Open Access Publications

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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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Memristors Based on (Zr, Hf, Nb, Ta, Mo, W) High-Entropy Oxides
(Advanced Electronic Materials, 2021-04-15) Ahn, Minhyung; Park, Yongmo; Lee, Seung Hwan; Chae, Sieun; Lee, Jihang; Heron, John T.; Kioupakis, Emmanouil; Lu, Wei D.; Phillips, Jamie D.
Memristors have emerged as transformative devices to enable neuromorphic and in-memory computing, where success requires the identification and development of materials that can overcome challenges in retention and device variability. Here, high-entropy oxide composed of Zr, Hf, Nb, Ta, Mo, and W oxides is first demonstrated as a switching material for valence change memory. This multielement oxide material provides uniform distribution and higher concentration of oxygen vacancies, limiting the stochastic behavior in resistive switching. (Zr, Hf, Nb, Ta, Mo, W) high-entropy-oxide-based memristors manifest the “cocktail effect,” exhibiting comparable retention with HfO2- or Ta2O5-based memristors while also demonstrating the gradual conductance modulation observed in WO3-based memristors. The electrical characterization of these high-entropy-oxide-based memristors demonstrates forming-free operation, low device and cycle variability, gradual conductance modulation, 6-bit operation, and long retention which are promising for neuromorphic applications.
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.
Regulating gene expression to achieve temporal precision
(IFAC-PapersOnLine, 2021-07-16) Ghusinga, Khem Raj; Singh, Abhyudai
Cellular response to a stimulus is often triggered by accumulation of an appropriate gene product up to a critical threshold. How cells regulate gene expression to achieve precision in timing of such responses is a question of interest. Earlier work has shown that for a stable gene product, a constant rate of accumulation provides minimum noise in the time of response, provided that initial gene product distribution is degenerate. Here, we show that this strategy is no longer optimal if the initial gene product level is drawn from a non-degenerate distribution. We also discuss biological relevance of this finding.
Modeling protein concentrations in cycling cells using stochastic hybrid systems
(IFAC-PapersOnLine, 2021-07-16) Vahdat, Zahra; Xu, Zikai; Singh, Abhyudai
We analyze a class of time-triggered stochastic hybrid systems where the state-space evolves as per a linear time-invariant dynamical system. This continuous-time evolution is interspersed with two kinds of stochastic resets. The first reset occurs based on an internal timer that measures the time elapsed since it last occurred. Whenever the first reset occurs, the states-space undergoes a random jump, and the timer is reset to zero. The second reset occurs based on an arbitrary timer-depended rate, and whenever this reset fires, the state-space is changed based on a given random map. We provide exact conditions for this class of systems that lead to finite statistical moments and the corresponding exact analytical expressions for the first two moments. This framework is applied to study random fluctuations in the concentration of a protein in a growing cell. In the context of this example, the timer denotes the time elapsed since the cell was born, and the cell division event (first reset) is triggered based on a timer-dependent rate. The second reset corresponds to the protein synthesis in stochastic bursts, and finally, during cell growth, protein concentration continuously decreases due to dilution. Our analysis provides closed-form formulas for the noise in the protein concentration and leads to a striking result - for a constant (timer-independent) protein synthesis rate, the noise in the protein concentration is invariant of the noise in the cell-cycle time. Finally, we provide a rigorous framework for investigating protein noise levels for different forms of timer-dependent synthesis rates, as is the case for cell-cycle regulated genes inside the cell.
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.
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.
Tolerating Defects in Low-Power Neural Network Accelerators Via Retraining-Free Weight Approximation
(ACM Transactions on Embedded Computing Systems, 2021-09-23) Hosseini, Fateme S.; Meng, Fanruo; Yang, Chengmo; Wen, Wujie; Cammarota, Rosario
Hardware accelerators are essential to the accommodation of ever-increasing Deep Neural Network (DNN) workloads on the resource-constrained embedded devices. While accelerators facilitate fast and energy-efficient DNN operations, their accuracy is threatened by faults in their on-chip and off-chip memories, where millions of DNN weights are held. The use of emerging Non-Volatile Memories (NVM) further exposes DNN accelerators to a non-negligible rate of permanent defects due to immature fabrication, limited endurance, and aging. To tolerate defects in NVM-based DNN accelerators, previous work either requires extra redundancy in hardware or performs defect-aware retraining, imposing significant overhead. In comparison, this paper proposes a set of algorithms that exploit the flexibility in setting the fault-free bits in weight memory to effectively approximate weight values, so as to mitigate defect-induced accuracy drop. These algorithms can be applied as a one-step solution when loading the weights to embedded devices. They only require trivial hardware support and impose negligible run-time overhead. Experiments on popular DNN models show that the proposed techniques successfully boost inference accuracy even in the face of elevated defect rates in the weight memory.
