Open Access Publications - Department of Electrical and Computer Engineering

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Open access publications by faculty, postdocs, and graduate students in the Department of Electrical and Computer Engineering

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Information theoretical computational lithography based on pattern density statistics
(Optics Express, 2025-04-10) Wang, Bingyang; Ma, Xu; Liu, Jiamin; Jiang, Hao; Liu, Shiyuan; Arce, Gonzalo R.
Computational lithography is an important technology to improve the image resolution and fidelity of the optical lithography process. Recently, information theoretical models were introduced to explore the physical limit of image fidelity that can be achieved by different computational lithography methods. However, the existing models were derived based on a simple and idealized assumption of uniform pattern density, thus rendering a loose lower bound on the lithography imaging error. This work improves the accuracy of the information theoretical model by introducing a statistical approach of pattern density. In particular, a density classification rule (DCR) of mask and print image is established based on a number of randomly generated layout samples. The information transfer function between the mask and print image is formulated under the DCR constraint. Then, the optimal information transfer (OIT) and the theoretical limit of lithography image fidelity are derived using a numerical optimization algorithm with mask manufacturing regularization. It has been proved analytically and experimentally that our proposed model provides a much more accurate theoretical limit of lithography image fidelity than the conventional approach.
Short-range order and longer-range disorder revealed in germanium–tin alloy thin films by extended x-ray absorption fine structure analysis Open Access
(Journal of Applied Physics, 2025-04-08) Lentz, J. Zachary; Zhao, Haochen; Woicik, J. C.; Zeng, Yuping; McIntyre, Paul C.
Short-range order (SRO) in semiconductor alloys, a relatively under-studied structural phenomenon in which local atomic arrangements differ from those of a random solid solution, is investigated in molecular beam epitaxy (MBE)-grown GeSn thin films. A novel preparation technique is used to pattern these films into microscale ribbons that are released from the substrate for extended x-ray absorption fine structure (EXAFS) analysis. The results indicate a strong SRO in which the first shell around Sn atoms is greatly denuded of Sn atoms relative to the nominal atomic composition of the alloy. This effect is more pronounced than that observed recently in GeSn nanowires grown by chemical vapor deposition. Additionally, the presence of a longer-range disorder detected by EXAFS analysis in the shells of atoms more distant from the absorbers is indicative of the defects and inhomogeneous strain present in the MBE-grown films. The evident existence of the SRO in GeSn alloys deposited by different growth methods and in different strain states suggests that SRO is a general phenomenon in the thin films of this metastable solid solution.
Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction
(Remote Sensing, 2025-03-29) Ramirez-Jaime, Andres; Arce, Gonzalo R.; Porras-Diaz, Nestor; Ieremeiev, Oleg; Rubel, Andrii; Lukin, Vladimir; Kopytek, Mateusz; Lech, Piotr; Fastowicz, Jarosław; Okarma, Krzysztof
Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. Diffusion models then reconstruct detailed HHDCs from sparse samples on Earth. To ensure reliability despite lossy compression, we analyze distortion metrics for derived products like Digital Terrain and Canopy Height Models and evaluate the 3D reconstruction accuracy in waveform space. We identify image quality assessment metrics—ADD_GSIM, DSS, HaarPSI, PSIM, SSIM4, CVSSI, MCSD, and MDSI—that strongly correlate with subjective quality in reconstructed forest landscapes. This work advances high-resolution Earth observation by combining efficient data handling with insights into LiDAR imaging fidelity.
A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes
(Sports Medicine, 2025-03-27) Claros, Claudio C.; Anderson, Melissa N.; Qian, Wei; Brockmeier, Austin J.; Buckley, Thomas A.
