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Recent Submissions

  • Investigation of a Separated Short-Wavelength Peak in InGaN Red Light-Emitting Diodes

    Kirilenko, Pavel; Zhuang, Zhe; Iida, Daisuke; Velazquez-Rizo, Martin; Ohkawa, Kazuhiro (Crystals, MDPI AG, 2021-09-15) [Article]
    We fabricated indium gallium nitride (InGaN) red light-emitting diodes (LEDs) with a peak emission wavelength of 649 nm and investigated their electroluminescence (EL) properties. An additional separated peak in the EL spectrum of the red LEDs at 20 mA was observed at 465 nm. This additional peak also exhibits a blue-shift with increasing currents as does the main emission peak. Using high-resolution microscopy, we observed many point-like emission spots in the EL emission images at the currents below 1 mA. However, these emission spots cannot be identified at currents above 5 mA because the red emission from quantum wells (QWs) is much stronger than that emitted by these spots. Finally, we demonstrate that these emission spots are related to the defects generated in red QWs. The measured In content was lower at the vicinity of the defects, which was regarded as the reason for separated short-wavelength emission in red InGaN LEDs.
  • Janus monolayers of magnetic transition metal dichalcogenides as an all-in-one platform for spin-orbit torque

    Smaili, Idris; Laref, Slimane; Garcia, Jose H.; Schwingenschlögl, Udo; Roche, Stephan; Manchon, Aurelien (Physical Review B, American Physical Society (APS), 2021-09-15) [Article]
    We theoretically predict that vanadium-based Janus dichalcogenide monolayers constitute an ideal platform for spin-orbit torque memories. Using first-principles calculations, we demonstrate that magnetic exchange and magnetic anisotropy energies are higher for heavier chalcogen atoms, while the broken inversion symmetry in the Janus form leads to the emergence of Rashba-like spin-orbit coupling. The spin-orbit torque efficiency is evaluated using optimized quantum transport methodology and found to be comparable to heavy nonmagnetic metals. The coexistence of magnetism and spin-orbit coupling in such materials with tunable Fermi-level opens new possibilities for monitoring magnetization dynamics in the perspective of nonvolatile magnetic random access memories.
  • Clean Carbon Cycle via High-Performing and Low-Cost Solar-Driven Production of Freshwater

    Mazzone, Valerio; Bonifazi, Marcella; Aegerter, Christof M.; Cruz, Aluizio M.; Fratalocchi, Andrea (Advanced Sustainable Systems, Wiley, 2021-09-12) [Article]
    While renewable power available worldwide costs increasingly less than the least expensive option based on fossil fuels, countries continue to increase their coal-fired capacity, which should conversely fall by 80% within a decade to limit global warming effects. To address the challenges to the implementation of such an aim, here, a path is explored that leverages on a previously unrecognized aspect of coal, opening to a new solar-driven carbon cycle that is environmentally friendly. By engineering the porosity matrix of coal into a suitably designed compressed volumetric structure, and by coupling it with a network of cotton fibers, it is possible to create a record performing device for freshwater production, with a desalination rate per raw material cost evaluated at 1.39 kg h −1 $ −1 at one sun intensity. This value is between two and three times higher than any other solar desalination device proposed to date. These results could envision a clean and socially sustainable cycle for carbon materials that, while enabling an enhanced water economy with global access to freshwater and sanitation, poses zero risks of reinjecting 𝐶𝑂2 into the environment through competing economies in the fossil's market.
  • Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain

    Hammoud, Hasan Abed Al Kader; Ghanem, Bernard (arXiv, 2021-09-12) [Preprint]
    Deep Neural Networks (DNNs) have been utilized in various applications ranging from image classification and facial recognition to medical imagery analysis and real-time object detection. As our models become more sophisticated and complex, the computational cost of training such models becomes a burden for small companies and individuals; for this reason, outsourcing the training process has been the go-to option for such users. Unfortunately, outsourcing the training process comes at the cost of vulnerability to backdoor attacks. These attacks aim at establishing hidden backdoors in the DNN such that the model performs well on benign samples but outputs a particular target label when a trigger is applied to the input. Current backdoor attacks rely on generating triggers in the image/pixel domain; however, as we show in this paper, it is not the only domain to exploit and one should always "check the other doors". In this work, we propose a complete pipeline for generating a dynamic, efficient, and invisible backdoor attack in the frequency domain. We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks through extensive experiments on various datasets and network architectures. The backdoored models are shown to break various state-of-the-art defences. We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them. We conclude the work with some remarks regarding a network's learning capacity and the capability of embedding a backdoor attack in the model.
  • MovieCuts: A New Dataset and Benchmark for Cut Type Recognition