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.
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.
Iterative Implementation Method for Robust Target Localization in a Mixed Interference Environment
(IEEE Transactions on Geoscience and Remote Sensing, 2021-11-30) Liu, Yuan; Xia, Xiang-Gen; Liu, Hongwei; Nguyen, Anh H. T.; Khong, Andy W. H.
For the problem of target localization under the multipath propagation environment, the existing methods are mainly restricted to the limited prior information of complex reflections, especially when the target is embedded in a mixed interference environment. They may suffer from performance degradation due to the shortage of target classification ability. To address this problem, we propose a target localization method based on iterative implementation with semi-unitary constraint and eigen-decomposition technique, where a practical propagation scenario based on the spherical earth model is considered. Compared to the previous works, the proposed method can automatically distinguish a real target from the mixed interference environment with improved localization accuracy. Neither additional decorrelation preprocessing nor prior information of the dynamic scenario is required. Both simulations and real data experiments validate the effectiveness and robustness of the proposed method.
Multi-UAV Aided Millimeter-Wave Networks: Positioning, Clustering, and Beamforming
(IEEE Transactions on Wireless Communications, 2021-12-07) Zhu, Lipeng; Zhang, Jun; Xiao, Zhenyu; Xia, Xiang-Gen; Zhang, Rui
In this paper, we propose to employ multiple unmanned aerial vehicle (UAV) base stations to serve ground users in the millimeter-wave (mmWave) frequency bands. To improve the spectrum efficiency, uniform planar arrays are equipped at the UAVs and users for compensation of the high path loss and for mitigation of interference. We formulate a problem to jointly optimize the UAV positioning, user clustering, and hybrid analog-digital beamforming (BF) for the maximization of user achievable sum rate (ASR), subject to a minimum rate constraint for each user. Since the problem is highly non-convex and involves high-dimensional variable matrices and combinatorial programming variables, we develop a suboptimal solution via alternating optimization, successive convex optimization, and combinatorial optimization. First, we design the UAV positioning and user clustering under the assumption of ideal beam patterns, which significantly decouples the UAV positioning and directional BF. Then, the transmit and receive BF variables are successively optimized to approach the ideal beam patterns. Our simulation results verify the convergence and superiority of the proposed algorithm. Significant performance gains can be obtained compared to some benchmark schemes in terms of the ASR, and the proposed hybrid BF solution closely approaches a performance bound given by fully-digital BF.
Seabed Characterization Experiment: Analysis of Broadband Data
(IEEE Journal of Oceanic Engineering, 2021-12-17) Rajan, Subramaniam D.; Wan, Lin; Badiey, Mohsen; Wilson, Preston S.
Analysis of data acquired during the seabed characterization experiment conducted in the New England mud patch area in March 2017 is presented in this article. A particular feature of these data is the presence of the Airy phase in some of the modes. The mode dispersion data along with the Airy phase information are extracted using the time warping method. The mode dispersion data obtained from the analysis of the acoustic data are then used to estimate the compressional wave speed and density profiles of the sediment layers using linear inversion methods. The validity of these results is investigated, and their validity is demonstrated. The results are consistent with the results obtained by the analysis of similar data in which a nonlinear inversion method was used to estimate the sediment properties. These results further show that meaningful results can be obtained using the linearized inversion procedure.
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.
Static coded illumination strategies for low-dose x-ray material decomposition
(Applied Optics, 2022-01-20) Cuadros, Angela P.; Restrepo, Carlos M.; Noël, Peter; Arce, Gonzalo R.