Background Emerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. Objective The purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. Methods A risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant’s heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variables by the weight of evidence method, variable selection using L1-penalized logistic regression, model selection via the Akaike Information Criterion, and a final L2-regularized logistic regression fit. Results A model with 48 predictive variables yielded significant predictive performance of subsequent musculoskeletal injury with an area under the curve of 0.82. Top predictors included cognitive, balance, and reaction at baseline and acute timepoints. At a specified false-positive rate of 6.67%, the model achieves a true-positive rate (sensitivity) of 79% and a precision (positive predictive value) of 95% for identifying at-risk athletes via a well-calibrated composite risk score. Conclusions These results support the development of a sensitive and specific injury risk model using standard data combined with a novel methodological approach that may allow clinicians to target high injury risk student athletes. The development and refinement of predictive models, incorporating machine learning and utilizing comprehensive datasets, could lead to improved identification of high-risk athletes and allow for the implementation of targeted injury risk reduction strategies by identifying student athletes most at risk for post-concussion musculoskeletal injury. Key Points - There is a well-established elevated risk of post-concussion subsequent musculoskeletal injury; however, prior efforts have failed to identify risk factors. - This study developed a composite risk score model with an area under the curve of 0.82 from common concussion clinical measures and participant demographics. - By identifying athletes at elevated risk, clinicians may be able to reduce injury risk through targeted injury risk reduction programs.
Color-Coded Compressive Spectral Imager Based on Focus Transformer Network
(Sensors, 2025-03-23) Li, Jinshan; Ma, Xu; Paruchuri, Aanish; Alrushud, Abdullah; Arce, Gonzalo R.
Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms.
Monolithically integrated ultra-wideband photonic receiver on thin film lithium niobate
(Communications Engineering, 2025-03-22) Moller de Freitas, Marco; Zhu, Xiaofeng; Ullah, Md Saheed; Shi, Shouyuan; Yao, Peng; Schneider, Garrett; Prather, Dennis W.
As the demand for data capacity in wireless networks and mobile communications continues to grow, they are moving toward higher carrier frequencies and wider modulation bandwidths. Unfortunately, electronic device performance degrades in association with increased frequency and modulation bandwidths, which inhibits the application of conventional microwave architectures, particularly in the millimeter wave and terahertz regimes. Alternatively, microwave photonic systems address these challenges by offering device and system performance with exceptionally higher operational bandwidths. The challenge, however, is the ability to monolithically integrate both electronic and photonic devices into functional components that provide ultra-wideband performance up into the millimeter wave and terahertz regions. In particular, such integration remains a major technical challenge due to the high dielectric permittivity of commonly used material platforms for photonic integrated circuits, such as silicon, indium phosphide, and lithium niobate. In this paper, we present a photonic receiver consisting of a broadband antenna and a low-drive-voltage modulator monolithically integrated on thin-film lithium niobate with a quartz handle. A free-space data link is demonstrated, achieving data rates up to 2.7 Gbps using quadrature amplitude modulation, with error vector magnitude as low as 3%. This work demonstrates the potential of thin-film lithium niobate for high-frequency, monolithically integrated radiofrequency and photonic devices to enable ultra-wideband millimeter wave-to-terahertz communication systems.
A tool to measure maize root system stiffness that enables a comprehensive understanding of plant mechanics and lodging
(Journal of Experimental Botany, 2025-01-24) Hostetler, Ashley N.; Reneau, Jonathan W.; Cristiano, Joseph; Weldekidan, Teclemariam; Kermani, Taran A.; Kim, Therese T.; Sparks, Erin E.
Plant mechanical failure, known as lodging, has detrimental impacts on the quality and quantity of maize yields. Failure can occur at stalks (stalk lodging) or at roots (root lodging). While previous research has focused on proxy measures for stalk stiffness, stalk strength, and root strength, there is a need to quantify the root system stiffness, which quantifies the force–displacement relationship. Here, we report a tool to quantify the root system stiffness of maize hybrids grown in different conditions. The results show that maize hybrids with a higher root system stiffness have a greater susceptibility to root lodging. This result is consistent with expected mechanical behavior, since higher root system stiffness values mean that the plant reaches the failure strength at lower displacements compared with a plant with lower root system stiffness. Collectively, this study describes the first tool to measure root system stiffness and enables a comprehensive understanding of the integrated plant mechanics and lodging.
