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Lightweight object detection algorithms play a paramount role in unmanned aerial vehicles (UAVs) remote sensing. However, UAV remote sensing requires target detection algorithms to have higher inference speeds and greater accuracy in detection. At present, most lightweight object detection algorithms have achieved fast inference speed, but their detection precision is not satisfactory. Consequently, this paper presents a refined iteration of the lightweight object detection algorithm to address the above issues. The MobileNetV3 based on the efficient channel attention (ECA) module is used as the backbone network of the model. In addition, the focal and efficient intersection over union (FocalEIoU) is used to improve the regression performance of the algorithm and reduce the false-negative rate. Furthermore, the entire model is pruned using the convolution kernel pruning method. After pruning, model parameters and floating-point operations (FLOPs) on VisDrone and DIOR datasets are reduced to 1.2 M and 1.5 M and 6.2 G and 6.5 G, respectively. The pruned model achieves 49 frames per second (FPS) and 44 FPS inference speeds on Jetson AGX Xavier for VisDrone and DIOR datasets, respectively. To fully exploit the performance of the pruned model, a plug-and-play structural re-parameterization fine-tuning method is proposed. The experimental results show that this fine-tuned method improves mAP@0.5 and mAP@0.5:0.95 by 0.4% on the VisDrone dataset and increases mAP@0.5:0.95 by 0.5% on the DIOR dataset. The proposed algorithm outperforms other mainstream lightweight object detection algorithms (except for FLOPs higher than SSDLite and mAP@0.5 Below YOLOv7 Tiny) in terms of parameters, FLOPs, mAP@0.5, and mAP@0.5:0.95. Furthermore, practical validation tests have also demonstrated that the proposed algorithm significantly reduces instances of missed detection and duplicate detection. [ABSTRACT FROM AUTHOR]

Recently, super-resolution (SR) has achieved superior performance with the development of deep learning. However, previous methods usually require considerable computational resources with a large model size, which hinders practical applications. To achieve real-time inference and high quality for SR, this paper presents an edge-priority-extraction network, which is constructed with our proposed edge-priority blocks (EPB). The EPB utilizes multiple branches with edge information to further improve the network representation. Moreover, it can be re-parameterized for efficient inference. For more effective utilization of edge information, this paper also proposes the mix-priority filter with edge extraction of horizontal and vertical priorities to improve the network performance. The filters can adaptively extract the edge information with multi-direction derivatives. The experimental results show that our models can use less computational cost to meet the real-time demand and have a better SR performance than the recent real-time SR models. [ABSTRACT FROM AUTHOR]

In this paper, we aim to improve super-resolution (SR) imaging quality on 4K/8K images with a negligible increase in computational cost and alleviate the accuracy drop after quantization. Experiments have discovered two phenomena: (1) the re-parameterization (Rep) technique has no apparent advantages in regions with smooth textures, and (2) the accuracy drop after quantization compared with SR methods based on No-Rep because the structure information of the image will be weakened caused by the multi-branch fusion in Rep technique. Inspired by the above phenomenon, we innovatively combine texture classification and Rep techniques to propose a generic TARepSR framework (consisting of TA-Module and VarRepSR-Module) to adjust the branching of Rep blocks texture-awarely. Specifically, the TA-Module is a lightweight classification network to classify textures in different regions. An existing SR network using texture-aware Rep techniques can be used as the VarRepSR-Module to super-resolute images with higher imaging quality without additional computational costs. Moreover, we propose a TC loss to avoid over-fitting caused by an unbalanced degree of the tendency of classification results to classify different textures better. Experiments show that our TARepSR can not only improve the imaging quality of most existing methods (e.g., FSRCNN, CARN, EDSR, XLSR) on 4K/8K images with negligible increase in computational cost but also improve the accuracy after quantization compared with the state-of-the-art Rep methods. [ABSTRACT FROM AUTHOR]

