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Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain errors, ranging from clinical inaccuracies to linguistic mistakes. To address this, we introduce ReXErr, a methodology that leverages Large Language Models to generate representative errors within chest X-ray reports. Working with board-certified radiologists, we developed error categories that capture common mistakes in both human and AI-generated reports. Our approach uses a novel sampling scheme to inject diverse errors while maintaining clinical plausibility. ReXErr demonstrates consistency across error categories and produces errors that closely mimic those found in real-world scenarios. This method has the potential to aid in the development and evaluation of report correction algorithms, potentially enhancing the quality and reliability of radiology reporting.

Touchscreen-based interaction on display devices are ubiquitous nowadays. However, capacitive touch screens, the core technology that enables its widespread use, are prohibitively expensive to be used in large displays because the cost increases proportionally with the screen area. In this paper, we propose a millimeter wave (mmWave) radar-based solution to achieve subresolution error performance using a network of four mmWave radar sensors. Unfortunately, achieving this is non-trivial due to inherent range resolution limitations of mmWave radars, since the target (human hand, finger etc.) is 'distributed' in space. We overcome this using a deep learning-based approach, wherein we train a deep convolutional neural network (CNN) on range-FFT (range vs power profile)-based features against ground truth (GT) positions obtained using a capacitive touch screen. To emulate the clutter characteristics encountered in radar-based positioning of human fingers, we use a metallic finger mounted on a metallic robot arm as the target. Using this setup, we demonstrate subresolution position error performance. Compared to conventional signal processing (CSP)-based approaches, we achieve a 2-3x reduction in positioning error using the CNN. Furthermore, we observe that the inference time performance and CNN model size support real-time integration of our approach on general purpose processor-based computing platforms., Comment: 7 pages, 9 figures and 2 tables. To appear in the 100th Vehicular Technology Conference (VTC-Fall 2024)

We present the analysis of optical photometric and spectroscopic observations of two non-magnetic cataclysmic variables, namely CRTS J080846.2+313106 and V416 Dra. CRTS J080846.2+313106 has been found to vary with a period of 4.9116$\pm$0.0003 h, which was not found in earlier studies and is provisionally suggested as the orbital period of the system. In both long-period systems, the observed dominant signal at second harmonic of the orbital frequency and the orbital modulation during quiescence are suggestive of ellipsoidal variation from changing aspects of the secondary, with an additional contribution from the accretion stream or hotspot. However, during the outburst, the hotspot itself is overwhelmed by the increased brightness, which is possibly associated with the accretion disc. The mid-eclipse phase for V416 Dra occurs earlier and the width of the eclipse is greater during outbursts compared to quiescence, suggesting an increased accretion disc radius during outbursts. Furthermore, the investigation of accretion disc eclipse in V416 Dra implies that a total disc eclipse is possible during quiescence, whereas the disc seems to be partially obscured during outbursts, which further signifies that the disc may grow in size as the outburst progresses. Optical spectra of CRTS J080846.2+313106 and V416 Dra are typical of dwarf novae during quiescence, and they both show a significant contribution from the M2-4V secondary. The light curve patterns, orbital periods, and spectra observed in both systems look remarkably similar, and seem to resemble the characteristics of U Gem-type dwarf novae., Comment: 14 pages, 11 Figures, and 3 Tables, Accepted for publication in A&A

Transformer-based architectures have become the de-facto standard models for diverse vision tasks owing to their superior performance. As the size of the models continues to scale up, model distillation becomes extremely important in various real applications, particularly on devices limited by computational resources. However, prevailing knowledge distillation methods exhibit diminished efficacy when confronted with a large capacity gap between the teacher and the student, e.g, 10x compression rate. In this paper, we present a novel approach named Automatic Multi-step Distillation (AMD) for large-scale vision model compression. In particular, our distillation process unfolds across multiple steps. Initially, the teacher undergoes distillation to form an intermediate teacher-assistant model, which is subsequently distilled further to the student. An efficient and effective optimization framework is introduced to automatically identify the optimal teacher-assistant that leads to the maximal student performance. We conduct extensive experiments on multiple image classification datasets, including CIFAR-10, CIFAR-100, and ImageNet. The findings consistently reveal that our approach outperforms several established baselines, paving a path for future knowledge distillation methods on large-scale vision models., Comment: 19 pages, 5 figures

