Department of Electrical and Computer Engineering

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A Coherent Integration Method for Moving Target Detection in a Parameter Jittering Radar System Based on Signum Coding
(IEEE Signal Processing Letters, 2022-11-04) Huang, Penghui; Xia, Xiang-Gen; Wang, Lingyu; Liu, Xingzhao; Liao, Guisheng
In this letter, we propose a novel long-time coherent integration detection method to detect an uncooperative moving target in a frequency and pulse repetition interval randomly jittering radar system based on signum coding (SC). In the proposed algorithm, an additional reference waveform is applied to eliminate the third-order harmonic influence induced by SC. Then, a generalized Keystone transform (GKT) is proposed to resolve the complex coupling among the range frequency, jittered carrier frequency, and nonuniformly sampled time. Simulation results are presented to validate the effectiveness and feasibility of the proposed method.
Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective
(Reaction Chemistry and Engineering, 2022-01-25) Batchu, Sai Praneet; Hernandez, Borja; Malhotra, Abhinav; Fang, Hui; Ierapetritou, Marianthi; Vlachos, Dionisios G.
We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. We discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.
AI Cannot Understand Memes: Experiments with OCR and Facial Emotions
(Computers, Materials & Continua, 2021-05-11) Priyadarshini, Ishaani; Cotton, Chase
The increasing capabilities of Artificial Intelligence (AI), has led researchers and visionaries to think in the direction of machines outperforming humans by gaining intelligence equal to or greater than humans, which may not always have a positive impact on the society. AI gone rogue, and Technological Singularity are major concerns in academia as well as the industry. It is necessary to identify the limitations of machines and analyze their incompetence, which could draw a line between human and machine intelligence. Internet memes are an amalgam of pictures, videos, underlying messages, ideas, sentiments, humor, and experiences, hence the way an internet meme is perceived by a human may not be entirely how a machine comprehends it. In this paper, we present experimental evidence on how comprehending Internet Memes is a challenge for AI. We use a combination of Optical Character Recognition techniques like Tesseract, Pixel Link, and East Detector to extract text from the memes, and machine learning algorithms like Convolutional Neural Networks (CNN), Region-based Convolutional Neural Networks (RCNN), and Transfer Learning with pre-trained denseNet for assessing the textual and facial emotions combined. We evaluate the performance using Sensitivity and Specificity. Our results show that comprehending memes is indeed a challenging task, and hence a major limitation of AI. This research would be of utmost interest to researchers working in the areas of Artificial General Intelligence and Technological Singularity.
An Empirical Loss Model for an Additively Manufactured Luneburg Lens Antenna
(The Applied Computational Electromagnetics Society Journal, 2022-11-14) LaRocca, Brian F.; Mirotznik, Mark S.
This research applies Effective Medium Theory and 3D Finite Element Analysis to model the transmissive loss through a waveguide fed additively manufactured Luneburg lens. New results are presented that provide rational function approximations for accurately modeling the aperture, beam, and radiation loss factors of the antenna. It introduces a normalized loss tangent and shows that the loss factors are dependent on the product of this parameter and the lens radius. Applying the constraint that the main beam of the radiation pattern contains 50% of accepted power, a maximum useful radius is tabulated for common polymers used in additive manufacturing.
Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks
(The Journal of the Acoustical Society of America, 2022-01-19) Escobar-Amado, Christian D.; Badiey, Mohsen; Pecknold, Sean
Bearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. In this work, an automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. Bearded seal sounds were year-round recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016–2017. The DCS is divided in two sections. First, regions of interest (ROI) containing possible bearded seal vocalizations are found by using the two-dimensional normalized cross correlation of the measured spectrogram and a representative template of two main calls of interest. Second, CNNs are used to validate and classify the ROIs among several possible classes. The CNNs are trained on 80% of the ROIs manually labeled from one of the four spatially separated recorders. When validating on the remaining 20%, the CNNs show an accuracy above 95.5%. To assess the generalization performance of the networks, the CNNs are tested on the remaining recorders, located at different positions, with a precision above 89.2% for the main class of the two types of calls. The proposed technique reduces the laborious task of manual inspection prone to inconstant bias and possible errors in detections.
Block-based spectral image reconstruction for compressive spectral imaging using smoothness on graphs
(Optics Express, 2022-02-17) Florez-Ospina, Juan F.; Alrushud, Abdullah K. M.; Lau, Daniel L.; Arce, Gonzalo R.
A novel reconstruction method for compressive spectral imaging is designed by assuming that the spectral image of interest is sufficiently smooth on a collection of graphs. Since the graphs are not known in advance, we propose to infer them from a panchromatic image using a state-of-the-art graph learning method. Our approach leads to solutions with closed-form that can be found efficiently by solving multiple sparse systems of linear equations in parallel. Extensive simulations and an experimental demonstration show the merits of our method in comparison with traditional methods based on sparsity and total variation and more recent methods based on low-rank minimization and deep-based plug-and-play priors. Our approach may be instrumental in designing efficient methods based on deep neural networks and covariance estimation.
