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1. Wideband Array Signal Processing with Real-Time Adaptive Interference Mitigation

   https://doi.org/10.3390/s23146584  

   Whipple, Adam; Ruzindana, Mark W; Burnett, Mitchell C; Kunzler, Jakob W; Lyman, Kayla; Jeffs, Brian D; Warnick, Karl F (July 2023, Sensors)

   Wideband beamforming and interference cancellation for phased array antennas requires advances in signal processing algorithms, software, and specialized hardware platforms. A high-throughput array receiver has been developed that enables communication in radio frequency interference-rich environments with field programmable gate array (FPGA)-based frequency channelization and packetization. In this study, a real-time interference mitigation algorithm was implemented on graphics processing units (GPUs) contained in the data pipeline. The key contribution is a hardware and software pipeline for subchannelized wideband array signal processing with 150 MHz instantaneous bandwidth and interference cancellation with a heterogeneous, distributed, and scaleable digital signal processing (DSP) architecture that achieves 30 dB interferer cancellation null depth in real time with a moving interference source. 

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2. Biological signal processing filters via engineering allosteric transcription factors

   https://doi.org/10.1073/pnas.2111450118  

   Groseclose, Thomas M.; Hersey, Ashley N.; Huang, Brian D.; Realff, Matthew J.; Wilson, Corey J. (November 2021, Proceedings of the National Academy of Sciences)

   Signal processing is critical to a myriad of biological phenomena (natural and engineered) that involve gene regulation. Biological signal processing can be achieved by way of allosteric transcription factors. In canonical regulatory systems (e.g., the lactose repressor), an INPUT signal results in the induction of a given transcription factor and objectively switches gene expression from an OFF state to an ON state. In such biological systems, to revert the gene expression back to the OFF state requires the aggressive dilution of the input signal, which can take 1 or more d to achieve in a typical biotic system. In this study, we present a class of engineered allosteric transcription factors capable of processing two-signal INPUTS, such that a sequence of INPUTS can rapidly transition gene expression between alternating OFF and ON states. Here, we present two fundamental biological signal processing filters, BANDPASS and BANDSTOP, that are regulated by D-fucose and isopropyl-β-D-1-thiogalactopyranoside. BANDPASS signal processing filters facilitate OFF–ON–OFF gene regulation. Whereas, BANDSTOP filters facilitate the antithetical gene regulation, ON–OFF–ON. Engineered signal processing filters can be directed to seven orthogonal promoters via adaptive modular DNA binding design. This collection of signal processing filters can be used in collaboration with our established transcriptional programming structure. Kinetic studies show that our collection of signal processing filters can switch between states of gene expression within a few minutes with minimal metabolic burden—representing a paradigm shift in general gene regulation. 

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3. Underwater optical signal detection system using diffuser-based lensless imaging

   https://doi.org/10.1364/OE.512438  

   Huang, Yinuo; Krishnan, Gokul; Goswami, Saurabh; Javidi, Bahram (January 2024, Optics Express)

   We propose a diffuser-based lensless underwater optical signal detection system. The system consists of a lensless one-dimensional (1D) camera array equipped with random phase modulators for signal acquisition and one-dimensional integral imaging convolutional neural network (1DInImCNN) for signal classification. During the acquisition process, the encoded signal transmitted by a light-emitting diode passes through a turbid medium as well as partial occlusion. The 1D diffuser-based lensless camera array is used to capture the transmitted information. The captured pseudorandom patterns are then classified through the 1DInImCNN to output the desired signal. We compared our proposed underwater lensless optical signal detection system with an equivalent lens-based underwater optical signal detection system in terms of detection performance and computational cost. The results show that the former outperforms the latter. Moreover, we use dimensionality reduction on the lensless pattern and study their theoretical computational costs and detection performance. The results show that the detection performance of lensless systems does not suffer appreciably. This makes lensless systems a great candidate for low-cost compressive underwater optical imaging and signal detection. 

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4. Radar signal demixing via convex optimization

   https://doi.org/10.1109/ICDSP.2017.8096069  

   Xie, Youye; Li, Shuang; Tang, Gongguo; Wakin, Michael B. (August 2017, 2017 22nd International Conference on Digital Signal Processing (DSP))

   The problem of decomposing a signal into components of different structures arises in many applications of signal processing and machine learning. In this paper, we study such a signal demixing problem for airborne radar systems, where the received signal consists of contributions from targets, jammers, and clutter. We promote the structures of the target and jammer components by designing two atomic norms, while we model the clutter signal as lying in a known subspace. This allows the formulation of a convex optimization that is able to separate these components, enabling the localization of targets and jammers as well as the supression of the clutter. Simulations show the superior performance of our approach compared to the classical space-time adaptive processing (STAP) technique. 

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5. EM-Based Radar Signal Processing and Tracking of Maneuvering Targets

   Nussbaum, Alan; Blair, Dare; Keel, Byron; Ramachandran, Umakishore (November 2023, IEEE International Radar Conference 2023)

   The accuracy of radar tracks depends strongly on the variances of the measurements, and those variances are inversely proportional to the signal-to-noise (SNR) produced the hardware and signal processor. The signal processor uses matched filter processing, and the efficiency of that depends on knowledge of the kinematics of the target. In particular, the matched filter performance depends heavily on range rate and range acceleration. Traditionally, the predicted state of the target from the track filter is used for matched filter processing, but the predicted kinematic state can have rather large errors, and those errors result in match filter loss. This loss can be very large for maneuvering (i.e., accelerating) targets. In this paper, an expected-maximization (EM) approach is taken to jointly address signal processing and tracking. The signal processor maximizes the SNR using the predicted state and produces measurements. The state estimator ( e.g., Kalman filter) uses those measurements to produce expected values of the kinematic state (i.e. the nuisance parameters). The signal processor then maximizes the SNR using the new state estimates. This process continues until the maximum likelihood values of the measurements are achieved. In this paper, the Interacting Multiple Model (IMM) estimator is introduced for the tracking function better address sudden maneuvers. The EM-Based approach to join signal processing and tracking are presented along with a discussion of the real-time computing. Monte Carlo simulation results are given to illustrate a 6 dB improvement in SNR and enhanced tracks for a maneuvering target. 

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