In this work, we introduce the Prototypical Transformer (ProtoFormer), a general and unified framework that approaches various motion tasks from a prototype perspective. ProtoFormer seamlessly integrates prototype learning with Transformer by thoughtfully considering motion dynamics, introducing two innovative designs. First, Cross-Attention Prototyping discovers prototypes based on signature motion patterns, providing transparency in understanding motion scenes. Second, Latent Synchronization guides feature representation learning via prototypes, effectively mitigating the problem of motion uncertainty. Empirical results demonstrate that our approach achieves competitive performance on popular motion tasks such as optical flow and scene depth. Furthermore, it exhibits generality across various downstream tasks, including object tracking and video stabilization., Comment: 21 pages, 10 figures

Defects in metal halide perovskites (MHP) are photosensitive, making the observer effect unavoidable when laser spectroscopy methods are applied. Photoluminescence (PL) bleaching and enhancement under light soaking and recovery in dark are examples of the transient phenomena that are consequent to the creation and healing of defects. Depending on the initial sample composition, environment, and other factors, the defect nature and evolution can strongly vary, making spectroscopic data analysis prone to misinterpretations. Herein, the use of an automatically acquired dependence of PL quantum yield (PLQY) on the laser pulse repetition rate and pulse fluence as a unique fingerprint of both charge carrier dynamics and defect evolution is demonstrated. A simple visual comparison of such fingerprints allows for assessment of similarities and differences between MHP samples. The study illustrates this by examining methylammonium lead triiodide (MAPbI3) films with altered stoichiometry that just after preparation showed very pronounced defect dynamics at time scale from milliseconds to seconds, clearly distorting the PLQY fingerprint. Upon weeks of storage, the sample fingerprints evolve toward the standard stoichiometric MAPbI3 in terms of both charge carrier dynamics and defect stability. Automatic PLQY mapping can be used as a universal method for assessment of perovskite sample quality.

Neuromorphic computing promises to transform the current paradigm of traditional computing towards Non-Von Neumann dynamic energy-efficient problem solving. Thus, dynamic memory devices capable of simultaneously performing nonlinear operations (volatile) similar to neurons and also storing information (non-volatile) alike brain synapses are in the great demand. To satisfy these demands, a neuromorphic platform has to possess intrinsic complexity reflected in the built-in diversity of its physical operation mechanisms. Herein, we propose and demonstrate the novel concept of a memlumor - an all-optical device combining memory and luminophore, and being mathematically a full equivalence of the electrically-driven memristor. By utilizing metal halide perovskites as a memlumor material platform, we demonstrate the synergetic coexistence of both volatile and non-volatile memory effects within a broad timescale from ns to days. We elucidate the origin of such complex response to be related to the phenomena of photodoping and photochemistry activated by a tunable light input and explore several possible realizations of memlumor computing. Leveraging on the existence of a history-dependent photoluminescent quantum yield in various material platforms, the memlumor device concept will trigger multiple new research directions in both material science and optoelectronics. We anticipate that the memlumor, as a new optical dynamic computing element, will add a new dimension to existing optical technologies enabling their transition into application in photonic neuromorphic computing.

Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans. The most accurate method, manual segmentation, is highly labor intensive, so automated tools like FreeSurfer have been adopted to handle this task. However, these traditional pipelines are slow and inefficient for processing large datasets. In this study, we propose TABSurfer, a novel 3D patch-based CNN-Transformer hybrid deep learning model designed for superior subcortical segmentation compared to existing state-of-the-art tools. To evaluate, we first demonstrate TABSurfer's consistent performance across various T1w MRI datasets with significantly shorter processing times compared to FreeSurfer. Then, we validate against manual segmentations, where TABSurfer outperforms FreeSurfer based on the manual ground truth. In each test, we also establish TABSurfer's advantage over a leading deep learning benchmark, FastSurferVINN. Together, these studies highlight TABSurfer's utility as a powerful tool for fully automated subcortical segmentation with high fidelity., Comment: 5 pages, 3 figures, 2 tables

