Traditional Projection Research Articles

An efficient and high-quality scheme for cone-beam CT reconstruction from sparse-view dat

Computed tomography (CT) is capable of generating detailed cross-sectional images of the scanned objects non-destructively. So far, CT has become an increasingly vital tool for 3D modelling of cultural relics. Compressed sensing (CS)-based CT reconstruction algorithms, such as the algebraic reconstruction technique (ART) regularized by total variation (TV), enable accurate reconstructions from sparse-view data, which consequently reduces both scanning time and costs. However, the implementation of the ART-TV is considerably slow, particularly in cone-beam reconstruction. In this paper, we propose an efficient and high-quality scheme for cone-beam CT reconstruction based on the traditional ART-TV algorithm. Our scheme employs Joseph's projection method for the computation of the system matrix. By exploiting the geometric symmetry of the cone-beam rays, we are able to compute the weight coefficients of the system matrix for two symmetric rays simultaneously. We then employ multi-threading technology to speed up the reconstruction of ART, and utilize graphics processing units (GPUs) to accelerate the TV minimization. Experimental results demonstrate that, for a typical reconstruction of a 512 × 512 × 512 volume from 60 views of 512 × 512 projection images, our scheme achieves a speedup of 14 × compared to a single-threaded CPU implementation. Furthermore, high-quality reconstructions of ART-TV are obtained by using Joseph's projection compared with that using traditional Siddon's projection.

Single-frame two-stage fringe projection profilometry based on deep learning

For dynamic objects with surface discontinuities, traditional fringe projection profilometry struggles to obtain accurate three-dimensional information. To address this challenge, this paper presents a single-frame, dual-stage fringe projection profilometry technique that requires only one deformed fringe pattern and employs two neural networks. The first neural network predicts deformed fringe patterns at different frequencies, while the second neural network predicts the wrapped phase numerator and denominator for each frequency. By integrating a traditional multi-frequency phase unwrapping method with system calibration, a step-by-step 3D measurement process is achieved. Moreover, this paper introduces a convolutional neural network called DARU-Net, which is based on U-Net and demonstrates significant advantages over U-Net and its derivatives in deep learning tasks. The experimental results show that the proposed method can accurately predict the 3D information of objects with surface height discontinuities using only a single fringe pattern, thus expanding the application scenarios of 3D measurement.

Measuring the Effects of Mobile and Social Networking Technology on the Enhancement of English Language Skills: A Comparative Study

This study investigates the effectiveness of mobile learning and teaching technologies and social networking platforms in enhancing English language skills among students in senior secondary public schools in India. Given the rising interest in these educational tools, their impact on language skill development remains underexplored. Addressing this gap, the study employed an experimental design with 120 randomly selected participants divided into three groups. For 12 weeks, each group received English language instruction through different mediums: mobile learning technology (Google Classroom), a social media application (WeChat), and a traditional multimedia projection system. The findings indicate a significant improvement in the English language proficiency of students who used mobile learning technology, as opposed to those who engaged with social media tools or traditional methods. This highlights the potential of mobile learning technologies in effectively enhancing language learning outcomes in the educational context. Furthermore, the traditional multimedia methods employed were found to be less effective in fostering language proficiency when compared to the employment of Google Classroom and WeChat. Given the results of this study, it is proposed that future research endeavors investigate the potential impact of severe games on language learning outcomes. In addition, it is advised that future investigations focus on creating and integrating demanding games that aim to enhance students’ proficiency in the English language.

Research on CT System Image based on Back Projection Reconstruction Model

CT technology has been widely used in clinical medicine, industrial engineering and other fields in contemporary society. Since the traditional back projection reconstruction algorithm will introduce star artifacts, we decide to use a parallel beam filtered back projection reconstruction model based on Radon changes and R-L filters. Since the rotation center of the system does not coincide with the geometric center, the data will be missing when using Radon transformation, so we carry out "edging" processing on the square tray. After processing the data to remove the gain (where the gain coefficient is 2.0033) and removing the "edge" at the end of the filtering, we reconstructed the detected object and obtained the absorption rates of the ten points required by the problem as follows :0.0126, 2.2902, 5.9159, 0.0163, 0.0823, 3.1336, 6.0333, 0.0000, 7.7184, 0.0861.

Variety is the spice of life: nongenetic variation in life histories influences population growth and evolvability.

