation, Atlantic Multidecadal Oscillation, and the Pacific North American teleconnection).
Fire detection control system continually monitors its own integrity, automatically signals any malfunction, and separately signals fire in any zone being monitored. Should be of interest in fields of chemical and petroleum processing, power generation, equipment testing, and building protection.
Separate signals averaged to produce reference frequency and phase. System develops reference carrier from separate received signals. Phase of signal at each receiver determined by comparison with reference phase. Useful in applications requiring accurate phase estimates: reception of weak telemetry signals, transmitter or reflector locating, nondestructive testing of structures, or geophysical exploration.
Abstract- Digital techniques for separation of luminance and chrominance components from composite NTSC broadcast television signals are explored. First, comb filters are introduced. Secondly, filters based on linear regression are developed. Finally, a third filtering form, the allpass filter is considered. Techniques are compared based on mean squared error criteria and subjective testing. It is demonstrated that the allpass filtering technique is superior to both the comb filtering as well as the regression filtering.
FSO (Free-Space Optical)-based communication systems experience difficulty with receiving and separating signals arising from multiple transmitters, a capability that would facilitate implementation of MIMO (Multiple-In, Multiple-Out) systems. Current implementations require multiple, distinct optical antennas, each tracking a single user, which proves bulky and costly, especially if the transmitters are moving and must be tracked. A fiber-bundle receiver has the potential to use multiple pathways, corresponding to the individual fibers within the receiver, to capture different combinations of the incoming optical signals. If the bundle provides linear combining of the optical signals from both the individual fibers in the bundle and amongst the incoming optical signals, signal processing could extract the individual signals from the combinations. In this paper, we experimentally investigate whether the fiber-bundle receiver possesses sufficient linearity of operation to allow the separation of two signals by simple processing algorithms, for both turbulent and non-turbulent conditions. Data from two distinct sources enters a single-bundle, single field of view receiver, and a basis signal from one transmitter provides the reference for performing simple subtraction-based extraction of the unknown signal from the other transmitter. The experimental results show that the receiver does operate linearly, and that the linear operation remains sufficiently intact in the presence of turbulence to extract a recognizable copy of one signal from the other. The ability of the fiber bundle receiver to mitigate turbulence effects appears to assist in maintaining this sufficient level of linearity.
This paper presents a method for separating cochannel FM signals. We show that the Viterbi algorithm, traditionally limited to estimation of digital quantities, can jointly track analog FM signals by quantizing the derivative of their instantaneous frequencies.
A selection switch with high isolation between RF signal input terminals is achieved with a gated Schmitt trigger circuit feeding into a control NAND gate in each signal switching channel. The control NAND gates of the separate signal channels are coupled to an output terminal by a single NAND gate. The schmitt trigger circuits and all gates are implemented with Schottky transistor-transistor logic circuits having input clamping diodes. Each Schmitt trigger circuit includes two cascaded NAND gates and a feedback isolation Schottky diode between one input terminal connected to receive an RF input and another input terminal connected to receive a feedback signal from the second of the two cascaded NAND gates. Both NAND gates of the Schmitt trigger circuits are enabled by the same switch control signal which enables the control gates.