Absolute Reconstruction Research Articles

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex.

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Combined AMS 14C and archaeological dating of the Rouran period cemetery of Choburak-I (Northern Altai)

AbstractThis paper presents a detailed chronological study of the previously undisturbed burial ground of Choburak-I of the Bulan-Koby Culture in the Northern Altai using a program of comprehensive dating, including AMS 14C dating of human and animal remains (26 14C dates from 12 kurgans in total), and archaeological dating of the associated artifacts. This completely excavated cemetery contained numerous grave goods and various organic remains (anthropological and archaeozoological) critical for understanding the social and chronological dynamics of this culture during the Rouran period in Altai (second half of the 4th–first half of the 6th century CE). The results of archaeological dating, supported by the largest set of AMS 14C dates for the Bulan-Koby Culture, and further aided by Bayesian analysis, demonstrate the likely continuous existence of the necropolis within the period of 310–400 cal CE, which broadly corresponds to the beginning of the Rouran period in the history of Altai, with a maximum duration of 66 years. The presented results make it possible to consider the necropolis of Choburak-I as a chronologically defining monument of the Rouran period of Northern Altai and permit a new level of relative and absolute chronological reconstructions for archaeological sites of this region and adjacent territories at the turn of late antiquity and the early Middle Ages.

MFO–RIMS Tandem Workshop: Arithmetic Homotopy and Galois Theory

This report presents a general panorama of recent progress in the arithmetic-geometry theory of Galois and homotopy groups and its ramifications. While still relying on Grothendieck’s original pillars [1], the present program has now evolved beyond the classical group-theoretic legacy to result in an autonomous project that exploits a new geometrization of the original insight and sketches new frontiers between homotopy geometry, homology geometry, and diophantine geometry . This panorama “closes the loop” by including the last twenty-year progress of the Japanese arithmetic-geometry school via Ihara’s program and Nakamura–Tamagawa–Mochizuki’s anabelian approach, which brings its expertise in terms of algorithmic, combinatoric, and absolute reconstructions. These methods supplement and interact with those from the classical arithmetic of covers and Hurwitz spaces and the motivic and geometric Galois representations .This workshop has brought together the next generation of arithmetic homotopic Galois geometers, who, with the support of senior experts, are developing new techniques and principles for the exploration of the next research frontiers. [1] That are, as presented in “Récoltes et semailles”, the resolution of the discrete and the continuous, the local-to-global thinking by generization-specialization, and the quintessential intersection of arithmetic and geometry – see § 2.10 ibid.

Absolute electron density fluctuation reconstruction for two-dimensional hydrogen beam emission spectroscopy.

Scrape-off layer (SOL) and edge plasma turbulence significantly contribute to the radial particle and heat transport, lowering the plasma confinement and increasing the heat load on the plasma facing components. SOL turbulence is predominantly intermittent, which manifests in the occurrence of isolated density filaments or blobs. Filaments propagate radially outward toward plasma facing components, limiting their lifetime by erosion and sputtering. To characterize this phenomenon in detail, few diagnostic techniques are available. Beam emission spectroscopy is a diagnostic capable of measuring plasma turbulence in both SOL and edge plasmas. Due to the finite lifetime of the excitation states during the beam-plasma interaction and the misalignment between the optics and the magnetic field, spatial smearing is introduced in the measurement. In this paper, a novel method is introduced to overcome this hindering effect by inverting the fluctuation response matrix on an optimally smoothed signal. We show that this method is fast and provides significantly more accurate absolute density fluctuation reconstruction than the direct inversion technique. The presented method is usable for all types of beam emission diagnostics where the spatial resolution is higher than the combined smearing of the atomic physics and the observation.