Aiming at the two problems that most existing deep network-based fusion approaches have complex network architecture with high computation cost, and they fail to adequately consider the intrinsic characteristics of multi-modal images which results in insufficient information interaction of cross-modality features, a re-parameterized infrared and visible image fusion network based on coordinate attention is proposed. In this network, re-parameterization technique is introduced and combined with residual learning to perform feature extraction for computing efficiency and satisfying fusion quality. Moreover, to improve the interactivity of cross-modality features and fully utilize multi-modal image information, a coordinate attention-based fusion module is devised to yield fused feature. Considering the information loss during extracting process, a fused feature enhance module which leverages the preceding cross-modality features to implement feature compensation is further developed. Extensive experiments demonstrate that the proposed method not only has lower computational cost, but also achieves the improvement of multiple objective evaluation metrics while ensuring good visual effects. [ABSTRACT FROM AUTHOR]

Stitched images can offer a broader field of view, but their boundaries can be irregular and unpleasant. To address this issue, current methods for rectangling images start by distorting local grids multiple times to obtain rectangular images with regular boundaries. However, these methods can result in content distortion and missing boundary information. We have developed an image rectangling solution using the reparameterized transformer structure, focusing on single distortion. Additionally, we have designed an assisted learning network to aid in the process of the image rectangling network. To improve the network's parallel efficiency, we have introduced a local thin-plate spline Transform strategy to achieve efficient local deformation. Ultimately, the proposed method achieves state-of-the-art performance in stitched image rectangling with a low number of parameters while maintaining high content fidelity. The code is available at https://github.com/MelodYanglc/TransRectangling. [ABSTRACT FROM AUTHOR]

Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks. [ABSTRACT FROM AUTHOR]

Various types of crop diseases have posed a serious threat to the high-quality development of crops in agricultural production. A timely identification of disease types can greatly contribute to the prevention and control of crop diseases. However, the convolutional neural networks (CNN) recognition of crop leaf diseases cannot fully meet the large-scale production in recent years, due to the numerous parameters, high computational complexity, and poor real-time performance. There is a high demand to improve the accuracy of lightweight CNN models under the relatively complex background of crop leaf images in natural environments. In this study, a lightweight re parameterized leaf diseases identification network (RLDNet) was proposed to identify crop leaf diseases. Firstly, reparameterization was introduced into the MobileNetV2. The inverted residual blocks were reparameterized to construct the reparameterized inverse residual (RIR) block for the high inference speed. A multi-branch architecture was adopted during the training phase. The diversity of the feature space was enriched to improve the representation learning of the network. The reparameterization was used to equivalently convert the multi-branch architecture into a single path structure for the inference after training. The output of the transformed inference model was the same as the original multi-branch. The parameter identity transformation was achieved to improve the inference speed of the model with high recognition accuracy. There was a decrease in the number of output channels and the stacking times of core modules. A shallow and narrow network structure was obtained to enhance the extraction of shallow features, such as the disease colors and textures, indicating the reduced number of model parameters. Secondly, a lightweight ultra lightweight subspace attention module attention mechanism (ULSAM) was added to the last two RIR blocks of the model. Leaf disease characteristics were combined to more effectively distinguish between backgrounds and prospects for better target classification. The interference was reduced in the disease areas from the complex backgrounds. Finally, the DepthShrinker pruning was utilized to learn the importance of the activation function in the model. The redundant parameters were removed to further reduce the space occupation. The lossless lightweight and high accuracy of the model were achieved using knowledge distillation. The recognition accuracy of 99.53% was achieved in the RLDNet on the PlantVillage dataset, whereas 98.49% on the self-built leaf disease dataset with a parameter size of 0.65 M. The inference time for a single-leaf disease image was 2.51 ms. RLDNet shared a performance similar to the Transformer-based models (such as MobileVit-S) and CNN-based models (such as ResNet18) on both datasets, with a significant reduction in parameter, respectively, compared with MobileVitS and ResNet18. The higher recognition accuracy and lighter weight were obtained in the RLDNet, compared with the lightweight models, such as MobileNetV3 and ShuffleNetV2. The improved model can be expected to effectively identify the leaf diseases in the complex backgrounds, with less parameter memory and faster inference speed. The findings can also be applied in the practical production and application of smart agriculture. [ABSTRACT FROM AUTHOR]