Folded Sub-1V Vπ Thin Film Lithium Niobate Phase Modulator
(IEEE Photonics Technology Letters, 2025-02-20) Zhu, Xiaofeng; Moller de Freitas, Marco; Shi, Shouyuan; Yao, Peng; Wang, Fuquan; Cullen, Christopher J.
This work reports a sub-1 volt drive voltage (V π ) folded phase modulator utilizing capacitor-loaded traveling wave electrodes (CLTWEs). The implementation employs a low-loss CLTWE on a quartz (Qz) substrate, facilitating both broadband index matching and minimal RF loss. A series of phase modulators of varying lengths have been fabricated and subjected to experimental characterization. The measured loss for straight CLTWEs is 0.21 dB/(cm ⋅ GHz 1/2 ). The experimentally determined DC V π is 0.52 V for a modulation length of 7.5 cm.
Capacitive-loaded traveling wave electrodes on thin film lithium niobate for sub-terahertz operation
(Optical Materials Express, 2025-02-19) Zhu, Xiaofeng; Moller de Freitas, Marco; Shi, Shouyuan; Xue, Ruidong; Yao, Peng; Prather, Dennis W.
This paper presents the experimental demonstration of a capacitive-loaded traveling wave electrode (CLTWE) on a thin-film lithium niobate electro-optic modulator designed for ultra-wideband operation, up to the sub-terahertz region. The parametric structural analysis of the T-rail in the CLTWE and its periodicity were varied to evaluate its high-frequency loss and phase index behavior. Modulator designs having various T-rail periodicities were fabricated with the S-parameters and the optical response of the TFLN modulators was characterized up to 220 GHz. The measured results reveal that the cutoff frequency of the CLTWE strongly depends on the T-rail periodicity. The 20-mm long Mach-Zehnder Modulator (MZM) with 50 µm T-rail periodicity exhibits a half-wave voltage (Vπ) of 1 V with a corresponding 3-dB bandwidth of approximately 125 GHz, and 6-dB bandwidth of 180 GHz.
A Generalized Adder for Cell Size Homeostasis: Effects on Stochastic Clonal Proliferation
(Biophysical Journal, 2025-03-21) Nieto, César; Vargas-García, César Augusto; Singh, Abhyudai
Measurements of cell size dynamics have revealed phenomenological principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder considering that cell size follows any arbitrary non-exponential growth law. Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.
Priestia megaterium cells are primed for surviving lethal doses of antibiotics and chemical stress
(Communications Biology, 2025-02-08) Guha, Manisha; Singh, Abhyudai; Butzin, Nicholas C.
Antibiotic resistant infections kill millions worldwide yearly. However, a key factor in recurrent infections is antibiotic persisters. Persisters are not inherently antibiotic-resistant but can withstand antibiotic exposure by entering a non-dividing state. This tolerance often results in prolonged antibiotic usage, increasing the likelihood of developing resistant strains. Here, we show the existence of “primed cells” in the Gram-positive bacterium Priestia megaterium, formerly known as Bacillus megaterium. These cells are pre-adapted to become persisters prior to lethal antibiotic stress. Remarkably, this prepared state is passed down through multiple generations via epigenetic memory, enhancing survival against antibiotics and other chemical stress. Previously, two distinct types of persisters were proposed: Type I and Type II, formed during stationary and log phases, respectively. However, our findings reveal that primed cells contribute to an increase in persisters during transition and stationary phases, with no evidence supporting distinct phenotypes between Type I and Type II persisters.
The magneto-mechanical coupling of multiphase magnetorheological elastomers
(Journal of Physics: Condensed Matter, 2025-01-31) Barron III, Edward J.; Williams, Ella T.; Lazarus, Nathan; Bartlett, Michael D.