With the emergence of Artificial Intelligent chatbot tools such as ChatGPT and code writing AI tools such as GitHub Copilot, educators need to question what and how we should teach our courses and curricula in the future. In reality, automated tools may result in certain academic fields being deeply reduced in the number of employable people. In this work, we make a case study of cybersecurity undergrad education by using the lens of ``Understanding by Design'' (UbD). First, we provide a broad understanding of learning objectives (LOs) in cybersecurity from a computer science perspective. Next, we dig a little deeper into a curriculum with an undergraduate emphasis on cybersecurity and examine the major courses and their LOs for our cybersecurity program at Miami University. With these details, we perform a thought experiment on how attainable the LOs are with the above-described tools, asking the key question ``what needs to be enduring concepts?'' learned in this process. If an LO becomes something that the existence of automation tools might be able to do, we then ask ``what level is attainable for the LO that is not a simple query to the tools?''. With this exercise, we hope to establish an example of how to prompt ChatGPT to accelerate students in their achievements of LOs given the existence of these new AI tools, and our goal is to push all of us to leverage and teach these tools as powerful allies in our quest to improve human existence and knowledge.

Hypersonic waveriders are special shapes with leading edges coincident with the body's shock wave, yielding high lift-to-drag ratios. The waverider geometry results from streamline tracing using the solutions of a basic flow field such as the wedge or the cone for specified shock and base curves. The base and shock curves can be independently prescribed in the osculating cone method enabling a larger design space. Generally, low values of the conical shock angle (9-15 degrees) are used. The lack of any method to limit the maximum cone angle for osculating cone waverider motivates this study. Mathematical expressions are derived for geometrical conditions that result in successful osculating cone waverider generation. A power law curve and a Bezier curve are analyzed. Closed-form expressions for the maximum cone shock angle are obtained for the power law curve. A numerical procedure to solve the same for the Bezier curve is developed. The results, for a typical Mach number of 6.0, evidently show that the maximum cone shock angle for successful waverider generation is significantly lower than the maximum angle for attached shock solutions. The limiting conditions developed will be essential in constraining the waverider sample space for automated multiobjective optimization routines.

We present the timing analysis of the intermediate polar TX Col in the X-ray band using the observations made by Chandra, Swift, and Suzaku during the years 2000, 2007, and 2009, respectively. The spin, orbital, and beat periods derived from these data are consistent with the earlier findings. We found that the spin modulation was dominant during the Chandra observation, whereas both orbital and beat modulations were dominant during the Swift and Suzaku observations. These findings and past X-ray observations indicate that TX Col is changing its accretion geometry from disc dominance to stream dominance and vice versa., Comment: Proceeding paper of the 3rd Belgo-Indian Network for Astronomy and Astrophysics (BINA) workshop, Accepted for publication in the Bulletin of Li\`{e}ge Royal Society of Sciences

We have carried detailed time-resolved timing analysis of an intermediate polar V709 Cas using the long-baseline, short-cadence optical photometric data from the Transiting Exoplanet Survey Satellite. We found an orbital period of 5.3341 $\pm$ 0.0004 hr, a spin period of 312.75 $\pm$ 0.02 sec, and a beat period of 317.93 $\pm$ 0.03 sec, which is similar to the earlier published results. From the continuous high cadence data, we found that V709 Cas is a disc overflow system with disc-fed dominance., Comment: 6 Pages, 2 figures, 1 Table, Proceeding paper of the 3rd Belgo-Indian Network for Astronomy and Astrophysics (BINA) workshop, Accepted for publication in the Bulletin of Li\`ege Royal Society of Sciences

We present the preliminary timing analysis of confirmed intermediate polar UU Col and possible intermediate polar Swift J0939.7-3224 in the optical band with the help of long-term, high-cadence continuous photometry from Transiting Exoplanet Survey Satellite (TESS). For UU Col, we revise previously reported orbital and spin periods as 3.464 $\pm$ 0.005 h and 863.74 $\pm$ 0.08 s, respectively. Using the second harmonic of the beat frequency, the beat period is estimated as $\sim$928 s. These findings indicate that UU Col is a disc-fed dominated disc-overflow accretor. For J0939, we establish the spin period as 2671.8 $\pm$ 0.8 s and refine the provisionally suggested orbital period as 8.49 $\pm$ 0.03 h. The absence of beat frequency in J0939 signifies that it might be a pure disc-fed accretor; however, an X-ray study of this source will help to understand its true nature., Comment: 9 pages, 3 figures, 2 tables. Proceeding paper of the 3$^{rd}$ Belgo-Indian Network for Astronomy and Astrophysics (BINA) workshop, Accepted for publication in the Bulletin of Li\`{e}ge Royal Society of Sciences