Individual vital rates, such as mortality and birth rates, are key determinants of lifetime reproductive success, and variability in these rates shapes population dynamics. Previous studies have found that this vital rate heterogeneity can influence demographic properties, including population growth rates. However, the explicit effects of the variation within and the covariance between vital rates that can also vary throughout the lifespan on population growth remain unknown. Here, we explore the analytical consequences of nongenetic heterogeneity on long-term population growth rates and rates of evolution by modifying traditional age-structured population projection matrices to incorporate variation among individual vital rates. The model allows vital rates to be permanent throughout life ("fixed condition") or to change over the lifespan ("dynamic condition"). We reduce the complexity associated with adding individual heterogeneity to age-structured models through a novel application of matrix collapsing ("phenotypic collapsing"), showing how to collapse in a manner that preserves the asymptotic and transient dynamics of the original matrix. The main conclusion is that nongenetic individual heterogeneity can strongly impact the long-term growth rate and rates of evolution. The magnitude and sign of this impact depend heavily on how the heterogeneity covaries across the lifespan of an organism. Our results emphasize that nongenetic variation cannot simply be viewed as random noise, but rather that it has consistent, predictable effects on fitness and evolvability.

Lighting Projection Design of Art Stage From Holographic Naked Eye 3D Perspective

In today's modern environment, as an important part of stage art and performance, many places need to use stage lighting. Therefore, the correct use of stage lighting design is particularly important, and it is necessary to study the design of stage lighting. However, the traditional stage light projection is designed by the designer according to the understanding of music and music matching light projection. This scheme is easily influenced by the designer's subjective factors, which lack a clear and perfect design scheme. In this context, from the perspective of holographic naked eye 3D, this paper designs from the stage layout, utilization rate of lighting, the analysis of music features and the distribution of different colors, and evaluates the merits and disadvantages of music and light shows. This evaluation method can provide a design basis for the light projection in the design process of art stage and provide a scientific basis for the evaluation method of performance scheme. It can greatly reduce the design and debugging time of the scheme and save the design cost of the lighting projection.

Robust Ultrafast Projection Pipeline for Structural and Angiography Imaging of Fourier-Domain Optical Coherence Tomography.

The current methods to generate projections for structural and angiography imaging of Fourier-Domain optical coherence tomography (FD-OCT) are significantly slow for prediagnosis improvement, prognosis, real-time surgery guidance, treatments, and lesion boundary definition. This study introduced a robust ultrafast projection pipeline (RUPP) and aimed to develop and evaluate the efficacy of RUPP. RUPP processes raw interference signals to generate structural projections without the need for Fourier Transform. Various angiography reconstruction algorithms were utilized for efficient projections. Traditional methods were compared to RUPP using PSNR, SSIM, and processing time as evaluation metrics. The study used 22 datasets (hand skin: 9; labial mucosa: 13) from 8 volunteers, acquired with a swept-source optical coherence tomography system. RUPP significantly outperformed traditional methods in processing time, requiring only 0.040 s for structural projections, which is 27 times faster than traditional summation projections. For angiography projections, the best RUPP variation took 0.15 s, making it 7518 times faster than the windowed eigen decomposition method. However, PSNR decreased by 41-45% and SSIM saw reductions of 25-74%. RUPP demonstrated remarkable speed improvements over traditional methods, indicating its potential for real-time structural and angiography projections in FD-OCT, thereby enhancing clinical prediagnosis, prognosis, surgery guidance, and treatment efficacy.

Visualization enhancement by PCA-based image fusion for skin burns assessment in polarization-sensitive OCT.

Polarization-sensitive optical coherence tomography (PS-OCT) is a functional imaging tool for measuring tissue birefringence characteristics. It has been proposed as a potentially non-invasive technique for evaluating skin burns. However, the PS-OCT modality usually suffers from high system complexity and relatively low tissue-specific contrast, which makes assessing the extent of burns in skin tissue difficult. In this study, we employ an all-fiber-based PS-OCT system with single-state input, which is simple and efficient for skin burn assessment. Multiple parameters, such as phase retardation (PR), degree of polarization uniformity (DOPU), and optical axis orientation, are obtained to extract birefringent features, which are sensitive to subtle changes in structural arrangement and tissue composition. Experiments on ex vivo porcine skins burned at different temperatures were conducted for skin burn investigation. The burned depths estimated by PR and DOPU increase linearly with the burn temperature to a certain extent, which is helpful in classifying skin burn degrees. We also propose an algorithm of image fusion based on principal component analysis (PCA) to enhance tissue contrast for the multi-parameter data of PS-OCT imaging. The results show that the enhanced images generated by the PCA-based image fusion method have higher tissue contrast, compared to the en-face polarization images by traditional mean value projection. The proposed approaches in this study make it possible to assess skin burn severity and distinguish between burned and normal tissues.