Magnetorheological elastomers (MREs) are soft magnetic composites that achieve tunable changes in stiffness and damping in the presence of a magnetic field. Rigid particle composite (RC) MREs have been studied for decades for their potential applications to automotive dampers and robotic systems. Recently, magnetic fluid composite (FC) MREs have been developed which utilize magnetic fluids as inclusions to elastomers. An investigation into how inclusion phase affects magneto-mechanical performance may greatly improve MRE design capabilities. Here we experimentally evaluate the impact of solid and liquid magnetic inclusions on MRE properties, construct a simple model that captures the performance of diverse MRE material architectures, and demonstrate the use of the model to create material design maps relating the material structure, zero-field properties, and applied field to the elastic modulus and specific loss. The magneto-mechanical response is evaluated for three material architectures: RC, FC, and hybrid composite MREs that use solid particles, magnetic fluids, and a combination of the two as inclusions respectively. The model is developed through magnetic and mechanical energy principles, which suggests that the phase of the magnetic inclusions impacts the change in energy density during deformation. We show that the magneto-mechanical coupling factor is dependent on the zero-field properties of the composites, which allows for the development of material design maps to inform the fabrication of MREs based on desired properties.
Creating a Softer RoboSquid: Liquid-Metal-Based Compliant Pumps for Pulsed Jet Propulsion
(Advanced Materials Technologies, 2025-02-28) Cortazar, Juan D.; Lazarus, Nathan
Soft pumps are a crucial element in biology, from our own hearts to actuators driving the motion of many creatures in nature. For example, the squid is one of the fastest and most powerful animals in the ocean despite being almost entirely soft, using controlled jets by compressing an internal chamber to propel itself rapidly through the water. Duplicating this performance in an artificial system has, however, remained challenging. Here, a novel soft electromagnetic pump based on using coils of room temperature LM (liquid metal) to drive a permanent magnet piston is demonstrated and used to propel a squid-inspired vehicle. Liquid gallium alloys in silicone tubing are used to manufacture a stretchable electromagnet which is then integrated into a silicone bubble and used to actuate an attached permanent magnet. The robosquid here was able to achieve as high as 4.53 cm s−1 (0.65 body lengths/s) and 9.52 cm s−1 (1.38 body lengths/s) average and peak speed respectively, competitive with current squid inspired vehicles integrating rigid motors and actuators. This result is a major milestone in creating high performance pumps for soft robotics, and will enable superior performance of future soft underwater vehicles leveraging biological principles.
Single-Step Fabrication of a 3D Stretchable Inductor with Multi-jet Modeling Printing Technology
(Advanced Materials Technologies, 2025-01-23) Ahn, Jung-Bin; Yoo, Byungseok; Pines, Darryll J.; Lazarus, Nathan; Bowen, David; Kim, Soaram
The development of flexible and stretchable electronic devices is crucial for advanced electronics, which necessitate inductors with stable performance under deformation. This work presents the fabrication of stretchable polymeric matrices for 3D inductors through a single-step method via additive manufacturing. A multi-jet modeling (MJM) type 3D printer is used to print a stretchable and rigid hybrid matrix by leveraging the features of high-resolution and multi-component printing techniques. Owing to the presence of access channels designed in multiple directions, the coil channel shows a clean and smooth surface with uniformity. A room-temperature liquid metal, the eutectic gallium indium (EGaIn) alloy, is encapsulated in the designated channels without any leakage under mechanical deformation. Electrical performance tests demonstrate that the MJM-printed solenoid and toroid inductors maintain stable performance under bending and stretching deformations, which is suitable for soft electronic applications. Additionally, a flexible helical structured inductor is fabricated and tested as a wireless power receiver inductor. It generated an output voltage of more than 10 V, sufficient to power a red LED light bulb. These results highlight the simplicity and effectiveness of multi-jet 3D printing for fabricating a stretchable and rigid hybrid matrix for the inductors at once, with excellent mechanical deformability and electrical performance.