In this paper, we propose a new multilevel power series solution method for solving a large surface and volume electric field integral equation based H-Matrix. The proposed solution method converges in a fixed number of iterations and is solved at each level of the H-Matrix computation.The solution method avoids the computation of a full matrix, as it can be solved independently at each level, starting from the leaf level. Solution at each level can be used as the final solution, thus saving the matrix computation time for full H-Matrix. The paper shows that the leaf level matrix computation and solution with power series gives an accurate results as full H-Matrix iterative solver method. The method results in considerable time and memory savings compared to the H-Matrix iterative solver. Further, the proposed method retains the O(NlogN) solution complexity, Comment: 8 pages. The Applied Computational Electromagnetics Society Journal (ACES) 2023

We present analyses of an Intermediate Polar, IGR J15094-6649, based on the archival optical data obtained from the Transiting Exoplanet Survey Satellite (TESS) and X-ray data obtained from the Suzaku, NuSTAR, and Neil Gehrels Swift Observatory (Swift). Present analysis confirms and refines the previously reported spin period of IGR J15094-6649 as 809.49584$\pm$0.00075 s. Clear evidence of a beat period of 841.67376$\pm$0.00082 s is found during the long-term TESS optical observations, which was not evident in the earlier studies. The dominance of X-ray and optical spin pulse unveils the disc-fed dominance accretion, however, the presence of an additional beat frequency indicates that part of the accreting material also flows along the magnetic field lines. The energy-dependent spin pulsations in the low (< 10 keV) energy band are due to the photoelectric absorption in the accretion flow. However, the complex absorbers may be responsible to produce low amplitude spin modulations via Compton scattering in the hard ( > 10 keV) energy band and indicate that the height of the X-ray emitting region may be negligible. The observed double-humped X-ray profiles with a pronounced dip are indicative of the photoelectric absorption in the intervening accretion stream. Analysis of the X-ray spectra reveals the complexity of the X-ray emission, being composed of multi-temperature plasma components with a soft excess, reflection, and suffers from strong absorption., Comment: 15 pages, 10 figures, and 5 Tables, Accepted for publication in MNRAS. arXiv admin note: text overlap with arXiv:2112.06270

In this paper, an adaptive super-twisting controller is designed for an agile maneuvering quadrotor unmanned aerial vehicle to achieve accurate trajectory tracking in the presence of external disturbances. A cascaded control architecture is designed to determine the desired accelerations using the proposed controller and subsequently used to compute the desired orientation and angular rates. The finite-time convergence of sliding functions and closed-loop system stability are analytically proven. Furthermore, the restrictive assumption on the maximum variation of the disturbance is relaxed by designing a gain adaptation law and low-pass filtering of the estimated equivalent control. The proper selection of design parameters is discussed in detail. Finally, the effectiveness of the proposed method is evaluated by high-fidelity software-in-the-loop simulations and validated by experimental studies., Comment: We are working on a new version of this paper and revising some technical parts. We will replace the new version as soon as it is carefully revised

In this paper, we investigate requirements to prepare an application for Specific Operations Risk Assessment (SORA), regulated by European Union Aviation Safety Agency (EASA) to obtain flight authorization for Unmanned Aerial Vehicles (UAVs) operations and propose some perspectives to automate the approach based on our successful application. Preparation of SORA requires expert knowledge as it contains technicalities. Also, the whole process is an iterative and time-consuming one. It is even more challenging for higher-risk operations, such as those in urban environments, near airports, and multi- and customized models for research activities. SORA process limits the potential socio-economic impacts of innovative UAV capabilities. Therefore, in this paper, we present a SORA example, review the steps and highlight challenges. Accordingly, we propose an alternative workflow, considering the same steps, while addressing the challenges and pitfalls, to shorten the whole process. Furthermore, we present a comprehensive list of preliminary technical procedures, including the pre/during/post-flight checklists, design and installation appraisal, flight logbook, operational manual, training manual, and General Data Protection Regulation (GDPR), which are not explicitly instructed in SORA manual. Moreover, we propose the initial idea to create an automated SORA workflow to facilitate obtaining authorization, which is significantly helpful for operators, especially the scientific community, to conduct experimental operations.

