arXiv subjects

Robert J. Marks II

Publications and source records attributed to Robert J. Marks II.

16 recordsLinked to original sources

arXiv2026

Implementation of a Directional Modulation Testbed for Reconfigurable Transmitters for Spatially Agile MIMO Systems

This paper demonstrates the implementation and validation of a microwave testbed for directionally modulated transmission. Directional modulation enables multiple communication and/or radar signals to be transmitted in multiple directions simultaneously using a single phased array aperture, helping to relieve spectral congestion. A two-element transmitter array is driven by a Xilinx ZCU208 Radio Frequency System on a Chip (RFSoC). Our testbed provides a foundation for developing a fully reconfigurable array transmitter for multi-user multiple-input multiple-output (MU-MIMO) radar and communications, which will incorporate in-situ measurement, reconfigurable matching circuitry, and fast tuning algorithms for frequency and directional selectivity. This testbed enables development and validation of reconfigurable techniques for adaptive spectral and spatial coexistence.

eess.SY↗

arXiv2025

Growing Into an Adaptive and Reconfigurable Paradigm for Spectrum Sharing

A significant movement from rigid use of the wireless spectrum toward adaptive and reconfigurable spectrum use has been prompted by increasing spectral crowding. Some bands have moved to an adaptive sharing model, and proposals are growing for this approach to be applied to additional bands. The process of moving from a fixed, rigid spectrum paradigm to adaptive and reconfigurable use involves maturation of policy and technology at multiple levels within the system of systems. Using the concept of Bloom's Taxonomy from the education discipline, this paper examines the development of a policy and technology progression toward a mature, adaptive and reconfigurable paradigm.

eess.SY↗

arXiv2023

Dilated POCS: Minimax Convex Optimization

Alternating projection onto convex sets (POCS) provides an iterative procedure to find a signal that satisfies two or more convex constraints when the sets intersect. For nonintersecting constraints, the method of simultaneous projections produces a minimum mean square error (MMSE) solution. In certain cases, a minimax solution is more desirable. Generating a minimax solution is possible using dilated POCS. The minimax solution uses morphological dilation of nonintersecting signal convex constraints. The sets are progressively dilated to the point where there is intersection at a minimax solution. Examples are given contrasting the MMSE and minimax solutions in problems of tomographic reconstruction of images. Dilated POCS adds a new imaging modality for image synthesis. Lastly, morphological erosion of signal sets is suggested as a method to shrink the overlap when sets intersect at more than one point.

eess.IV↗

arXiv2022

Changepoint Detection for Real-Time Spectrum Sharing Radar

Radar must adapt to changing environments, and we propose changepoint detection as a method to do so. In the world of increasingly congested radio frequencies, radars must adapt to avoid interference. Many radar systems employ the prediction action cycle to proactively determine transmission mode while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and then transmits in this predicted availability. For these selection strategies, performance is directly reliant on the quality of the underlying environmental models. In order to keep up with a changing environment, these models can employ changepoint detection. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard "garbage" data from a previous distribution, which has no relation to the current state of the environment. In this work, bayesian online changepoint detection (BOCD) is applied to the sense and predict algorithm to increase the accuracy of its models and improve its performance. In the context of spectrum sharing, these changepoints represent interferers leaving and entering the spectral environment. The addition of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to many other predictive algorithms that create models in a changing environment.

eess.SY↗

arXiv2022

In-Situ Assessment of Array Antenna Currents for Real-Time Impedance Tuning

Impedance tuning has shown promise to maximize output power, gain, or efficiency for transmitter amplifier devices in changing frequency or array environments. Using impedance tuning in phased-array elements, however, has a significant and dynamically changing impact on the transmitted array pattern. To assess the impact of impedance tuning operations on the array pattern, this paper presents a simple method to monitor the antenna input current in real time, allowing optimizations that attempt to preserve the array pattern while achieving goals of increased power, gain, or efficiency. The real-time monitoring of current provides many advantages over traditional array calibration methods in situations where the array element chains can change significantly in magnitude and/or phase during operations.