Robust Precoder Design for Massive MIMO High-Speed Railway Communications with Matrix Manifold Optimization
(IEEE Transactions on Wireless Communications, 2025-02-21) Sun, Rui; Sun, Chen; Shi, Ding; Lu, An-An; Gao, Xiqi; Xia, Xiang-Gen
In high-speed railway (HSR) communications, the channel suffers from severe Doppler and channel aging effects caused by the high mobility, making the channel outdated quickly. To address this issue, we investigate the robust precoder design against channel aging and prediction inaccuracy in massive multiple-input multiple-output (MIMO) systems with matrix manifold optimization. First of all, we introduce the concept of the quadruple beams (QBs), and establish a QB based channel model with sampled quadruple steering vectors. Then, the upcoming space domain channel of interest can achieve a higher accuracy by channel prediction with the estimated QB domain channel. To further improve the performance while save the pilot overhead, we predict the forthcoming QB domain channel and integrate the prediction inaccuracy within the a posterior QB domain statistical channel model. Then, we consider the robust precoder design aiming to maximize the upper bound of the ergodic weighted sum-rate (WSR) on the Riemannian submanifold formed by the precoders satisfying the total power constraint (TPC). Riemannian ingredients are derived for matrix manifold optimization, with which the Riemannian conjugate gradient (RCG) method is proposed to solve the unconstrained problem on the manifold. The RCG method mainly involves the matrix multiplication and avoids the need of matrix inversion of the transmit antenna dimension. The simulation results demonstrate the effectiveness of the proposed channel model and the superiority of the RCG method for robust precoder design against channel aging and prediction inaccuracy.
A Novel ISAR Imaging Algorithm for a Maneuvering Target Based on Generalized Second-Order Time-Scaled Transform
(IEEE Transactions on Geoscience and Remote Sensing, 2025-02-11) Ma, Jingtao; Wang, Jiannan; Xia, Xiang-Gen; Huang, Haihong Tao Penghui; Liao, Guisheng
It is well-known that for a maneuvering target, its ISAR imaging quality may significantly deteriorate by using the classical range-Doppler (RD) algorithm. To address this issue, this paper proposes a novel ISAR imaging algorithm based on the generalized second-order time-scaled transform (GSOTST). In the proposed method, the multicomponent cubic phase signal (CPS) modeling is adopted for the radar echo signal after translational motion compensation (TMC) in order to portray the phase change characteristics more accurately. First, the target signal is transformed into the slow-time-delay-time domain using a correlation kernel function (CKF). Subsequently, the non-stationary phase is eliminated in the slow-time-generalized delay-time frequency domain, and the GSOTST is utilized to decouple the temporal variables. Finally, the generalized Fourier transform is performed to transform the signal into the 2-D frequency domain, where the energy of the target signal is integrated into a well-focused 2-D peak, enabling the high-precision target parameter estimation as well as finely focused ISAR imaging. The experimental results from both simulation and real-measured data validate the effectiveness of the proposed algorithm.
Energy-efficient and Accuracy-aware DNN Inference with IoT Device-edge Collaboration
(IEEE Transactions on Services Computing, 2025-01-30) Jiang, Wei; Han, Haichao; Feng, Daquan; Qian, Liping; Wang, Qian; Xia, Xiang-Gen
Due to the limited energy and computing resources of Internet of Things (IoT) devices, the collaboration of IoT devices and edge servers is considered to handle the complex deep neural network (DNN) inference tasks. However, the heterogeneity of IoT devices and the various accuracy requirements of inference tasks make it difficult to deploy all the DNN models in edge servers. Moreover, a large-scale data transmission is engaged in collaborative inference, resulting in an increased demand on spectrum resource and energy consumption. To address these issues, in this paper, we first design an accuracy-aware multi-branch DNN inference model and quantify the relationship between branch selection and inference accuracy. Then, based on the multi-branch DNN model, we aim to minimize the energy consumption of devices by jointly optimizing the selection of DNN branches and partition layers, as well as the computing and communication resources allocation. The proposed problem is a mixed-integer nonlinear programming problem. We propose a hierarchical approach to decompose the problem, and then solve it with a proportional integral derivative based searching algorithm. Experimental results demonstrate our proposed scheme has better inference performance and can reduce the total energy consumption up to 65.3%, compared to other collaboration schemes
Massive MIMO-OFDM Channel Acquisition with Time-Frequency Phase-Shifted Pilots