Breast Reconstruction Complications After Postmastectomy Proton Radiation Therapy for Breast Cancer

Our purpose was to report complications requiring surgical intervention among patients treated with postmastectomy proton radiation therapy (PMPRT) for breast cancer in the setting of breast reconstruction (BR). Patients enrolled on a prospective proton registry who underwent BR with immediate autologous flap, tissue expander (TE), or implant in place during PMPRT (50/50.4 Gy +/- chest wall boost) were eligible. Major reconstruction complication (MRC) was defined as a complication requiring surgical intervention. Absolute reconstruction failure was an MRC requiring surgical removal of BR. A routine revision (RR) was a plastic surgery refining cosmesis of the BR. Kaplan-Meier method was used to assess disease outcomes and MRC. Cox regression was used to assess predictors of MRC. Seventy-three courses of PMPRT were delivered to 68 women with BR between 2013 and 2021. Median follow-up was 42.1 months. Median age was 47 years. Fifty-six (76.7%) courses used pencil beam scanning PMPRT. Of 73 BR, 29 were flaps (39.7%), 30 implants (41.1%), and 14 TE (19.2%) at time of irradiation. There were 20 (27.4%) RR. There were 9 (12.3%) MRC among 5 implants, 2 flaps, and 2 TE, occurring a median of 29 months from PMPRT start. Three-year freedom from MRC was 86.9%. Three (4.1%) of the MRC were absolute reconstruction failure. Complications leading to MRC included capsular contracture in 5, fat necrosis in 2, and infection in 2. On univariable analysis, BR type, boost, proton technique, age, and smoking status were not associated with MRC, whereas higher body mass index trended toward significance (hazard ratio, 1.07; 95% CI, 0.99-1.16; P = .10). Patients undergoing PMPRT to BR had a 12.3% incidence of major complications leading to surgical intervention, and total loss of BR was rare. MRC rates were similar among reconstruction types. Minor surgery for RR is common in our practice.

Absolute reconstruction for sums of powers of linear forms: degree 3 and beyond

Absolute reconstruction for sums of powers of linear forms: degree 3 and beyond

Monocular polarized three-dimensional absolute depth reconstruction technology for multi-target scenes.

The traditional polarization three-dimensional (3D) imaging technology has limited applications in the field of vision because it can only obtain the relative depth information of the target. Based on the principle of polarization stereo vision, this study combines camera calibration with a monocular ranging model to achieve high-precision recovery of the target's absolute depth information in multi-target scenes. Meanwhile, an adaptive camera intrinsic matrix prediction method is proposed to overcome changes in the camera intrinsic matrix caused by focusing on fuzzy targets outside the depth of field in multi-target scenes, thereby realizing monocular polarized 3D absolute depth reconstruction under dynamic focusing of targets at different depths. Experimental results indicate that the recovery error of monocular polarized 3D absolute depth information for the clear target is less than 10%, and the detail error is only 0.19mm. Also, the precision of absolute depth reconstruction remains above 90% after dynamic focusing on the blurred target. The proposed monocular polarized 3D absolute depth reconstruction technology for multi-target scenes can broaden application scenarios of the polarization 3D imaging technology in the field of vision.

Temporal-spatial binary encoding method based on dynamic threshold optimization for 3D shape measurement.

The binary encoding method has been widely used for three-dimensional (3D) shape measurement due to the high-speed projection characteristics of its digital mirror device (DMD)-based projector. However, traditional binary encoding methods require a larger defocus to achieve a good sinusoidality, leading to a reduction in the measurement depth of field and signal-to-noise ratio (SNR) of captured images, which can adversely affect the accuracy of phase extraction, particularly high-frequency fringes for 3D reconstruction. This paper proposes a spatial-temporal binary encoding method based on dynamic threshold optimization for 3D shape measurement. The proposed method decomposes an 8-bit sinusoidal fringe pattern into multiple(K) binary patterns, which can be outlined into two steps: determining the dynamic threshold and then performing temporal-spatial error diffusion encoding. By using an integral imaging strategy, approximate sinusoidal patterns can be obtained under nearly focused projection, which can then be subjected to absolute phase unwrapping and 3D reconstruction. The experiments show that compared to the three comparative algorithms under the same experimental conditions, this proposed method improves the reconstruction error of measuring a plane and an object by at least 13.66% and 12.57% when K=2. The dynamic experimental result on the palm confirms that the proposed method can reliably reconstruct the 3D shape of the moving object.