With the supervised deep learning algorithms, it is prone to generate artifacts when restoring the blurred solar speckle images taken by Yunnan Observatories, and it has a long training time and over-reliance on reference images, this paper proposed a weakly supervised method based on structural reparameterization combined with multi-branch module to reconstruct solar speckle images. First, deblurring model combined single-scale and multi-scale network to design, with constructing multi-branch modules to extract features of different scales, enhance detailed information, and reduce the generation of artifacts; second, each branch structure re-parameterized to make the reuse of structure parameters runs through the entire feature extraction process; after that, the deblurring model embedded in the weakly supervised training, the blurred image assorted firstly, then the degradation model used to learn different levels of degradation. Constituted paired dataset of corresponding levels, and the deblurring model used to inversely degenerate the dataset to reconstruct solar speckle images. Experimental results show that compared with the existing deblurring method, the proposed method has higher model training efficiency and less dependence on reference images, which can meet the high-resolution reconstruction requirements of solar speckle images. [ABSTRACT FROM AUTHOR]

In the field of steel production, the detection of steel surface defects is one of the most important guarantees for the quality of steel production. In the process of defect detection, there are problems regarding the noise of the acquisition background, the scale of defects, and the detection speed. At present, in the face of complex steel surface defects, realizing efficient real-time steel surface defect detection has become a difficult problem. In this paper, we propose a lightweight and efficient real-time defect detection method, LDE-YOLO, based on YOLOv8. First, we propose a lightweight multi-scale feature extraction module, LighterMSMC, which not only achieves a lightweight backbone network, but also effectively guarantees the long range dependence of the features, so as to realize multi-scale feature extraction more efficiently. Secondly, we propose lightweight re-parameterized feature pyramid, DE-FPN, in which the sparse patterns of the overall features and the detailed features of the local features are efficiently captured by the DE-Block, and then efficiently fused by the PAN feature fusion structure. Finally, we propose Efficient Head, which lightens the model by group convolution while its improves the diagonal correlation of the feature maps on some specific datasets, thus enhancing the detection performance. Our proposed LDE-YOLO obtains 80.8 mAP and 75.5 FPS on NEU-DET , 80.5 mAP and 75.5 FPS on GC10-DET. It obtains 2.5 mAP and 4.7 mAP enhancement compared to the baseline model, and the detection speed is also improved by 10.4 FPS, while in terms of the number of floating point operations and parameters of the model reduced by 60.2% and 49.1%, which is sufficient to illustrate its lightweight effectiveness and realize an efficient real-time steel surface defect detection model. • A lightweight multi-scale feature extraction module LighterMSMC was proposed, for the backbone network. Lightweight convolution PConv and multi-scale feature extraction module LMSMC were applied. While the model is more lightweight, so as to achieve multi-scale feature extraction more efficiently and improve the generalization performance of the defect detection model in complex situations. • Propose a lightweight re-parameterized module, DE-Block, and apply it to the feature fusion part of YOLOv8 to obtain the DE-FPN. DE-Block can better extract sparse patterns from overall features, while supplement detailed features. And its structural design can minimize the parameters increase. During the inference process, convolutions in DE-Block can be re-parameterized to reduce inference costs. Thus, a lightweight feature pyramid structure is constructed. • Propose an efficient decoupled detection head, Efficient Head, to lightweight detection heads through group convolution. Applying the aforementioned methods to YOLOv8, this paper proposes LDE-YOLO, which not only has the ability to effectively detect defects in complex situations. Meanwhile, LDE-YOLO significantly reduces the floating-point operations and parameters of the model, thereby improving its defect detection speed. [ABSTRACT FROM AUTHOR]