physics.ins-det↗

arXiv2022

Sometimes size does not matter

Cosmological fine-tuning has traditionally been associated with the narrowness of the intervals in which the parameters of the physical models must be located to make life possible. A more thorough approach focuses on the probability of the interval, not on its size. Most attempts to measure the probability of the life-permitting interval for a given parameter rely on a Bayesian statistical approach for which the prior distribution of the parameter is uniform. However, the parameters in these models often take values in spaces of infinite size, so that a uniformity assumption is not possible. This is known as the normalization problem. This paper explains a framework to measure tuning that, among others, deals with normalization, assuming that the prior distribution belongs to a class of maximum entropy (maxent) distributions. By analyzing an upper bound of the tuning probability for this class of distributions the method solves the so-called weak anthropic principle, and offer a solution, at least in this context, to the well-known lack of invariance of maxent distributions. The implication of this approach is that, since all mathematical models need parameters, tuning is not only a question of natural science, but also a problem of mathematical modeling. Cosmological tuning is thus a particular instantiation of a more general scenario. Therefore, whenever a mathematical model is used to describe nature, not only in physics but in all of science, tuning is present. And the question of whether the tuning is fine or coarse for a given parameter -- if the interval in which the parameter is located has low or high probability, respectively -- depends crucially not only on the interval but also on the assumed class of prior distributions. Novel upper bounds for tuning probabilities are presented.

physics.hist-ph↗

arXiv2021

Generalized active information: extensions to unbounded domains

In the last three decades, several measures of complexity have been proposed. Up to this point, most of such measures have only been developed for finite spaces. In these scenarios the baseline distribution is uniform. This makes sense because, among other things, the uniform distribution is the measure of maximum entropy over the relevant space. Active information traditionally assumes a finite interval universe of discourse but can be extended to other cases where maximum entropy is defined. Illustrating this is the purpose of this paper. Disequilibrium from maximum entropy, measured as active information, can be evaluated from baselines with unbounded support.

cs.IT↗

arXiv2021

Active information requirements for fixation on the Wright-Fisher model of population genetics

In the context of population genetics, active information can be extended to measure the change of information of a given event (e.g., fixation of an allele) from a neutral model in which only genetic drift is taken into account to a non-neutral model that includes other sources of frequency variation (e.g., selection and mutation). In this paper we illustrate active information in population genetics through the Wright-Fisher model.

cs.IT↗

arXiv2021

Generatively Augmented Neural Network Watchdog for Image Classification Networks

The identification of out-of-distribution data is vital to the deployment of classification networks. For example, a generic neural network that has been trained to differentiate between images of dogs and cats can only classify an input as either a dog or a cat. If a picture of a car or a kumquat were to be supplied to this classifier, the result would still be either a dog or a cat. In order to mitigate this, techniques such as the neural network watchdog have been developed. The compression of the image input into the latent layer of the autoencoder defines the region of in-distribution in the image space. This in-distribution set of input data has a corresponding boundary in the image space. The watchdog assesses whether inputs are in inside or outside this boundary. This paper demonstrates how to sharpen this boundary using generative network training data augmentation thereby bettering the discrimination and overall performance of the watchdog.

cs.CV↗

arXiv2021

Classification of Common Waveforms Including a Watchdog for Unknown Signals

In this paper, we examine the use of a deep multi-layer perceptron model architecture to classify received signal samples as coming from one of four common waveforms, Single Carrier (SC), Single-Carrier Frequency Division Multiple Access (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM), and Linear Frequency Modulation (LFM), used in communication and radar networks. Synchronization of the signals is not needed as we assume there is an unknown and uncompensated time and frequency offset. An autoencoder with a deep CNN architecture is also examined to create a new fifth classification category of an unknown waveform type. This is accomplished by calculating a minimum and maximum threshold values from the root mean square error (RMSE) of the radar and communication waveforms. The classifier and autoencoder work together to monitor a spectrum area to identify the common waveforms inside the area of operation along with detecting unknown waveforms. Results from testing showed the classifier had 100\% classification rate above 0 dB with accuracy of 83.2\% and 94.7\% at -10 dB and -5 dB, respectively, with signal impairments present. Results for the anomaly detector showed 85.3\% accuracy at 0 dB with 100\% at SNR greater than 0 dB with signal impairments present when using a high-value Fast Fourier Transform (FFT) size. Accurate detection rates decline as additional noise is introduced to the signals, with 78.1\% at -5 dB and 56.5\% at -10 dB. However, these low rates seen can be potentially mitigated by using even higher FFT sizes also shown in our results.

eess.SP↗

arXiv2021

Is Cosmological Tuning Fine or Coarse?