    Pardo, Alejandro; Heilbron, Fabian Caba; Alcázar, Juan León; Thabet, Ali Kassem; Ghanem, Bernard (arXiv, 2021-09-12) [Preprint]
    Understanding movies and their structural patterns is a crucial task to decode the craft of video editing. While previous works have developed tools for general analysis such as detecting characters or recognizing cinematography properties at the shot level, less effort has been devoted to understanding the most basic video edit, the Cut. This paper introduces the cut type recognition task, which requires modeling of multi-modal information. To ignite research in the new task, we construct a large-scale dataset called MovieCuts, which contains more than 170K videoclips labeled among ten cut types. We benchmark a series of audio-visual approaches, including some that deal with the problem's multi-modal and multi-label nature. Our best model achieves 45.7% mAP, which suggests that the task is challenging and that attaining highly accurate cut type recognition is an open research problem.
  • IntraTomo: Self-supervised Learning-based Tomography via Sinogram Synthesis and Prediction

    Zang, Guangming; Idoughi, Ramzi; Li, Rui; Wonka, Peter; Heidrich, Wolfgang (IEEE, 2021-09-10) [Conference Paper]
    We propose IntraTomo, a powerful framework that combines the benefits of learning-based and model-based approaches for solving highly ill-posed inverse problems, in the Computed Tomography (CT) context. IntraTomo is composed of two core modules: a novel sinogram prediction module and a geometry refinement module, which are applied iteratively. In the first module, the unknown density field is represented as a continuous and differentiable function, parameterized by a deep neural network. This network is learned, in a self-supervised fashion, from the incomplete or/and degraded input sinogram. After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors. With these two core modules, we show that IntraTomo significantly outperforms existing approaches on several ill-posed inverse problems, such as limited angle tomography with a range of 45 degrees, sparse view tomographic reconstruction with as few as eight views, or super-resolution tomography with eight times increased resolution. The experiments on simulated and real data show that our approach can achieve results of unprecedented quality.
  • Influences of ALD Al2O3 on the surface band-bending of c-plane, Ga-face GaN and the implication to GaN-collector npn heterojunction bipolar transistors

    Gong, Jiarui; Kim, Jisoo; Ng, Tien Khee; Lu, Kuangye; Kim, Donghyeok; Zhou, Jie; Liu, Dong; Kim, Jeehwan; Ooi, Boon S.; Ma, Zhenqiang (arXiv, 2021-09-10) [Preprint]
    Due to the lack of effective p-type doping in GaN and the adverse effects of surface band-bending of GaN on electron transport, developing practical GaN heterojunction bipolar transistors has been impossible. The recently demonstrated approach of grafting n-type GaN with p-type semiconductors, like Si and GaAs, by employing ultrathin (UO) Al2O3 at the interface of Si/GaN and GaAs/GaN, has shown the feasibility to overcome the poor p-type doping challenge of GaN by providing epitaxy-like interface quality. However, the surface band-bending of GaN that could be influenced by the UO Al2O3 has been unknown. In this work, the band-bending of c-plane, Ga-face GaN with UO Al2O3 deposition at the surface of GaN was studied using X-ray photoelectron spectroscopy (XPS). The study shows that the UO Al2O3 can help in suppressing the upward band-bending of the c-plane, Ga-face GaN with a monotonic reduction trend of the upward band-bending energy from 0.48 eV down to 0.12 eV as the number of UO Al2O3 deposition cycles is increased from 0 to 20 cycles. The study further shows that the band-bending can be mostly recovered after removing the Al2O3 layer, concurring that the change in the density of fixed charge at the GaN surface caused by UO Al2O3 is the main reason for the surface band-bending modulation. The potential implication of the surface band-bending results of AlGaAs/GaAs/GaN npn heterojunction bipolar transistor (HBT) was preliminarily studied via Silvaco® simulations.
  • Solar Powered Small Unmanned Aerial Vehicles: A Review