Autonomous-rail Rapid Transit (ART) possesses various advantages in intelligent transportation, but it does not effectively recognize road conditions caused solely by deploying single-modal cameras. In this paper, a lightweight fusion-based object detection neural network was designed with multi-modal sensors for the ART. Firstly, the Light Detection and Ranging (LiDAR) applied additional encoding and preprocessing to the point cloud. Secondly, a backbone and a detection head of the network structure were proposed through re-parameterization and pruning techniques. Furthermore, a fusion module was designed with a selective soft attention mechanism to fuse the extracted features. The proposed model was tested on the open autonomous driving dataset; it achieved a 7.38% improvement in mean average precision (mAP) compared to the original you only look once (YOLO) as well as other state-of-the-art (SOTA) models. Finally, practical experiments were conducted in the maintenance center of ART to simulate the operational scenarios and validate the feasibility of the proposed method in this study. By fully utilizing the information in different modalities and addressing the limitations of single-modal recognition, efforts were made to improve the robustness of road object detection for ART under different road conditions. Consequently, our method provides effective solutions which benefit intelligent transportation with advanced algorithms and strategies. [Display omitted] • Multi-modal sensors were applied in the ART operating scenarios for road targets detection. • A lightweight detection model with re-parameterization methods and anchor-free detectors. • Proposed selective soft attention fusion module based on feature-level fusion strategy. • Practical experiments performed in the ART factory for testing and achieving intelligent transportation. [ABSTRACT FROM AUTHOR]

Vehicle and drone-mounted surveillance equipment face severe computational constraints, posing significant challenges for real-time, accurate crack segmentation. This paper introduces the crack location segmentation transformer (CLST) to address these issues. Images are processed to better resemble patches associated with cracks, enabling precise segmentation while significantly reducing the model's computational load. To handle varying segmentation challenges, a range of models with different computational demands has been designed to suit diverse needs. The most lightweight model can be deployed for real-time use on edge devices. A module in the neck of the pipeline encodes crack coordinate information, and end-to-end training has resulted in state-of-the-art performance across multiple datasets. • Crack location segmentation transformer (CLST) is introduced. • Focusing the field of view on the cracks reduces the amount of computation. • Rep-Crackformer can select modes to cope with different scenarios. • CLocation can encode crack spatial information with fewer parametric quantities. [ABSTRACT FROM AUTHOR]

In multi-view video systems, the decoded texture video and its corresponding depth video are utilized to synthesize virtual views from different perspectives using the depth-image-based rendering (DIBR) technology in 3D-high efficiency video coding (3D-HEVC). However, the distortion of the compressed multi-view video and the disocclusion problem in DIBR can easily cause obvious holes and cracks in the synthesized views, degrading the visual quality of the synthesized views. To address this problem, a novel two-stream re-parameterized refocusing hybrid attention (TRRHA) network is proposed to significantly improve the quality of synthesized views. Firstly, a global multi-scale residual information stream is applied to extract the global context information by using refocusing attention module (RAM), and the RAM can detect the contextual feature and adaptively learn channel and spatial attention feature to selectively focus on different areas. Secondly, a local feature pyramid attention information stream is used to fully capture complex local texture details by using re-parameterized refocusing attention module (RRAM). The RRAM can effectively capture multi-scale texture details with different receptive fields, and adaptively adjust channel and spatial weights to adapt to information transformation at different sizes and levels. Finally, an efficient feature fusion module is proposed to effectively fuse the extracted global and local information streams. Extensive experimental results show that the proposed TRRHA achieves significantly better performance than the state-of-the-art methods. The source code will be available at https://github.com/647-bei/TRRHA. • A two-stream re-parameterization refocusing hybrid attention network (TRRHA) for SVQE. • Design includes global multi-scale residual (GMR) and local feature pyramid attention (LFPA). • Proposed re-parameterized refocusing attention module (RRAM) for local multi-scale texture. • Captures multi-scale features with re-parameterized convolution (RC) branches. • Efficient feature fusion module (EFFM) significantly enhances SVQE performance. [ABSTRACT FROM AUTHOR]