(IEEE Transactions on Communications, 2024-11-27) Tang, Jinke; Gao, Xiqi; You, Li; Shi, Ding; Yang, Jiyuan; Xia, Xiang-Gen
In this paper, we propose a channel acquisition approach with time-frequency phase-shifted pilots (TFPSPs) for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We first present a triple-beam (TB) based channel tensor model, allowing for the representation of the space-frequency-time (SFT) domain channel as the product of beam matrices and the TB domain channel tensor. By leveraging the specific characteristics of TB domain channels, we develop TFPSPs, where distinct pilot signals are simultaneously transmitted in the frequency and time domains. Then, we present the optimal TFPSP design and provide the corresponding pilot scheduling algorithm. Further, we propose a tensor-based information geometry approach (IGA) to estimate the TB domain channel tensors. Leveraging the specific structure of beam matrices and the properties of TFPSPs, we propose a low-complexity implementation of the tensor-based IGA. We validate the efficiency of our proposed channel acquisition approach through extensive simulations. Simulation results demonstrate the superior performance of our approach. The proposed approach can effectively suppress inter-UT interference with low complexity and limited pilot overhead, thereby enhancing channel estimation performance. Particularly in scenarios with a large number of UTs, the channel acquisition method outperforms existing approaches by reducing the normalized mean square error (NMSE) by more than 8 dB.
Multichannel Enhanced Millimeter-Wave SAR Imaging via Low-Rank Tensor-Train Decomposition
(IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024-12-03) Zhang, Bangjie; Xu, Gang; Xia, Xiang-Gen; Chen, Jianlai; Zhou, Rui; Shao, Shuai
Millimeter-wave (mmWave) synthetic aperture radar (SAR) has found wide applications in autonomous driving, landslide detection, urban mapping, etc. However, the high propagation loss of mmWave bands and the limitation of transmitting power have led to limited imaging performance for mmWave SAR. In this article, an enhanced SAR imaging framework that combines along-track multiple channels is proposed using a low-rank tensor-train (TT) decomposition method, which is applicable for a co-located multiple input multiple output (MIMO) array or a phased-transmitting-digital-receiving array. First, the multichannel images are stacked into a tensor form after SAR imaging on individual channels and spatial variant array phase correction for each pixel. Then, the low-rank property of tensor stack is exploited and the TT model is utilized to find the redundancy and leverage the intrinsic structure of image stack. In addition, ket augmentation is introduced to exhibit the local data structure more clearly than the original tensor under TT decomposition. Finally, tensor-train nuclear norm is used to relax the NP-hard problem with low-rank constraint and the minimization problem is solved in the framework of alternating direction method of multipliers for enhanced imaging. The proposed algorithm can effectively improve the working distance and image quality of mmWave SAR. Numerical experiments using simulated data of MIMO SAR and measured data collected by a ground-based phased-transmitting-digital-receiving array system are carried out to verify the performance of the proposed algorithm.
Identifying mechanisms of regulation to model carbon flux during heat stress and generate testable hypotheses
(PLoS ONE, 2018-10-26) Hubbard, Allen H.; Zhang, Xiaoke; Jastrebski, Sara; Lamont, Susan J.; Singh, Abhyudai; Schmidt, Carl J.
Understanding biological response to stimuli requires identifying mechanisms that coordinate changes across pathways. One of the promises of multi-omics studies is achieving this level of insight by simultaneously identifying different levels of regulation. However, computational approaches to integrate multiple types of data are lacking. An effective systems biology approach would be one that uses statistical methods to detect signatures of relevant network motifs and then builds metabolic circuits from these components to model shifting regulatory dynamics. For example, transcriptome and metabolome data complement one another in terms of their ability to describe shifts in physiology. Here, we extend a previously described linear-modeling based method used to identify single nucleotide polymorphisms (SNPs) associated with metabolic changes. We apply this strategy to link changes in sulfur, amino acid and lipid production under heat stress by relating ratios of compounds to potential precursors and regulators. This approach provides integration of multi-omics data to link previously described, discrete units of regulation into functional pathways and identifies novel biology relevant to the heat stress response, in addition to generating hypotheses.

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