This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to satisfy constraints. A novel sigmoid-type collision avoidance constraint is proposed to overcome the drawbacks of Lagrange polynomial approximation in pseudospectral methods that only guarantees inequality constraint satisfaction only at nodal points. Automatic differentiation of cost function and constraints is used to quickly determine their gradient and Jacobian, respectively. An APF method is used to update the optimal control inputs for guaranteeing collision avoidance. The trajectory optimization and APF method are implemented in a closed-loop fashion continuously, but in parallel at moderate and high frequencies, respectively. The initial guess for the optimization is provided based on the previous solution. The proposed approach is tested and validated through indoor experiments.

We present our analysis of new and archived observations of two candidate magnetic cataclysmic variables, namely 1RXS J174320.1-042953 and YY Sex. 1RXS J174320.1-042953 was observed in two distinctive high and low states where a phase shift was seen, which could be due to the changes in the shape, size, and (or) location of the accretion region. We find that its orbital X-ray modulations only persist in the soft (0.3-2.0 keV) energy band, which could be attributed to the photoelectric absorption in the accretion flow. The X-ray spectra exhibit a multi-temperature post-shock region where the hard X-rays are absorbed through a thick absorber with an equivalent hydrogen column of $\sim$7.5 $\times$ 10$^{23}$ cm$^{-2}$, which partially covers $\sim$56 per cent of the emission. No soft X-ray excess was found to be present; however, a soft X-ray emission with a blackbody temperature of $\sim$97 eV describes the spectra. Extensive TESS observations of YY Sex allow us to refine its orbital period to 1.5746 $\pm$ 0.0011 h. We did not find any signature of previously reported spin or beat periods in this system. Furthermore, our new polarimetric observations show clear circular polarization modulated on the orbital period only. Finally, both systems show strong Balmer and He II 4686 A$^\circ$ emission lines in the optical spectra, further indicative of their magnetic nature., Comment: 16 pages, 12 figures, and 7 tables (accepted for publication in MNRAS)

Localizing targets outside the anchors' convex hull is an understudied but prevalent scenario in vehicle-centric, UAV-based, and self-localization applications. Considering such scenarios, this paper studies the optimal anchor placement problem for Time-of-Arrival (ToA)-based localization schemes such that the worst-case Dilution of Precision (DOP) is minimized. Building on prior results on DOP scaling laws for beyond convex hull ToA-based localization, we propose a novel metric termed the Range-Normalized DOP (RNDOP). We show that the worst-case DOP-optimal anchor placement problem simplifies to a min-max RNDOP-optimal anchor placement problem. Unfortunately, this formulation results in a non-convex and intractable problem under realistic constraints. To overcome this, we propose iterative anchor addition schemes, which result in a tractable albeit non-convex problem. By exploiting the structure arising from the resultant rank-1 update, we devise three heuristic schemes with varying performance-complexity tradeoffs. In addition, we also derive the upper and lower bounds for scenarios where we are placing anchors to optimize the worst-case (a) 3D positioning error and (b) 2D positioning error. We build on these results to design a cohesive iterative algorithmic framework for robust anchor placement, characterize the impact of anchor position uncertainty, and then discuss the computational complexity of the proposed schemes. Using numerical results, we validate the accuracy of our theoretical results. We also present comprehensive Monte-Carlo simulation results to compare the positioning error and execution time performance of each iterative scheme, discuss the tradeoffs, and provide valuable system design insights for beyond convex hull localization scenarios., Comment: 16 pages. To appear in a future issue of the IEEE Transactions on Vehicular Technology