Although detectors currently perform well in well-light conditions, their accuracy decreases due to insufficient object information. In addressing this issue, we propose the Re-parameterization Forward Semantic Compensation Network (RFSC-Net). We propose the Reparameterization Residual Efficient Layer Aggregation Networks (RSELAN) for feature extraction, which integrates the concepts of re-parameterization and the Efficient Layer Aggregation Networks (ELAN). While focusing on the fusion of feature maps of the same dimension, it also incorporates upward fusion of lower-level feature maps, enhancing the detailed texture information in higher-level features. Our proposed Forward Semantic Compensation Feature Fusion (FSCFF) network reduces interference from high-level to low-level semantic information, retaining finer details to improve detection accuracy in low-light conditions. Experiments on the low-light ExDark and DarkFace datasets show that RFSC-Net improves mAP by 2% on ExDark and 0.5% on DarkFace over the YOLOv8n baseline, without an increase in parameter counts. Additionally, AP50 is enhanced by 2.1% on ExDark and 1.1% on DarkFace, with a mere 3.7 ms detection latency on ExDark. • Retaining more detailed information improves detection performance in low-light environments. • Fusing lower-level feature maps with higher-level feature can retain more detailed information. • Designed an information compensation detection network that retains more effective detailed information. • The new model outperforms YOLOv8n by 2% in mAP on the low-light ExDark dataset, according to experimental results. [ABSTRACT FROM AUTHOR]

Forest is the largest carbon reservoir and carbon absorber on earth. Thus, mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal. Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images. However, deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources. Therefore, there was an urgent need for a fast and accurate deforestation detection model. In this study, we proposed an interesting but effective re-parameterization deforestation detection model, named RepDDNet. Unlike other existing models designed for deforestation detection, the main feature of RepDDNet was its decoupling feature, which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage, thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged. A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images (the total area of it was over 20,000 square kilometers), and the result indicated that the model computation efficiency could be improved by nearly 30% compared with the model without re-parameterization. Additionally, compared with other lightweight models, RepDDNet also displayed a trade-off between accuracy and computation efficiency. [ABSTRACT FROM AUTHOR]

Single image super-resolution is a well-established low-level vision task that aims to reconstruct high-resolution images from low-resolution images. Methods based on Transformer have shown remarkable success and achieved outstanding performance in SISR tasks. While Transformer effectively models global information, it is less effective at capturing high frequencies such as stripes that primarily provide local information. Additionally, it has the potential to further enhance the capture of global information. To tackle this, we propose a novel Large Kernel Hybrid Attention Transformer using re-parameterization. It combines different kernel sizes and different steps re-parameterized convolution layers with Transformer to effectively capture global and local information to learn comprehensive features with low-frequency and high-frequency information. Moreover, in order to solve the problem of using batch normalization layer to introduce artifacts in SISR, we propose a new training strategy which is fusing convolution layer and batch normalization layer after certain training epochs. This strategy can enjoy the acceleration convergence effect of batch normalization layer in training and effectively eliminate the problem of artifacts in the inference stage. For re-parameterization of multiple parallel branch convolution layers, adopting this strategy can further reduce the amount of calculation of training. By coupling these core improvements, our LKHAT achieves state-of-the-art performance for single image super-resolution task. [Display omitted] • The proposed module takes into account both high-frequency and low-frequency information of the image • The proposed module is conducive to generating clear texture details • The training strategy we proposed helps to reduce artifacts [ABSTRACT FROM AUTHOR]