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

This paper shows solicitude for the generalized projective synchronization of Caputo fractional-order uncertain memristive neural networks (FOUMNNs) with multiple delays. By extending the constant scale factor to the time-varying function matrix, we establish an extraordinary synchronization mode called time-varying function matrix projection synchronization (TFMPS), which is a generalized version of traditional matrix projection synchronization, modified projection synchronization, complete synchronization, and anti-synchronization. To achieve the goal of TFMPS, we design a novel mixed controller including the open loop feedback control and impulsive control, which employs the state information in the time-delayed interval and the sampling information at the impulse instants. It has a prominent advantage that impulse intervals are not restricted by time delays. To establish the connection between the error system and the auxiliary system, a generalized fractional-order comparison theorem with time-varying coefficients and impulses is established. Applying the stability theory, the comparison theorem, and the Laplace transform, new synchronization criteria of FOUMNNs are acquired under the mixed impulsive control schemes, and the derived synchronization theorem and corollary can effectively expand the correlative synchronization achievements of fractional-order systems.

Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction

The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections for electric utilities and policymakers, leading to the adoption of machine learning-based modelling tools due to the limitations of traditional power usage projection approaches. However, successful modeling of power usage in neuro-fuzzy models depends on the optimal selection of hyper-parameters. Consequently, this research looked at the major impact clustering methods and hyper-parameter modifications on a particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS) model. The study examined two distinct clustering methods and other key hyperparameters such as the number of clusters and cluster radius, resulting in a total of 10 sub-models. The performance of the developed models was assessed using four widely recognized performance indicators: root mean square error, mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of variation of the root mean square error (CVRMSE). Additionally, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models based on three different PSO variants. The results revealed that the combination of the ANFIS approach and PSO, specifically with two clusters, yielded the most accurate forecasting scheme with the optimal values for MAPE (7.7778%), MAE (712.6094), CVRMSE (9.5464), and RMSE (909.4998).

Design and application of data security privacy protection technology in dance action sequence arrangement and display system

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.

Spatiotemporal extended homogeneous field correction method for reducing complex interference in OPM-MEG

Novel magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have undergone rapid development in recent years. However, environmental interference significantly degrades data quality. When the number of sensors in the OPM-MEG system is small, the traditional subspace projection denoising algorithms reliant on sensor space oversampling will be difficult to apply. Although the recently proposed homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model, it lacks the ability to suppress complex environmental interference such as nonhomogeneous fields. Therefore, this paper proposes a novel OPM-MEG environmental interference suppression method based on HFC. We first use a projection operator constructed from a sensor orientation matrix to project original data and empty-room noise data onto the null space of the homogeneous field; this enables dimensionality reduction to eliminate homogeneous field interference. The remaining interference is then suppressed through subspace projection in the space and time domains. We compare our method to four benchmark algorithms based on simulations and somatosensory-evoked experiments. The experimental results demonstrate that the proposed method has better interference suppression performance than the benchmark algorithms. Therefore, our method can provide high signal-to-noise ratio data for subsequent clinical applications and brain scientific research.

Phase-aided online self-correction method for high-accuracy three-dimensional measurement.

The binocular structured light 3D measurement system is widely used in situ industrial inspection and shape measurement, where the system structure is generally unstable due to mechanical loosening or environmental disturbance. Timely corrections to the changing structural parameters thus is an essential task for online high-accuracy measurement, which is difficult for traditional unidirectional fringe projection methods to self-correct the structural change. To this end, we propose an online self-correction method based on the investigation that orthogonal fringe projection can intrinsically relax the constraint on the epipolar geometry relationship and provide bidirectional phases for accurate corresponding point searching. Since orthogonal fringe projection may sacrifice the measurement efficiency, we further design a searching strategy by locally unwrapping one directional phase to reduce the number of projection patterns. Experimental results demonstrate that the proposed method is effective for online self-correction of unstable system structure to achieve high-accuracy 3D measurement under complex measurement environments.