    Elatab, Nazek; Mishra, Rishabh B.; Alshanbari, Reem; Hussain, Muhammad Mustafa (Energy Technology, Wiley, 2021-09-08) [Article]
    In recent years, there has been an increasing demand for unmanned aerial vehicles (UAVs) with various capabilities suitable for both military and civilian applications. There is also a substantial interest in the development of novel drones that can fly autonomously in different environments and locations and perform various missions. Nevertheless, current battery-powered UAVs are limited by their flight range. Consequently, several approaches are being developed to enhance the flight endurance of drones, including augmenting the drone with solar power. In this review paper, we identify the different classifications of drones that have been developed based on their weight and flight range. Then, we explain the design challenges of the electrical systems embedded in the flying drones. Next, we discuss in detail approaches used to increase the flight endurance using various types of solar cells with respect to their materials and mechanical flexibility, in addition to various navigation and control approaches. Finally, limitations of existing solar-powered UAVs are presented in addition to proposed solutions and recommendations for the next generation of drones.
  • EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML

    Ooi, Guang An; Özakin, Mehmet Burak; Mostafa, Tarek Mahmoud Atia; Bagci, Hakan; Ahmed, Shehab; Larbi Zeghlache, Mohamed (SPE, 2021-09-07) [Conference Paper]
    In the wake of today's industrial revolution, many advanced technologies and techniques have been developed to address the complex challenges in well integrity evaluation. One of the most prominent innovations is the integration of physics-based data science for robust downhole measurements. This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing. Unlike existing electromagnetism-based inspection tools, where only circumferential average metal thickness is measured, this research investigates the artificial intelligence (AI)-assisted interpretation of a unique arrangement of electromagnetic (EM) sensors. This facilitates the development of a novel solution for through-tubing corrosion imaging that enhances defect detection with pixel-level accuracy. The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects. The integration of the array EM-sensor responses and an ANN enabled generalizable and accurate measurements of metal loss percentage across various experimental defects. It also enabled the precise predictions of the defects’ aperture sizes, numbers, and locations in 360-degree coverage. Results were plotted in customized 2D heat-maps for any desired cross-section of the test casings. Further analysis of different techniques demonstrated that the LSTM-RNN could show higher precision and robustness compared to regular dense NNs, especially in the case of multiple defects. The LSTM-RNN is validated using additional data from simulated and experimental data. The results show reliable predictions even with limited training data. The model accurately predicted defects of larger and smaller sizes that were intentionally excluded from the training data to demonstrate generalizability. This highlights a major advance toward corrosion imaging behind tubing. This novel technique paves the way for the use of similar concepts on other sensors in multiple barriers imaging. Further work includes improvement to the sensor package and ANNs by adding a third dimension to the imaging capabilities to produce 3D images of defects on casings.
  • Towards self-calibrated lens metrology by differentiable refractive deflectometry

    Wang, Congli; Chen, Ni; Heidrich, Wolfgang (Optics Express, The Optical Society, 2021-09-02) [Article]
    Deflectometry, as a non-contact, fully optical metrology method, is difficult to apply to refractive elements due to multi-surface entanglement and precise pose alignment. Here, we present a computational self-calibration approach to measure parametric lenses using dual-camera refractive deflectometry, achieved by an accurate, differentiable, and efficient ray tracing framework for modeling the metrology setup, based on which damped least squares is utilized to estimate unknown lens shape and pose parameters. We successfully demonstrate both synthetic and experimental results on singlet lens surface curvature and asphere-freeform metrology in a transmissive setting.
  • Applications of Magnetic Transition Metal Dichalcogenide Monolayers to the Field of Spin-­orbitronics