Combining knowledge distillation (KD) and network pruning (NP) shows promise in learning a light network to accelerate object re-identification. However, KD requires an untrained student network to establish more critical connections in early epochs, but NP demands a well-trained student network to avoid destroying critical connections. This presents a dilemma, potentially leading to a collapse of the student network and harming object Re-ID performance. For that, we propose a distillation embedded absorbable pruning (DEAP) method. We design a pruner-convolution-pruner (PCP) unit to resolve the dilemma by loading NP's sparse regularization on extra untrained pruners. Additionally, we propose an asymmetric relation knowledge distillation method. It readily transfers feature representation knowledge and asymmetric pairwise similarity knowledge without using additional adaptation modules. Finally, we apply re-parameterization to absorb pruners of PCP units to simplify student networks. Experiments demonstrate the superiority of DEAP, such as on the VeRi-776 dataset, with ResNet-101 as a teacher, DEAP saves 73.24% of model parameters and 71.98% of floating-point operations without sacrificing accuracy. • We design a distillation embedded absorbable pruning method to readily transfer knowledge a teacher network from to a student network. • We propose an asymmetric relation knowledge distillation method to transfer both feature representation knowledge and pairwise similarity knowledge. • Our method achieves the state-of-the-art performance in terms of both accuracy and speed. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have notably advanced the field of image denoising. Yet, blindly increasing the depth or width of convolutional neural networks (CNNs) cannot ameliorate the network effectively, and even leads to training difficulties and sophisticated training tricks. In this paper, a lightweight CNN with heterogeneous kernels (HKCNN) is designed for efficient noise removal. HKCNN comprises four modules: a multiscale block (MB), an attention enhancement block (AEB), an elimination block (EB), and a construct block (CB). Specifically, the MB leverages heterogeneous kernels alongside re-parameterization to capture diverse complementary structure information, bolstering discriminative ability and the denoising robustness of the denoiser. The AEB incorporates an attention mechanism that prioritizes salient features, expediting the training stage and boosting denoising efficacy. The EB and CB are designed to further suppress noise and reconstruct latent clean images. Besides, the HKCNN integrates perceptual loss for both retaining semantic details and improving image perceptual quality, so as to refine the denoising output. Comprehensive qualitative and quantitative evaluations highlight the superior performance of HKCNN over state-of-the-art denoising methods, validating its efficacy in practical image denoising scenarios. [ABSTRACT FROM AUTHOR]

In many speaker verification tasks, the quality of speaker embedding is an important factor in affecting speaker verification systems. Advanced speaker embedding extraction networks aim to capture richer speaker features through the multi-branch network architecture. Recently, speaker verification systems based on transformer encoders have received much attention, and many satisfactory results have been achieved because transformer encoders can efficiently extract the global features of the speaker (e.g., MFA-Conformer). However, the large number of model parameters and computational latency are common problems faced by the above approaches, which make them difficult to apply to resource-constrained edge terminals. To address this issue, this paper proposes an effective, lightweight transformer model (MCA-former) with multi-scale convolutional self-attention (MCA), which can perform multi-scale modeling and channel modeling in the temporal direction of the input with low computational cost. In addition, in the inference phase of the model, we further develop a systematic re-parameterization method to convert the multi-branch network structure into the single-path topology, effectively improving the inference speed. We investigate the performance of the MCA-former for speaker verification under the VoxCeleb1 test set. The results show that the MCA-based transformer model is more advantageous in terms of the number of parameters and inference efficiency. By applying the re-parameterization, the inference speed of the model is increased by about 30%, and the memory consumption is significantly improved. • Designing a lightweight multi-scale convolutional self-attention module. • An efficient transformer encoder for speaker verification. • Using the re-parameterization method improves the model's inference efficiency. [ABSTRACT FROM AUTHOR]