Bi-Constraints Diffusion: A Conditional Diffusion Model with Degradation Guidance for Metal Artifact Reduction.

In recent years, score-based diffusion models have emerged as effective tools for estimating score functions from empirical data distributions, particularly in integrating implicit priors with inverse problems like CT reconstruction. However, score-based diffusion models are rarely explored in challenging tasks such as metal artifact reduction (MAR). In this paper, we introduce the BiConstraints Diffusion Model for Metal Artifact Reduction (BCDMAR), an innovative approach that enhances iterative reconstruction with a conditional diffusion model for MAR. This method employs a metal artifact degradation operator in place of the traditional metal-excluded projection operator in the data-fidelity term, thereby preserving structure details around metal regions. However, scorebased diffusion models tend to be susceptible to grayscale shifts and unreliable structures, making it challenging to reach an optimal solution. To address this, we utilize a precorrected image as a prior constraint, guiding the generation of the score-based diffusion model. By iteratively applying the score-based diffusion model and the data-fidelity step in each sampling iteration, BCDMAR effectively maintains reliable tissue representation around metal regions and produces highly consistent structures in non-metal regions. Through extensive experiments focused on metal artifact reduction tasks, BCDMAR demonstrates superior performance over other state-of-the-art unsupervised and supervised methods, both quantitatively and in terms of visual results.

Statistical Analysis of the Surface Roughness on Aircraft Icing

A statistical analysis of the surface roughness is performed on experimentally obtained ice shapes on an asymmetrical airfoil at Rec≈2⋅106. The ice shapes were generated in the Icing Wind Tunnel of the Technical University of Braunschweig under Appendix C and O conditions of the EASA airplane certification standards as part of the ICE GENESIS project. The photogrammetry method is used for the digitization of the experimental ice shapes, while statistical parameters such as the mean ice shape and the local root mean square (RMS) of the ice geometry are extracted using a traditional surface projection method, as well as a self-organizing maps approach. Results show the evolution of the statistical parameters over time and the influence of the freestream static temperature on these parameters. A comparison between the experimental values of the local RMS of the ice geometry and a correlation for roughness prediction is presented, showing a good match with the original formulation of the correlation for cases under Appendix C conditions while having a good match with Appendix O conditions when a temperature correction factor is applied to the formulation. Additionally, results show an almost linear growth of roughness over the whole accretion time.

Levelwise construction of a single cylindrical algebraic cell

Satisfiability modulo theories (SMT) solvers check the satisfiability of quantifier-free first-order logic formulae over different theories. We consider the theory of non-linear real arithmetic where the formulae are logical combinations of polynomial constraints. Here a commonly used tool is the cylindrical algebraic decomposition (CAD) to decompose the real space into cells where the constraints are truth-invariant through the use of projection polynomials.A CAD encodes more information than necessary for checking satisfiability. One approach to address this is to repackage the CAD theory into a search-based algorithm: one that guesses sample points to satisfy the formula, and generalizes guesses that conflict constraints to cylindrical cells around samples which are avoided in the continuing search. This can lead to a satisfying assignment more quickly, or conclude unsatisfiability with far fewer cells. A notable example of this approach is Jovanović and de Moura's NLSAT algorithm. Since these cells are being produced locally to a sample there is scope to use fewer projection polynomials than the traditional CAD projection. The original NLSAT algorithm reduced the set a little; while Brown's single cell construction reduced it much further still. However, it refines a cell polynomial-by-polynomial, meaning the shape and size of the cell produced depends on the order in which the polynomials are considered.The present paper proposes a method to construct such cells levelwise, i.e. built level-by-level according to a variable ordering instead of polynomial-by-polynomial for all levels. We still use a reduced number of projection polynomials, but can now consider a variety of different reductions and use heuristics to select the projection polynomials in order to optimize the shape of the cell under construction. The new method can thus improve the performance of the NLSAT algorithm. We formulate all the necessary theory that underpins the algorithm as a proof system: while not a common presentation for work in this field, it is valuable in allowing an elegant decoupling of heuristic decisions from the main algorithm and its proof of correctness. We expect the symbolic computation community may find uses for it in other areas too. In particular, the proof system could be a step towards formal proofs for non-linear real arithmetic.This work has been implemented in the SMT-RAT solver and the benefits of the levelwise construction are validated experimentally on the SMT-LIB benchmark library. We also compare several heuristics for the construction and observe that each heuristic has strengths offering potential for further exploitation of the new approach.