Central Attention Network for Hyperspectral Imagery Classification
(IEEE Transactions on Neural Networks and Learning Systems, 2022-03-10) Liu, Huan; Li, Wei; Xia, Xiang-Gen; Zhang, Mengmeng; Gao, Chen-Zhong; Tao, Ran
In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e., a query pixel to be classified) and pixels that are similar to the central pixel on an HSI patch. Then, employed with the HSI-tailored SDPCA module, a central attention network (CAN) is proposed by combining HSI-tailored dense connections of the features of the hidden layers and the spectral information of the query pixel. MiniCAN as a simplified version of CAN is also investigated. Superior classification performance of CAN and miniCAN on three datasets of different scenarios demonstrates their effectiveness and benefits compared with state-of-the-art methods.
Channel Estimation for Massive MIMO: An Information Geometry Approach
(IEEE Transactions on Signal Processing, 2022-10-04) Yang, Jiyuan; Lu, An-An; Chen, Yan; Gao, Xiqi; Xia, Xiang-Gen; Slock, Dirk T. M.
In this paper, we investigate the channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Using the sampled steering vectors in the space and frequency domain, we first establish a space-frequency (SF) beam based statistical channel model. The accuracy of the channel model can be guaranteed with sufficient sampling steering vectors. With the channel model, the channel estimation is formulated as obtaining the a posteriori information of the beam domain channel. We solve this problem by calculating an approximation of the a posteriori distribution's marginals within the information geometry framework. Specifically, by viewing the set of Gaussian distributions and the set of the marginals as a manifold and its e -flat submanifold, we turn the calculation of the marginals into an iterative projection process between submanifolds with different constraints. We derive the information geometry approach (IGA) for channel estimation by calculating the solutions of projections. We prove that the mean of the approximate marginals at the equilibrium of IGA is equal to that of the a posteriori distribution. Simulations demonstrate that the proposed IGA can accurately estimate the beam domain channel within limited iterations.
Communication-Constrained Routing and Traffic Control: A Framework for Infrastructure-Assisted Autonomous Vehicles
(IEEE Transactions on Intelligent Transportation Systems, 2022-09-07) Liu, Guangyi; Salehi, Seyedmohammad; Bala, Erdem; Shen, Chien-Chung; Cimini, Leonard J.
With the increasing demand for advanced autonomous driving, the available communication resources may become constrained over different geographic areas. In addition, due to dynamic channel variations and imperfect cell deployments, guaranteeing the required communication resources for data hungry and delay-sensitive applications in autonomous vehicles (AVs), along their entire trips, becomes challenging. To address these issues, the paper investigates the feasibility of a hybrid system-optimum and user-equilibrium AV traffic framework subject to communication constraints, as well as its performance gain. Within such a framework, the paper introduces the problems of communication-constrained routing (CCR) and traffic control (CCTC) in the context of infrastructure-assisted autonomous driving and presents respective solutions. For CCR, an efficient two-layered routing scheme is proposed which can provide optimal trip duration. Simulation results show that the routing scheme achieves a good balance between longer duration of communication coverage and acceptable source-to-destination travel time. For CCTC, it is shown that there exists an optimal AV speed on each road segment, as well as an optimal inter-AV distance and an optimal number of AVs in each cell, to maximize the road-network AV throughput within a single cell. Moreover, spectrum allocation is used to achieve Pareto-optimal road-network throughput across cells, and a new key performance index (KPI) is defined to evaluate the traffic control capability of cellular systems. Simulation results validate the improvement of AV throughput via the proposed CCTC solution.
Compact thin film lithium niobate folded intensity modulator using a waveguide crossing
(Optics Express, 2022-03-04) Nelan, Sean; Mercante, Andrew; Hurley, Cooper; Shi, Shouyuan; Yao, Peng; Shopp, Benjamin; Prather, Dennis W.
A small footprint, low voltage and wide bandwidth electro-optic modulator is critical for applications ranging from optical communications to analog photonic links, and the integration of thin-film lithium niobate with photonic integrated circuit (PIC) compatible materials remains paramount. Here, a hybrid silicon nitride and lithium niobate folded electro-optic Mach Zehnder modulator (MZM) which incorporates a waveguide crossing and 3 dB multimode interference (MMI) couplers for splitting and combining light is reported. This modulator has an effective interaction region length of 10 mm and shows a DC half wave voltage of roughly 4.0 V, or a modulation efficiency (Vπ ·L) of roughly 4 V·cm. Furthermore, the device demonstrates a power extinction ratio of roughly 23 dB and shows .08 dB/GHz optical sideband power roll-off with index matching fluid up to 110 GHz, with a 3-dB bandwidth of 37.5 GHz.