Lower percentiles of product lifetime are useful for engineers to understand product failure, and avoiding costly product failure claims. This paper proposes a percentile re-parameterization model to help reliability engineers obtain a better lower percentile estimation of accelerated life tests under Weibull distribution. A log transformation is made with the Weibull distribution to a smallest extreme value distribution. The location parameter of the smallest extreme value distribution is re-parameterized by a particular 100 pth percentile, and the scale parameter is assumed to be nonconstant. Maximum likelihood estimates of the model parameters are derived. The confidence intervals of the percentiles are constructed based on the parametric and nonparametric bootstrap method. An illustrative example and a simulation study are presented to show the appropriateness of the method. The simulation results show that the re-parameterization model performs better compared with the traditional model in the estimation of lower percentiles, in terms of Relative Bias and Relative Root Mean Squared Error. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A joint-processing model for multi-GNSS (GPS, GLONASS, BDS and GALILEO) precise point positioning (PPP) is proposed, in which raw code and phase observations are used. In the proposed model, inter-system biases (ISBs) and GLONASS code inter-frequency biases (IFBs) are carefully considered, among which GLONASS code IFBs are modeled as a linear function of frequency numbers. To get the full rank function model, the unknowns are re-parameterized and the estimable slant ionospheric delays and ISBs/IFBs are derived and estimated simultaneously. One month of data in April, 2015 from 32 stations of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX) tracking network have been used to validate the proposed model. Preliminary results show that RMS values of the positioning errors (with respect to external double-difference solutions) for static/kinematic solutions (four systems) are 6.2 mm/2.1 cm (north), 6.0 mm/2.2 cm (east) and 9.3 mm/4.9 cm (up). One-day stabilities of the estimated ISBs described by STD values are 0.36 and 0.38 ns, for GLONASS and BDS, respectively. Significant ISB jumps are identified between adjacent days for all stations, which are caused by the different satellite clock datums in different days and for different systems. Unlike ISBs, the estimated GLONASS code IFBs are quite stable for all stations, with an average STD of 0.04 ns over a month. Single-difference experiment of short baseline shows that PPP ionospheric delays are more precise than traditional leveling ionospheric delays. [ABSTRACT FROM AUTHOR]

You recklessly told your boss that solving a non-linear system of size n ( n unknowns and n equations) requires a time proportional to n , as you were not very attentive during algorithmic complexity lectures. So now, you have only one night to solve a problem of big size (e.g., 1000 equations/unknowns), otherwise you will be fired in the next morning. The system is well-constrained and structurally irreducible: it does not contain any strictly smaller well-constrained subsystems. Its size is big, so the Newton–Raphson method is too slow and impractical. The most frustrating thing is that if you knew the values of a small number k ≪ n of key unknowns, then the system would be reducible to small square subsystems and easily solved. You wonder if it would be possible to exploit this reducibility, even without knowing the values of these few key unknowns. This article shows that it is indeed possible. This is done at the lowest level, at the linear algebra routines level, so that numerous solvers (Newton–Raphson, homotopy, and also p -adic methods relying on Hensel lifting) widely involved in geometric constraint solving and CAD applications can benefit from this decomposition with minor modifications. For instance, with k ≪ n key unknowns, the cost of a Newton iteration becomes O ( k n 2 ) instead of O ( n 3 ) . Several experiments showing a significant performance gain of our re-parameterization technique are reported in this paper to consolidate our theoretical findings and to motivate its practical usage for bigger systems. [ABSTRACT FROM AUTHOR]

Highlights: [•] Presents a novel method for both shape design and isogeometric analysis using mapped basis functions. [•] The space spanned by mapped B-spline basis functions is an extension of uniform B-splines over an arbitrary parameterization. [•] The continuity of the resulting surfaces can be arbitrary higher order, including at extraordinary points. [•] The proposed method can be further extended to other basis functions as well as to non-quadrilateral meshes. [ABSTRACT FROM AUTHOR]

Abstract: The problem of fitting spline surfaces to triangular mesh models is of importance in computer-aided design. Many fitting algorithms have been developed. This paper proposes several novel plug-and-play components or strategies: the use of -splines for fitting, a curvature-guided strategy, faithful re-parameterization and initial spline knot re-placement, which can be used to enhance fitting algorithms. We also present an adaptive -spline fitting algorithm integrating these components and strategies. Extensive experiments have been conducted to demonstrate these components. Our fitting algorithm can generate spline surfaces that well respect the geometrical features of input mesh models and have a more compact representation. [Copyright &y& Elsevier]