Static coded aperture x-ray tomography was introduced recently where a static illumination pattern is used to interrogate an object with a low radiation dose, from which an accurate 3D reconstruction of the object can be attained computationally. Rather than continuously switching the pattern of illumination with each view angle, as traditionally done, static code computed tomography (CT) places a single pattern for all views. The advantages are many, including the feasibility of practical implementation. This paper generalizes this powerful framework to develop single-scan dual-energy coded aperture spectral tomography that enables material characterization at a significantly reduced exposure level. Two sensing strategies are explored: rapid kV switching with a single-static block/unblock coded aperture, and coded apertures with non-uniform thickness. Both systems rely on coded illumination with a plurality of x-ray spectra created by kV switching or 3D coded apertures. The structured x-ray illumination is projected through the objects of interest and measured with standard x-ray energy integrating detectors. Then, based on the tensor representation of projection data, we develop an algorithm to estimate a full set of synthesized measurements that can be used with standard reconstruction algorithms to accurately recover the object in each energy channel. Simulation and experimental results demonstrate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.
A Novel Dimension-Reduced Space–Time Adaptive Processing Algorithm for Spaceborne Multichannel Surveillance Radar Systems Based on Spatial–Temporal 2-D Sliding Window
(IEEE Transactions on Geoscience and Remote Sensing, 2022-01-21) Zou, Zihao; Xia, Xiang-Gen; Liu, Xingzhao; Liao, Guisheng
When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.
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.
Multi-Channel Clutter Modeling, Analysis, and Suppression for Missile-borne Radar Systems
(IEEE Transactions on Aerospace and Electronic Systems, 2022-01-31) Huang, Penghui; Yang, Hao; Zou, Zihao; Xia, Xiang-Gen; Liao, Guisheng
When a missile-borne radar system works in downward-looking surveillance mode, the broadened ground clutter signal in virtue of platform high-speed motion will be received by the radar receiver, which will cause difficulty in moving target detection and attacking. Unlike airborne and spaceborne platforms, a missile-borne platform exhibits some unique motion characteristics, such as diving, spinning, and coning, causing the clutter space-time distribution property significantly different from those of airborne and spaceborne radar platforms. In addition, the forward target striking requirements make the missile-borne clutter space-time spectrum further exhibit the severe range-dependent property. To deal with these issues, accurate motion modeling of a missile-borne radar platform is firstly carried out in this paper, where the complex platform motions including forward-looking diving, spinning, and coning are considered. Then, the autocorrelation processing combined with Iterative Adaptive Approach (IAA) is applied to estimate the clutter angle-Doppler center frequencies, so as to effectively realize the clutter non-stationary compensation along spatial and temporal directions. Finally, a time-domain sliding window based subspace projection (TSWSP) method is proposed to achieve the robust clutter suppression. Both simulation and real-measured radar data processing results are presented to validate the effectiveness and feasibility of the proposed algorithm.
A Physically-Based Model of Vertical TFET--Part II: Drain Current Model
(IEEE Transactions on Electron Devices, 2022-02-08) Cheng, Qi; Khandelwal, Sourabh; Zeng, Yuping
A physically based model for the tunneling current of vertical tunneling field transistors (TFET) is proposed. In part I, the expression of φ1D(x,) is derived from the multi-branch general solutions of Poisson's equation. The model's results are verified with TCAD simulation for transistors with different materials, device geometries, and biases. In this article, a surface potential model is validated at different device regions which include channel and drain. Based on the above two electric potential models, Kane's tunneling formula is utilized for the calculation of band-to-band tunneling current. The proposed current model is valid for all transistors' operating regions. The quantum effect on the band-structure parameters is taken into account in the modeling of InAs vertical TFET. It is shown that the channel thickness needs to be optimized to achieve the highest drive current.
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.
QRnet: fast learning-based QR code image embedding
(Multimedia Tools and Applications, 2022-02-16) Pena-Pena, Karelia; Lau, Daniel L.; Arce, Andrew J.; Arce, Gonzalo R.
Quick Response (QR) codes usage in e-commerce is on the rise due to their versatility and ability to connect offline and online content, taking over almost every aspect of a business from posters to payments. Thus, many efforts have aimed at improving the visual quality of QR codes to be easily included in publicity designs in billboards and magazines. The most successful approaches, however, are slow since optimization algorithms are required for the generation of each beautified QR code, hindering its online customization. The aim of this paper is the fast generation of visually pleasant and robust QR codes. The proposed framework leverages state-of-the-art deep-learning algorithms to embed a color image into a baseline QR code in seconds while keeping a maximum probability of error during the decoding procedure. Halftoning techniques that exploit the human visual system (HVS) are used to smooth the embedding of the QR code structure in the final QR code image while reinforcing the decoding robustness. Compared to optimization-based methods, our framework provides similar qualitative results but is 3 orders of magnitude faster.

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