    Smaili, Idris (2021-09) [Dissertation]
    Advisor: Schwingenschlögl, Udo
    Committee members: Manchon, Aurelien; El-Atab, Nazek; Laquai, Frédéric; Larsson, Andreas
    Magnetic random­access memory (MRAM) devices have been widely studied since the 1960s. During this time, the size of spintronic devices has continued to decrease. Conse quently, there is now an urgent need for new low­dimensional magnetic materials to mimic the traditional structures of spintronics at the nanoscale. We also require new effective mechanisms to conduct the main functions of memory devices, which are: reading, writ ing, and storing data. To date, most research efforts have focused on MRAM devices based on magnetic tun nel junction (MTJ), such as a conventional field­driven MRAM and spin­transfer torque (STT)­MRAM devices. Consequently, many efforts are currently focusing on new alterna tives using different techniques, such as spin­orbit torque (SOT) and magnetic skyrmions (a skyrmion is the smallest potential disruption to a uniform magnet required to obtain more effective memory devices). The most promising memory devices are SOT­MRAMs and skyrmion­based memories. This study investigates the magnetic properties of 1T­phase vanadium dichalcogenide (VXY) Janus monolayers, where X, Y= S, Se, or Te (i.e., monolayers that exhibit inversion symme try breaking due to the presence of different chalcogen elements). This study is developed along four directions: (I) the nature of the magnetism and the SOT effect of Janus mono layers; (II) the Dzyaloshinskii Moriya interaction (DMI); (III) investigation of stability en hancement by adopting practical procedures for industry; and (IV) study of the effect of a hexagonal boron nitride (h­BN) monolayer as an insulator on the magnetism of the VXY monolayer. This study provides a clear perspective for the next generation of memory de vices, such as SOT­MRAMs based on transition metal dichalcogenide monolayers.
  • Photoluminescence of InGaN-based red multiple quantum wells

    Hou, Xin; Fan, Shaosheng; Iida, Daisuke; Mei, Yang; Zhang, Baoping; Ohkawa, Kazuhiro (Optics Express, The Optical Society, 2021-09-01) [Article]
    Optical properties of InGaN-based red LED structure, with a blue pre-well, are reported. Two emission peaks located at 445.1 nm (PB) and 617.9 nm (PR) are observed in the PL spectrum, which are induced by a low-In-content blue InGaN single quantum well (SQW) and the red InGaN double quantum wells (DQWs), respectively. The peak shift of PB with increase of excitation energy is very small, which reflects the built-in electric field of PB-related InGaN single QW is remarkably decreased, being attributed to the significant reduction of residual stress in the LED structure. On the other hand, the PR peak showed a larger shift with increase of excitation energy, due to both the screening of built-in electric field and the band filling effect. The electric field in the red wells is caused by the large lattice mismatch between high-In-content red-emitting InGaN and surrounding GaN. In addition, the anomalous temperature dependences of the PR peak are well elucidated by assuming that the red emission comes from quasi-QD structures with deep localized states. The deep localization suppresses efficiently the escape of carriers and then enhances the emission in the red, leading to high internal quantum efficiency (IQE) of 24.03%.
  • Robust and Scalable Flat-Optics on Flexible Substrates via Evolutionary Neural Networks

    Makarenko, Maksim; Wang, Qizhou; Burguete-Lopez, A.; Getman, Fedor; Fratalocchi, Andrea (Advanced Intelligent Systems, Wiley, 2021-08-31) [Article]
    In the past 20 years, flat-optics has emerged as a promising light manipulation technology, surpassing bulk optics in performance, versatility, and miniaturization capabilities. As of today, however, this technology is yet to find widespread commercial applications. One of the challenges is obtaining scalable and highly efficient designs that can withstand the fabrication errors associated with nanoscale manufacturing techniques. This problem becomes more severe in flexible structures, in which deformations appear naturally when flat-optics structures are conformally applied to, for example, biocompatible substrates. Herein, an inverse design platform that enables the fast design of flexible flat-optics that maintain high performance under deformations of their original geometry is presented. The platform leverages on suitably designed evolutionary large-scale optimizers, equipped with fast-trained neural network predictors based on encoder decoder architectures. This approach supports the implementation of flexible flat-optics robust to both fabrication errors or user-defined perturbation stress. This method is validated by a series of experiments in which broadband flexible light polarizers, which maintain an average polarization efficiency of 80% over 200 nm bandwidths when measured under large mechanical deformations, are realized. These results could be helpful for the realization of a robust class of flexible flat-optics for biosensing, imaging, and biomedical devices.
  • Flow-Guided Video Inpainting with Scene Templates