Method of unwrapped phase error correction in phase-measuring profilometry based on adaptive local window

Phase measurement profilometry (PMP) is widely used to reconstruct three-dimensional (3D) shape of objects. However, errors are introduced during standard phase unwrapping process, which considerably brings down measurement accuracy and lowers method reliability. This paper proposes a novel method to identify the errors, and subsequently correct these errors through erecting adaptive local windows. Under proposal, two robust algorithms are developed to correct these errors in initial rudimentary correction as well as in subsequent refined error removal. The adaptive local window is designed as such that the fringe order corresponding to each period of the wrapping phase is independent of each other. Therefore, it avoids the error transmission in the correction process. Experiments are carried out on absolute phase error correction and 3D point cloud reconstruction. Experimental results verified the developed method in terms of reducing the stratification of 3D point cloud and improving the quality of 3D measurement.

A 3D shape measurement method for high-reflective surface based on dual-view multi-intensity projection

Phase shifting profilometry has been commonly used in three-dimensional shape measurement with the advantages of high-precision and non-contact. However, it is still challenging to measure high-reflective surface because image saturation will lead to absolute phase errors and reconstruction errors. In this paper, a dual-view multi-intensity projection method was proposed. Compared with the single-view method, the proposed method can reconstruct more points at each projection intensity especially for pixels around the specular angle and reduce the number of projections to reduce the time consumption. First, we established the dual-view structured light system consisting of two monocular systems that share the same projector. Subsequently, a dual-view saturated pixel judging method was proposed that enables the reconstruction results under two views to be combined without duplicate points. The multi-intensity projection method was adopted by reducing the input projection intensity step by step and reconstructing the remaining pixels around the specular angle. Finally, the reconstruction result can be obtained by stitching point clouds at each projection intensity. Experiments verified that the proposed method could improve the integrity of reconstructed point clouds and measurement efficiency.

In Situ Damage Monitoring of CFRPs by Electromagnetic Tomography With the Compatible Multitemplate Supervised Descent Method

In this paper, an in-situ damage detection framework for carbon fiber-reinforced plastics (CFRPs) is proposed. The methodology involves the design of an electromagnetic tomography (EMT) sensor and developing an absolute image reconstruction method for electromagnetic tomography. A linear EMT sensor is designed for in-situ structural health monitoring of CFRPs tensile load test specimens. A compatible multi-template supervised descent method (cmt-SDM) based on a normal mixture distribution model of pixels is proposed for EMT image reconstruction. The cmt-SDM was developed to overcome the ill-posed problem in absolute image reconstruction. Simulation and experimental results show that the method proposed in this paper can be employed to monitor and evaluate the local damage degree of CFRPs specimens under quasi-static tensile loading. In addition, when the local fiber-breaking ratio reaches 40%, the damage location can be accurately reflected by the imaging results. Compared with the traditional SDM method, the method proposed in this paper shows smaller estimation error and delivers more accurate reconstructed images.

Fast absolute 3D CGO-based electrical impedance tomography on experimental tank data

Objective. To present the first 3D CGO-based absolute EIT reconstructions from experimental tank data. Approach. CGO-based methods for absolute EIT imaging are compared to traditional TV regularized non-linear least squares reconstruction methods. Additional robustness testing is performed by considering incorrect modeling of domain shape. Main Results. The CGO-based methods are fast, and show strong robustness to incorrect domain modeling comparable to classic difference EIT imaging and fewer boundary artefacts than the TV regularized non-linear least squares reference reconstructions. Significance. This work is the first to demonstrate fully 3D CGO-based absolute EIT reconstruction on experimental data and also compares to TV-regularized absolute reconstruction. The speed (1–5 s) and quality of the reconstructions is encouraging for future work in absolute EIT.

High quality of an absolute phase reconstruction for coherent digital holography with an enhanced anti-speckle deep neural unwrapping network.

