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1. Non-Uniform Array and Frequency Spacing for Regularization-Free Gridless DOA

   https://doi.org/10.1109/TSP.2024.3386018  

   Wu, Yifan; Wakin, Michael B; Gerstoft, Peter (January 2024, IEEE Transactions on Signal Processing)

   Gridless direction-of-arrival (DOA) estimation with multiple frequencies can be applied in acoustics source localization problems. We formulate this as an atomic norm minimization (ANM) problem and derive an equivalent regularization-free semi-definite program (SDP) thereby avoiding regularization bias. The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the solution of the SDP. We also propose a fast SDP program to deal with non-uniform array and frequency spacing. For non-uniform spacings, the Toeplitz structure will not exist, but the DOA is retrieved via irregular Vandermonde decomposition (IVD), and we theoretically guarantee the existence of the IVD. We extend ANM to the multiple measurement vector (MMV) cases and derive its equivalent regularization-free SDP. Using multiple frequencies and the MMV model, we can resolve more sources than the number of physical sensors for a uniform linear array. Numerical results demonstrate that the regularization-free framework is robust to noise and aliasing, and it overcomes the regularization bias. 

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2. Inkless Multimaterial Printing Flexible Electronics by Directed Laser Deposition at Nano- and Microscale

   https://doi.org/10.1021/acsanm.3c01814  

   Ahmadi, Zabihollah; Patel, Aarsh; Taba, Adib; Jaiswal, Suman; Lee, Seungjong; Shamsaei, Nima; Mahjouri-Samani, Masoud (August 2023, ACS Applied Nano Materials)

   Additively manufactured electronics (AMEs), also known as printed electronics, are becoming increasingly important for the anticipated Internet of Things (IoT). This requires manufacturing technologies that allow the integration of various pure functional materials and devices onto different flexible and rigid surfaces. However, the current ink-based technologies suffer from complex and expensive ink formulation, ink-associated contaminations (additives/solvents), and limited sources of printing materials. Thus, printing contamination-free and multimaterial structures and devices is challenging. Here, a multimaterial additive nanomanufacturing (M-ANM) technique utilizing directed laser deposition at the nano and microscale is demonstrated, allowing the printing of lateral and vertical hybrid structures and devices. This M-ANM technique involves pulsed laser ablation of solid targets placed on a target carousel inside the printer head for in-situ generation of contamination-free nanoparticles, which are then guided via a carrier gas toward the nozzle and onto the surface of the substrate, where they are sintered and printed in real-time by a second laser. The target carousel brings a particular target in engagement with the ablation laser beam in predetermined sequences to print multiple materials, including metals, semiconductors, and insulators, in a single process. Using this M-ANM technique, various multimaterial devices such as silver/zinc oxide (Ag/ZnO) photodetector and hybrid silver/aluminum oxide (Ag/Al2O3) circuits are printed and characterized. The quality and versatility of our M-ANM technique offer a potential manufacturing option for emerging IoT. 

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3. Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

   https://doi.org/10.3390/electronics12163505  

   Jenkinson, George; Abbasi, Muhammad_Ali Babar; Molaei, Amir Masoud; Yurduseven, Okan; Fusco, Vincent (August 2023, Electronics)

   This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot. 

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4. Robust convergence analysis of distributed optimization algorithms

   https://doi.org/10.1109/ALLERTON.2017.8262874  

   Sundararajan, Akhil; Hu, Bin; Lessard, Laurent (October 2017, Communication, Control, and Computing (Allerton), 2017 55th Annual Allerton Conference on)

   We present a unified framework for analyzing the convergence of distributed optimization algorithms by formulating a semidefinite program (SDP) which can be efficiently solved to bound the linear rate of convergence. Two different SDP formulations are considered. First, we formulate an SDP that depends explicitly on the gossip matrix of the network graph. This result provides bounds that depend explicitly on the graph topology, but the SDP dimension scales with the size of the graph. Second, we formulate an SDP that depends implicitly on the gossip matrix via its spectral gap. This result provides coarser bounds, but yields a small SDP that is independent of graph size. Our approach improves upon existing bounds for the algorithms we analyzed, and numerical simulations reveal that our bounds are likely tight. The efficient and automated nature of our analysis makes it a powerful tool for algorithm selection and tuning, and for the discovery of new algorithms as well. 

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5. Recovery analysis of damped spectrally sparse signals and its relation to MUSIC

   https://doi.org/10.1093/imaiai/iaaa024  

   Li, Shuang; Mansour, Hassan; Wakin, Michael B (October 2020, Information and Inference: A Journal of the IMA)

   null (Ed.)

   Abstract One of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUltiple SIgnal Classification (MUSIC) algorithm, which exploits the low-rank structure of an autocorrelation matrix. Low-rank matrices have also received considerable attention recently in the context of optimization algorithms with partial observations, and nuclear norm minimization (NNM) has been widely used as a popular heuristic of rank minimization for low-rank matrix recovery problems. On the other hand, it has been shown that NNM can be viewed as a special case of atomic norm minimization (ANM), which has achieved great success in solving line spectrum estimation problems. However, as far as we know, the general ANM (not NNM) considered in many existing works can only handle frequency estimation in undamped sinusoids. In this work, we aim to fill this gap and deal with damped spectrally sparse signal recovery problems. In particular, inspired by the dual analysis used in ANM, we offer a novel optimization-based perspective on the classical MUSIC algorithm and propose an algorithm for spectral estimation that involves searching for the peaks of the dual polynomial corresponding to a certain NNM problem, and we show that this algorithm is in fact equivalent to MUSIC itself. Building on this connection, we also extend the classical MUSIC algorithm to the missing data case. We provide exact recovery guarantees for our proposed algorithms and quantify how the sample complexity depends on the true spectral parameters. In particular, we provide a parameter-specific recovery bound for low-rank matrix recovery of jointly sparse signals rather than use certain incoherence properties as in existing literature. Simulation results also indicate that the proposed algorithms significantly outperform some relevant existing methods (e.g., ANM) in frequency estimation of damped exponentials. 

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