Pupils’ perceived immersion, attitudes, and learning effectiveness in virtual field trips: A comparison between immersive and projective environments

Focusing on the implementation of virtual field trips in elementary school classroom, this study aimed to explore pupils’ affective perceptions and cognitive performance in either immersive environments (using VR headsets, n=26) or projective environments (using traditional projection screen, n=26) through a quasi-experimental design. The results showed that the pupils exhibited stronger perceived immersion and positive attitudes toward virtual learning and achieved better learning effectiveness in the immersive setting than in the projective setting. We are continuing to expand this work for more understandings of immersive virtual field trips in elementary classrooms.

Background-extracted extended Kalman filter-based phase shift estimation algorithm for phase shifting profilometry system

The accuracy of phase shift in the phase-measuring profilometry (PMP) directly affect the result of the three-dimensional reconstruction. A robust phase shifting estimation method based on the extended Kalman filter (EKF) is proposed to address the imprecise phase shift induced by the mechanical projector and dynamic object. Unlike the traditional fringe projection profilometry (FPP) in which the zero frequency is suppressed and the fundamental frequency is extracted, the zero-frequency component is extracted by using two-dimensional variational mode decomposition and bidimensional empirical mode decomposition (2D-VMD-BEMD) algorithm, and is used to establish the predicted observations of the measurement process in the EKF. The coefficient of the quadratic phase in a selected window is employed to estimate the actual phase step by using EKF and subsequently the accurate reconstructed phase map is obtained. This paper develops a novel 2D-VMD-BEMD EKF estimator to evaluate the actual phase shift and enhance the accuracy of the phase reconstruction, and simultaneously provides a reliable and effective method for the extraction of background under unfavorable conditions. Simulation and experimental results demonstrate that the proposed region-wise estimation method effectively removes the effect of the unexpected phase shift, and can be widely available for N-step phase shifting algorithm in phase shifting profilometry.

A regularized projection immersed boundary method for smooth boundary forces

The projection immersed boundary method calculates boundary forces (i.e., surface stresses) from velocity constraints without introducing additional timestep restrictions. However, the projection method generally fails to produce boundary forces that converge to the actual physical surface stresses when the grid is refined. In most cases, the boundary forces show spurious oscillation. This spurious oscillation is particularly violent when the Lagrangian-to-Eulerian grid spacing ratio is small. As a result, the traditional projection method may encounter problems in fluid-structural interaction (FSI) simulations where accurate boundary forces are needed. To address these challenges, we propose a regularized projection method using (generalized) Tikhonov regularization techniques. The regularized projection method yields smoother boundary force solutions that closely approximate the actual boundary force distributions for a wider range of Lagrangian-to-Eulerian grid spacing ratios. The boundary force shows first order convergence with properly chosen parameters. We demonstrate the effectiveness and accuracy of our approach through various test cases.

Back projection deep unrolling network for handwritten text image super resolution

Current super-resolution (SR) methods have demonstrated exceptional advancements in the domain of natural image processing. Nevertheless, these approaches do not fully address the open issues of edge blurring and distortion. In order to address the aforementioned challenges, we propose a novel deep unrolling model, coined back projection deep unrolling network (BPDUN), for super-resolution of handwritten text images leveraging the Algorithm Unrolling paradigm. We design the network model of BPDUN by unfolding and truncating the traditional iterative back projection algorithm (IBPA). We unroll IBPA into a cascade operation of three building blocks (deep denoising module, low-frequency reconstruction module, and residual projection module). BPDUN inherits the interpretability of the iterative optimization algorithm and is also designed to make the reconstructed text image more realistic and natural. Moreover, we propose a new benchmark dataset to address challenging SR problems of handwritten text image (HDT300). Extensive experiments show that BPDUN obtains an enhanced balance between the performance (quantified by PSNR and SSIM) and the cost (as measured by network parameters). Notably, BPDUN sets new benchmarks on the HDT300 dataset, surpassing previous state-of-the-art approaches by achieving up to 0.2 dB gains in PSNR.