It is always a challenge how to overcome speckle noise interference in the phase reconstruction for coherent digital holography (CDH) and its application, as this issue has not been solved well so far. In this paper, we are proposing an enhanced anti-speckle deep neural unwrapping network (E-ASDNUN) approach to achieve high quality of absolute phase reconstruction for CDH. The method designs a special network-based noise filter and embeds it into a deep neural unwrapping network to enhance anti-noise capacity in the image feature recognition and extraction process. The numerical simulation and experimental test on the phase unwrapping reconstruction and the image quality evaluation under the noise circumstances show that the E-ASDNUN approach is very effective against the speckle noise in realizing the high quality of absolute phase reconstruction. Meanwhile, it also demonstrates much better robustness than the typical U-net neural network and the traditional phase unwrapping algorithms in reconstructing high wrapping densities and high noise levels of phase images. The E-ASDNUN approach is also examined and confirmed by measuring the same phase object using a commercial white light interferometry as a reference. The result is perfectly consistent with that obtained by the E-ASDNUN approach.

A novel improved data-temporal attention network reconstruction model-based method for fault diagnosis of chiller sensors

Accurate measurement of sensors is the basis for the reliable operation of complex systems. To improve the fault diagnosis performance of the air-cooled chiller sensor, an improved data-temporal attention network is proposed, and an improved data-temporal attention network–based method for sensor fault diagnosis is established. First, the method combines the “memory” capability of the bidirectional improved long short-term memory network and the feature extraction capability of the multi-scale convolutional neural network to fully explore the time correlation of the chiller sensor, the data correlation between the physical quantities, and the dynamic response characteristics of the physical quantities. In addition, data and time attention mechanisms are introduced into the encoder and decoder, respectively, and valuable information is enhanced through weight distribution to capture relevant features further. Second, relying on the advantages of the “end-to-end” network structure of the improved data-temporal attention network model, the fault sensor is directly located by comparing the absolute reconstruction error vector with the fault threshold vector. Finally, it is verified by the datasets collected from a real air-cooled chiller platform that the proposed method has achieved an excellent fault diagnosis effect. The ablation experiment confirms that each improvement step can effectively improve the reconstruction performance of the network, which enhances the sensitivity of fault sensor identification. Compared with existing mainstream methods, the state-of-the-art framework presented in this article reveals its superiority.

Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods

Abstract. Groundwater monitoring and specific collection of data on the spatiotemporal dynamics of the aquifer are prerequisites for effective groundwater management and determine nearly all downstream management decisions. An optimally designed groundwater monitoring network (GMN) will provide the maximum information content at the minimum cost (Pareto optimum). In this study, PySensors, a Python package containing scalable, data-driven algorithms for sparse sensor selection and signal reconstruction with dimensionality reduction is applied to an existing GMN in 1D (hydrographs) and 2D (gridded groundwater contour maps). The algorithm first fits a basis object to the training data and then applies a computationally efficient QR algorithm that ranks existing monitoring wells (for 1D) or suitable sites for additional monitoring (for 2D) in order of importance, based on the state reconstruction of this tailored basis. This procedure enables a network to be reduced or extended along the Pareto front. Moreover, we investigate the effect of basis choice on reconstruction performance by comparing three types typically used for sparse sensor selection (i.e., identity, random projection, and SVD, respectively, PCA). We define a gridded cost function for the extension case that penalizes unsuitable locations. Our results show that the proposed approach performs better than the best randomly selected wells. The optimized reduction makes it possible to adequately reconstruct the removed hydrographs with a highly reduced subset with low loss. With a GMN reduced by 94 %, an average absolute reconstruction accuracy of 0.1 m is achieved, in addition to 0.05 m with a reduction by 69 % and 0.01 m with 18 %.

StaSiS-Net: A stacked and siamese disparity estimation network for depth reconstruction in modern 3D laparoscopy.

Developing accurate and real-time algorithms for a non-invasive three-dimensional representation and reconstruction of internal patient structures is one of the main research fields in computer-assisted surgery and endoscopy. Mono and stereo endoscopic images of soft tissues are converted into a three-dimensional representation by the estimation of depth maps. However, automatic, detailed, accurate and robust depth map estimation is a challenging problem that, in the stereo setting, is strictly dependent on a robust estimate of the disparity map. Many traditional algorithms are often inefficient or not accurate. In this work, novel self-supervised stacked and Siamese encoder/decoder neural networks are proposed to compute accurate disparity maps for 3D laparoscopy depth estimation. These networks run in real-time on standard GPU-equipped desktop computers and the outputs may be used for depth map estimation using the a known camera calibration. We compare performance on three different public datasets and on a new challenging simulated dataset and our solutions outperform state-of-the-art mono and stereo depth estimation methods. Extensive robustness and sensitivity analyses on more than 30000 frames has been performed. This work leads to important improvements in mono and stereo real-time depth map estimation of soft tissues and organs with a very low average mean absolute disparity reconstruction error with respect to ground truth.

Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.

A 3D shape measurement method for high-reflective surface based on accurate adaptive fringe projection

Fringe projection profilometry (FPP) is a popular three-dimensional (3D) shape measurement technique with advantages of high-precision and non-contact. However, measuring high-reflective surface is still a challenging task for conventional FPP because image saturation leads to absolute phase errors and reconstruction errors. In this paper, a new adaptive fringe projection (AFP) method is proposed. Compared with existing AFP methods, we simplify the experimental process and get more accurate coordinate mapping. First, we built the projection intensity model for each projector pixel rather than camera pixel. Then, a uniform gray pattern was projected and two images were captured under high and low exposures respectively to calculate the quantitative low projection intensity. In coordinate mapping, sub-pixel coordinates were mapped from camera image onto projector image. They were filtered with absolute phase to deal with resolution difference between camera and projector and get accurate coordinate correspondence. Finally, adaptive fringe patterns were generated. Besides, we proposed a novel quantitative criterion to evaluate the effectiveness of AFP methods called point cloud integrality (PCI), which is calculated based on the number of 3D points of high-reflective areas. The experiments demonstrate that the proposed method increases both the measurement accuracy and PCI compared with conventional FPP and some existing AFP methods.

3D deformation measurement in digital holographic interferometry using a multitask deep learning architecture.

The extraction of absolute phase from an interference pattern is a key step for 3D deformation measurement in digital holographic interferometry (DHI) and is an ill-posed problem. Estimating the absolute unwrapped phase becomes even more challenging when the obtained wrapped phase from the interference pattern is noisy. In this paper, we propose a novel multitask deep learning approach for phase reconstruction and 3D deformation measurement in DHI, referred to as TriNet, that has the capability to learn and perform two parallel tasks from the input image. The proposed TriNet has a pyramidal encoder-two-decoder framework for multi-scale information fusion. To our knowledge, TriNet is the first multitask approach to accomplish simultaneous denoising and phase unwrapping of the wrapped phase from the interference fringes in a single step for absolute phase reconstruction. The proposed architecture is more elegant than recent multitask learning methods such as Y-Net and state-of-the-art segmentation approaches such as UNet++. Further, performing denoising and phase unwrapping simultaneously enables deformation measurement from the highly noisy wrapped phase of DHI data. The simulations and experimental comparisons demonstrate the efficacy of the proposed approach in absolute phase reconstruction and 3D deformation measurement with respect to the existing conventional methods and state-of-the-art deep learning methods.

Constraining the Range and Variation of Lithospheric Net Rotation Using Geodynamic Modeling.

Lithospheric net rotation (LNR) is the movement of the lithosphere as a solid body with respect to the mantle. Separating the signal of LNR from plate tectonic motion is therefore an important factor in producing absolute plate motion models. Net rotation is difficult to constrain because of uncertainties in geological data and outstanding questions about the stability of the mantle plumes used as a reference frame. We use mantle convection simulations to investigate the controlling factors for the magnitude of LNR and to find the statistical predictability of LNR in a fully self‐consistent convective system. We find that high lateral viscosity variations are required to produce Earth‐like values of LNR. When the temperature dependence of viscosity is lower, and therefore slabs are softer, other factors such as the presence of continents and a viscosity gradient at the transition zone are also important for determining the magnitude of net rotation. We find that, as an emergent property of the chaotic mantle convection system, the evolution of LNR is too complicated to predict in our models. However, we find that the range of LNR within the simulations follows a Gaussian distribution, with a correlation time of 5 Myr. The LNR from the models needs to be sampled for around 50 Myr to produce a fully Gaussian distribution. This implies, that within the time frames considered for absolute plate motion reconstructions, LNR can be treated as a Gaussian variable. This provides a new geodynamic constraint for absolute plate motion reconstructions.