    Alzahrani, Majed A.; Zhu, Peihao; Wonka, Peter; Sundaramoorthi, Ganesh (arXiv, 2021-08-29) [Preprint]
    We consider the problem of filling in missing spatio-temporal regions of a video. We provide a novel flow-based solution by introducing a generative model of images in relation to the scene (without missing regions) and mappings from the scene to images. We use the model to jointly infer the scene template, a 2D representation of the scene, and the mappings. This ensures consistency of the frame-to-frame flows generated to the underlying scene, reducing geometric distortions in flow based inpainting. The template is mapped to the missing regions in the video by a new L2-L1 interpolation scheme, creating crisp inpaintings and reducing common blur and distortion artifacts. We show on two benchmark datasets that our approach out-performs state-of-the-art quantitatively and in user studies.
  • The criteria in above-bandgap photo-irradiation in molecular beam epitaxy growth of heterostructure of dissimilar growth temperature

    Park, Kwangwook; Min, Jung-Wook; Park, Gyeong Cheol; Lopatin, Sergei; Ooi, Boon S.; Alberi, Kirstin (Applied Surface Science, Elsevier BV, 2021-08-25) [Article]
    Above-bandgap photo-irradiation is known to improve the low temperature growth of II-VI semiconductors, but the trade-offs in the substrate temperature and light source power density are not well known. We investigated these effects on the growth of ZnSe epilayers on GaAs. We find that the above-bandgap photo-irradiation can improve the ZnSe epilayer without substantially negatively impacting the underlying GaAs epilayer only if the laser energy is below a threshold intensity. When the threshold is exceeded, the growth rate drops, the optical properties of ZnSe layer deteriorate and interface intermixing is enhanced. Together, cross-sectional transmission electron microscopy, energy dispersive spectroscopy and photoluminescence results suggest that photo-irradiation at moderate to high laser energies produces a trade-off in interface intermixing and planar defect formation. Most importantly, the damage produced by high laser energies does not start at the interface but instead in the bulk. Further flexibility for selecting the temperature and photo-irradiation intensities could be realized by turning on the laser irradiation after the ZnSe growth has been initiated, limiting the potential intermixing at the interface.
  • Improved performance of InGaN-based red light-emitting diodes by micro-hole arrays

    Zhuang, Zhe; Iida, Daisuke; Kirilenko, Pavel; Ohkawa, Kazuhiro (Optics Express, The Optical Society, 2021-08-24) [Article]
    This study demonstrates the performance improvements of InGaN-based red lightemitting diodes (LEDs) by fabricating micro-holes in the planar mesa. The peak wavelengths of the micro-hole LEDs (MHLEDs) exhibited a blue-shift of around 3 nm compared to the planar LEDs (PLEDs) at the same current density. The lowest full width at half maximum of MHLEDs was 59 nm, which is slightly less than that of the PLEDs. The light output power and external quantum efficiency of the MHLED with a wavelength of 634 nm at 20 mA were 0.6 mW and 1.5%, which are 8.5% higher than those of the PLED.
  • 630-nm red InGaN micro-light-emitting diodes (<20 μm × 20 μm) exceeding 1 mW/mm2 for full-color micro-displays

    Zhuang, Zhe; Iida, Daisuke; Velazquez-Rizo, Martin; Ohkawa, Kazuhiro (Photonics Research, The Optical Society, 2021-08-23) [Article]
    We demonstrated 10 × 10 arrays of InGaN 17 μm × 17 μm micro-light-emitting diodes (μLEDs) with a peak wavelength from 662 to 630 nm at 10–50 A∕cm2. The on-wafer external quantum efficiency reached 0.18% at 50 A∕cm2. The output power density of the red μLEDs was obtained as 1.76 mW∕mm2, which was estimated to be higher than that of 20 μm × 20 μm AlInGaP red μLEDs (∼630 nm). Finally, we demonstrate that InGaN red/green/blue μLEDs could exhibit a wide color gamut covering 81.3% and 79.1% of the Rec. 2020 color space in CIE 1931 and 1976 diagrams, respectively.
  • Model predictive control paradigms for fish growth reference tracking in precision aquaculture