Streamline tracing in conical hypersonic flows is essential for designing high-performance waverider and intake. Conventionally, the streamline equations are solved after obtaining the velocity field from the solution of the axisymmetric conical flow field. The hypersonic waverider shape is generated from the base conical flow field by repeatedly applying the streamline tracing approach along several planes. When exploring the design space for optimization of the waverider, streamline tracing can be computationally expensive. We provide a novel strategy where first the Taylor-Maccoll equations for the inviscid axisymmetric conical flowfield and the streamlines from the shock are solved for a wide range of cone angle and Mach number conditions resulting in an extensive database. The streamlines are parametrized by a third-order polynomial, and an Artificial Neural Network (ANN) is trained to predict the coefficients of the polynomial for arbitrary inputs of Mach number, cone angle, and streamline originating location on the shock . We apply this strategy to design a cone derived waverider and compare the geometry obtained with the standard conical waverider design method and the simplified waverider design method. The ANN technique is highly accurate, with a difference of 0.68% with the standard in the coordinates of the waverider. RANS computations show that the ANN derived waverider does not indicate severe flow spillage at the leading edge, which is observed in the waverider generated from the simplified method. The new ANN-based approach is 20 times faster than the conventional method.

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI., Comment: 13 pages, 6 figures

Brain tissue segmentation has demonstrated great utility in quantifying MRI data through Voxel-Based Morphometry and highlighting subtle structural changes associated with various conditions within the brain. However, manual segmentation is highly labor-intensive, and automated approaches have struggled due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. Despite the recent success of deep convolutional neural networks (CNNs) for brain tissue segmentation, many such solutions do not generalize well to new datasets, which is critical for a reliable solution. Transformers have demonstrated success in natural image segmentation and have recently been applied to 3D medical image segmentation tasks due to their ability to capture long-distance relationships in the input where the local receptive fields of CNNs struggle. This study introduces a novel CNN-Transformer hybrid architecture designed for brain tissue segmentation. We validate our model's performance across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, time points, and neuropsychiatric conditions. In all situations, our model achieved the greatest generality and reliability. Out method is inherently robust and can serve as a valuable tool for brain-related T1w MRI studies. The code for the TABS network is available at: https://github.com/raovish6/TABS.

In this paper, a robust and effective preconditioner for the fast Method of Moments(MoM) based Hierarchal Electric Field Integral Equation(EFIE) solver is proposed using symmetric near-field Schur's complement method. In this preconditioner, near-field blocks are scaled to a diagonal block matrix and these near-field blocks are replaced with the scaled diagonal block matrix which reduces the near-field storage memory and the overall matrix vector product time. Scaled diagonal block matrix is further used as a preconditioner and due to the block diagonal form of the final preconditioner, no additional fill-ins are introduced in its inverse. The symmetric property of the near-field blocks is exploited to reduce the preconditioner setup time. Near linear complexity of preconditioner set up and solve times is achieved by near-field block ordering, using graph bandwidth reduction algorithms and compressing the fill-in blocks in preconditioner computation. Preconditioner set up time is reduced to half by using the symmetric property and near-field block ordering. It has been shown using a complexity analysis that the cost of preconditioner construction in terms of computation and memory is linear. Numerical experiments demonstrate an average of 1.5-2.3x speed-up in the iterative solution time over Null-Field based preconditioners.

This paper presents a new fast power series solution method to solve the Hierarchal Method of Moment(MoM) matrix for a large complex,perfectly electric conducting (PEC) 3D structures. The proposed power series solution converges in just two iterations which is faster than the conventional fast solver-based iterative solution. The method is purely algebraic in nature and, as such applicable to existing conventional methods. The method uses regular fast solver Hierarchal Matrix (H-Matrix) and can also be applied to Multilevel Fast Multipole Method Algorithm(MLFMA). In the proposed method, we use the scaling of the symmetric near-field matrix to develop a diagonally dominant overall matrix to enable a power series solution. Left and right block scaling coefficients are required for scaling near-field blocks to diagonal blocks using Schur's complement method. However,only the right-hand scaling coefficients are computed for symmetric near-field matrix leading to saving of computation time and memory. Due to symmetric property, the left side-block scaling coefficients are just the transpose of the right-scaling blocks. Next, the near-field blocks are replaced by scaled near-field diagonal blocks. Now the scaled near-field blocks in combination with far-field and scaling coefficients are subjected to power series solution terminating after only two terms. As all the operations are performed on the near-field blocks, the complexity of scaling coefficient computation is retained as O(N). The power series solution only involves the matrix-vector product of the far-field, scaling coefficients blocks, and inverse of scaled near-field blocks. Hence, the solution cost remains O(NlogN). Several numerical results are presented to validate the efficiency and robustness of the proposed numerical method.