A comparative approach to stabilizing mechanisms between discrete- and continuous-time consumer-resource models
(PLoS ONE, 2022-04-12) Singh, Abhyudai
There is rich literature on using continuous-time and discrete-time models for studying population dynamics of consumer-resource interactions. A key focus of this contribution is to systematically compare between the two modeling formalisms the stabilizing/destabilizing impacts of diverse ecological processes that result in a density-dependent consumer attack rate. Inspired by the Nicholson-Bailey/Lotka-Volterra models in discrete-time/continuous-time, respectively, we consider host-parasitoid interactions with an arbitrary parasitoid attack rate that is a function of both the host/parasitoid population densities. Our analysis shows that a Type II functional response is stabilizing in both modeling frameworks only when combined with other mechanisms, such as mutual interference between parasitoids. A Type III functional response is by itself stabilizing, but the extent of attack-rate acceleration needed is much higher in the discrete-time framework, and its stability regime expands with increasing host reproduction. Finally, our results show that while mutual parasitoid interference can stabilize population dynamics, cooperation between parasitoids to handle hosts is destabilizing in both frameworks. In summary, our comparative analysis systematically characterizes diverse ecological processes driving stable population dynamics in discrete-time and continuous-time consumer-resource models.
Compressive Spectral X-Ray CT Reconstruction via Deep Learning
(IEEE Transactions on Computational Imaging, 2022-10-20) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Restrepo, Carlos; Arce, Gonzalo R.
Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.
Controlling Microring Resonator Extinction Ratio via Metal-Halide Perovskite Nonlinearity
(Advanced Optical Materials, 2021-09-09) Wang, Feifan; Zhao, Lianfeng; Xiao, Yahui; Li, Tiantian; Wang, Yixiu; Soman, Anishkumar; Lee, Hwaseob; Kananen, Thomas; Hu, Xiaoyong; Rand, Barry P.; Gu, Tingyi
The exceptionally high optical nonlinearities, wide bandgap, and homogeneity in solution-processed metal-halide perovskite media are utilized as optical nonlinear elements on a silicon photonic platform for low-power-active components, such as all-optical switches, modulators, and lasers. With room temperature back-end-of-line compatible processing, a hybrid metal-halide perovskite (CH3NH3PbI3) microring resonator (MRR) structure is fabricated on a foundry-processed low-loss silicon photonic platform. With in-plane exci-tation near the light intensity of 110 W m−2, strong two-photon absorption and free-carrier absorption saturation are observed. With 103 field enhancements by MRRs, the photorefractive effect in the metal-halide perovskite reduces linear absorption, represented by 102 improvement of the MRR’s intrinsic quality factor and 20 dB enhancement of the extinction ratio.
Cost-Efficient RIS-Assisted Transmitter Design With Discrete Phase Shifts for Wireless Communication
(IEEE Wireless Communications Letters, 2022-12-30) Pi, Xiangyu; Yi, Pengfei; Xiao, Zhenyu; Zhang, Wei; Han, Zhu; Xia, Xiang-Gen
In this letter, in order to achieve higher spectral and energy efficiency, we propose a novel cost-efficient transmitter conceptual design based on reconfigurable intelligent surface (RIS) with discrete phase shifts. The key idea is to directly utilize the digital signal to adjust the discrete reflection coefficients of RIS, resulting that the phases of the reflected carrier signal being modulated without the need for complex digital signal processing (DSP) hardware and costly radio frequency (RF) chains. Furthermore, a joint digital modulation and beamforming method is developed to enable information transmission as well as enhance signal strength. Based on the proposed transmitter, we derive the closed-form expressions of the signal-to-noise ratio (SNR) and bit error rate (BER) of the received signal and analyze the impact of hardware constraints on communication performance. Extensive simulation results validate that the novel design of RIS-assisted transmitter provides a cost-effective and power-efficient solution for wireless communications.
Design and Additive Manufacture of Multi-Tapered Coaxial Baluns
(IEEE Transactions on Components, Packaging and Manufacturing Technology, 2022-11-16) McParland, Kyle P.; Mirotznik, Mark S.
In this article, a new design method is introduced for wideband transitions from an unbalanced coaxial feed to a balanced two wire port. The device expands upon the traditional tapered coaxial balun by allowing for variations of both the inner and outer conductor radii in addition to the slot width. This multi-tapered approach provides additional design freedoms useful for realizing a wide range of impedance ratios, satisfying fixed geometrical constraints, and reducing transmission losses. A multimaterial additive manufacturing (AM) approach is described for fabricating the balun’s complex 3-D geometry. Experimental validation was conducted within the Ku -band for printed back-to-back baluns and an integrated antenna feed that combined a printed connector, a multi-tapered coaxial balun, and a spiral antenna. Simulated and measured results showed good performance over the frequency band of interest.