    Chahid, Abderrazak; Ndoye, Ibrahima; Majoris, John E.; Berumen, Michael L.; Laleg-Kirati, Taous-Meriem (Journal of Process Control, Elsevier BV, 2021-08-12) [Article]
    In precision aquaculture, the primary goal is to maximize biomass production while minimizing production costs. This objective can be achieved by optimizing factors that have a strong influence on fish growth, such as feeding rate, temperature, and dissolved oxygen. This paper provides a comparative study of four model predictive control (MPC) strategies for fish growth reference tracking under a representative bioenergetic growth model in precision aquaculture. We propose to evaluate four candidate MPC formulations for fish growth reference tracking based on the receding horizon. The first MPC formulation tracks a desired fish growth trajectory while penalizing the feed ration, temperature, and dissolved oxygen. The second MPC optimization strategy directly optimizes the feed conversion ratio (FCR), which is the ratio between food quantity and fish weight gain at each sampling time. This FCR-like optimization strategy minimizes the feed while maximizing the predicted growth state's deviation from the given reference growth trajectory. The third MPC formulation includes a tradeoff between the growth rate trajectory tracking, the dynamic energy, and food cost. The last MPC formulation aims to maximize the fish growth rate while minimizing the costs. Numerical simulations that integrate a realistic bioenergetic fish growth model of Nile tilapia (Oreochromis niloticus) are illustrated to examine the comparative performance of the four proposed optimal control strategies. A sensitivity analysis is conducted to study the robustness of these MPC strategies with respect to the effect of the prediction horizon, the regularization term, and the additive input disturbances to the process. The obtained results show great potential and flexibility to meet the fish farmers’ needs depending on the type of fish, selling price, culture duration, and feed cost.
  • Lattice-matched III-nitride structures comprising BAlN, BGaN, and AlGaN for ultraviolet applications

    AlQatari, Feras S.; Sajjad, Muhammad; Lin, Ronghui; Li, Kuanghui; Schwingenschlögl, Udo; Li, Xiaohang (Materials Research Express, IOP Publishing, 2021-08-11) [Article]
    The optical properties of BAlN, BGaN and AlGaN ternary alloys are investigated using hybrid density functional for the design of lattice-matched optical structures in the ultraviolet spectrum. The calculated AlGaN properties agree well with previous reports, validating the model. A peculiar non-monotonic behavior of the refractive index as a function of the boron composition is found. The results of this calculation are interpolated to generate a three-dimensional dataset, which can be employed for designing a countless number of lattice-matched and –mismatched heterostructures. These heterostructures could span a range of operating wavelength well into the deep ultraviolet with refractive indices ranging from 1.98 to 2.41 for AlN at 0 eV and GaN near the GaN bandgap, respectively. An example is shown where a lattice-matched heterostructure, AlN/B0.108Ga0.892N, is applied for DBR applications with a large index difference. A DBR comprising the AlN/B0.108Ga0.892N heterostructure at the UV wavelength of 375 nm is found to exceed 93% peak reflectivity with only 10 pairs and reaches 100% reflectivity with 35 pairs. For a chosen design with 25 pairs, the DBR has a peak reflectivity of 99.8% and a bandwidth of 26 nm fulfilling the requirements of most devices especially ultraviolet vertical-cavity surface emitting lasers.
  • On the Spurious Interior Resonance Modes of Time Domain Integral Equations for Analyzing Acoustic Scattering from Penetrable Objects

    Chen, Rui; Shi, Yifei; Sayed, Sadeed Bin; Lu, Mingyu; Bagci, Hakan (arXiv, 2021-08-10) [Preprint]
    The interior resonance problem of time domain integral equations (TDIEs) formulated to analyze acoustic field interactions on penetrable objects is investigated. Two types of TDIEs are considered: The first equation, which is termed the time domain potential integral equation (TDPIE) (in unknowns velocity potential and its normal derivative), suffers from the interior resonance problem, i.e., its solution is replete with spurious modes that are excited at the resonance frequencies of the acoustic cavity in the shape of the scatterer. Numerical experiments demonstrate that, unlike the frequency-domain integral equations, the amplitude of these modes in the time domain could be suppressed to a level that does not significantly affect the solution. The second equation is obtained by linearly combining TDPIE with its normal derivative. Weights of the combination are carefully selected to enable the numerical computation of the singular integrals. The solution of this equation, which is termed the time domain combined potential integral equation (TDCPIE), does not involve any spurious interior resonance modes.

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