The fine-tuning of the universe for life, the idea that the constants of nature (or ratios between them) must belong to very small intervals in order for life to exist, has been debated by scientists for several decades. Several criticisms have emerged concerning probabilistic measurement of life-permitting intervals. Herein, a Bayesian statistical approach is used to assign an upper bound for the probability of tuning, which is invariant with respect to change of physical units, and under certain assumptions it is small whenever the life-permitting interval is small on a relative scale. The computation of the upper bound of the tuning probability is achieved by first assuming that the prior is chosen by the principle of maximum entropy (MaxEnt). The unknown parameters of this MaxEnt distribution are then handled in such a way that the weak anthropic principle is not violated. The MaxEnt assumption is "maximally noncommittal with regard to missing information." This approach is sufficiently general to be applied to constants of current cosmological models, or to other constants possibly under different models. Application of the MaxEnt model reveals, for example, that the ratio of the universal gravitational constant to the square of the Hubble constant is finely tuned in some cases, whereas the amplitude of primordial fluctuations is not.

physics.gen-ph↗

arXiv2021

Symbiotic Hybrid Neural Network Watchdog For Outlier Detection

Neural networks are largely black boxes. A neural network trained to classify fruit may classify a picture of a giraffe as a banana. A neural network watchdog's job is to identify such inputs, allowing a classifier to disregard such data. We investigate whether the watchdog should be separate from the neural network or symbiotically attached. We present empirical evidence that the symbiotic watchdog performs better than when the neural networks are disjoint.

cs.LG↗

arXiv2020

Range Improvement in Single-Beam Phased Array Radars by Amplifier Impedance Tuning

Significant radar range degradation can be incurred due to variations in antenna impedance from changing array scan angle. Element-wise reconfigurable impedance tuners can be used to optimally match the power amplifier device; however, the impedance matching can also affect the array pattern. In this paper, the effects of element-wise impedance tuning on the transmitted power and on the array pattern are studied, and an approach is recommended for the element-wise implementation of impedance tuners. Examples of impact on array pattern and transmitted power are given using simulation of a designed switched-stub impedance tuner. As a result of these experiments, guidelines are developed for the creation of real-time circuit optimization techniques in the array elements.

nlin.AO↗

arXiv2009

Linear State Feedback Stabilization on Time Scales

For a general class of dynamical systems (of which the canonical continuous and uniform discrete versions are but special cases), we prove that there is a state feedback gain such that the resulting closed-loop system is uniformly exponentially stable with a prescribed rate. The methods here generalize and extend Gramian-based linear state feedback control to much more general time domains, e.g. nonuniform discrete or a combination of continuous and discrete time. In conclusion, we discuss an experimental implementation of this theory.

math.OC↗

arXiv2009

Algebraic and Dynamic Lyapunov Equations on Time Scales

We revisit the canonical continuous-time and discrete-time matrix algebraic and matrix differential equations that play a central role in Lyapunov based stability arguments. The goal is to generalize and extend these types of equations and subsequent analysis to dynamical systems on domains other than $\R$ or $\Z$, e.g. nonuniform discrete domains or domains consisting of a mixture of discrete and continuous components. We compare and contrast the standard theory with the theory in this general case.

math.OC↗

arXiv2009

Controllability, Observability, Realizability, and Stability of Dynamic Linear Systems

We develop a linear systems theory that coincides with the existing theories for continuous and discrete dynamical systems, but that also extends to linear systems defined on nonuniform time domains. The approach here is based on generalized Laplace transform methods (e.g. shifts and convolution) from our recent work \cite{DaGrJaMaRa}. We study controllability in terms of the controllability Gramian and various rank conditions (including Kalman's) in both the time invariant and time varying settings and compare the results. We also explore observability in terms of both Gramian and rank conditions as well as realizability results. We conclude by applying this systems theory to connect exponential and BIBO stability problems in this general setting. Numerous examples are included to show the utility of these results.

math.OC↗