Dirty Metadata: Understanding A Threat to Online Privacy
(IEEE Security and Privacy, 2022-03-01) Gouert, Charles; Tsoutsos, Nektarios Georgios
People have a certain expectation of privacy when using widespread and trusted online services. This study surveys several popular web applications to understand if uploaded user images are handled securely using open source digital forensics tools and a custom software framework.
Efficient passivation of n-type and p-type silicon surface defects by hydrogen sulfide gas reaction
(Journal of Physics: Condensed Matter, 2021-09-03) Das, U. K.; Theisen, R.; Hua, A.; Upadhyaya, A.; Lam, I.; Mouri, T. K.; Jiang, N.; Hauschild, D.; Weinhardt, L.; Yang, W.; Rohatgi, A.; Heske, C.
An efficient surface defect passivation is observed by reacting clean Si in a dilute hydrogen sulfide–argon gas mixture (<5% H2S in Ar) for both n-type and p-type Si wafers with planar and textured surfaces. Surface recombination velocities of 1.5 and 8 cm s−1 are achieved on n-type and p-type Si wafers, respectively, at an optimum reaction temperature of 550 °C that are comparable to the best surface passivation quality used in high efficiency Si solar cells. Surface chemical analysis using x-ray photoelectron spectroscopy shows that sulfur is primarily bonded in a sulfide environment, and synchrotron-based soft x-ray emission spectroscopy of the adsorbed sulfur atoms suggests the formation of S–Si bonds. The sulfur surface passivation layer is unstable in air, attributed to surface oxide formation and a simultaneous decrease of sulfide bonds. However, the passivation can be stabilized by a low-temperature (300 °C) deposited amorphous silicon nitride (a-Si:NX:H) capping layer.
Experimental demonstration and optimization of X-ray StaticCodeCT
(Applied Optics, 2021-10-18) Cuadros, Angela P.; Liu, Xiaokang; Parsons, Paul E.; Ma, Xu; Arce, Gonzalo R.
As the use of X-ray computed tomography (CT) grows in medical diagnosis, so does the concern for the harm a radiation dose can cause and the biological risks it represents. StaticCodeCT is a new low-dose imaging architecture that uses a single-static coded aperture (CA) in a CT gantry. It exploits the highly correlated data in the projection domain to estimate the unobserved measurements on the detector. We previously analyzed the StaticCodeCT system by emulating the effect of the coded mask on experimental CT data. In contrast, this manuscript presents test-bed reconstructions using an experimental cone-beam X-ray CT system with a CA holder. We analyzed the reconstruction quality using three different techniques to manufacture the CAs: metal additive manufacturing, cold casting, and ceramic additive manufacturing. Furthermore, we propose an optimization method to design the CA pattern based on the algorithm developed for the measurement estimation. The obtained results point to the possibility of the real deployment of StaticCodeCT systems in practice.
Fabrication of Germanium Tin Microstructures Through Inductively Coupled Plasma Dry Etching
(IEEE Transactions on Nanotechnology, 2021-09-30) Lin, Guangyang; Cui, Peng; Wang, Tao; Hickey, Ryan; Zhang, Jie; Zhao, Haochen; Kolodzey, James; Zeng, Yuping
Germanium tin (GeSn) with a Sn content of >12% has a great potential for optoelectronic devices due to its direct bandgap property. In this work, the anisotropic etching of GeSn with Sn content of 12.5% and selective etching of Ge over GeSn were explored by inductively couple plasma (ICP) dry etching to obtain various microstructures. Through adding oxygen into chlorine and argon and adjusting the process pressure, the anisotropic etching of GeSn was optimized with an ideal sidewall angle of 89 o . The optimized process is compatible with both positive and negative resists. By altering the ICP power, Ge etching recipes with low and high etching rates were developed, which are favorable for fabricating GeSn nano- and micro-structures, respectively. An etching selectivity of >126 for Ge over GeSn with Sn content of >10% can be achieved. With the optimized dry etching recipes, suspended GeSn microribbons and microdisks were realized. Ultimately, the suspended GeSn microstructures were transferred onto 40-nm-thick ZrO 2 on p + -Si to form a GeSn-on-insulator (GeSnOI) substrate. For a fabricated 45-nm-thick Ge 0.875 Sn 0.125 OI back-gated transistor, the subthreshold swing (SS) of 240 mV/dec is reasonably low for a non-optimized device, suggesting that the explored dry etching methods are promising for device processing.
A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection
(IEEE Transactions on Image Processing, 2022-02-09) Huang, Zhanchao; Li, Wei; Xia, Xiang-Gen; Tao, Ran
Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.

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