Unfortunately, the precision of long-range 2D offset regression is constrained, resulting in a substantial performance deficit when contrasted with the capabilities of heatmap-based methods. biomarker conversion The paper addresses the long-range regression challenge by redefining the 2D offset regression as a classification problem. A straightforward and effective method, termed PolarPose, is presented for performing 2D regression in polar coordinates. PolarPose efficiently simplifies the regression task by converting the 2D offset regression in Cartesian coordinates to a quantized orientation classification and 1D length estimation in the polar coordinate system, making framework optimization easier. To achieve greater precision in keypoint localization within the PolarPose algorithm, we introduce a multi-center regression strategy to address the issues stemming from orientation quantization errors. The PolarPose framework showcases enhanced reliability in regressing keypoint offsets, consequently achieving more accurate keypoint localization. Employing a single model and a single scale, PolarPose achieved an AP of 702% on the COCO test-dev dataset, surpassing existing regression-based state-of-the-art techniques. PolarPose exhibits substantial efficiency gains, achieving 715% AP at 215 FPS, 685% AP at 242 FPS, and 655% AP at 272 FPS on the COCO val2017 dataset, surpassing current leading-edge approaches.
To ensure the alignment of corresponding feature points, multi-modal image registration meticulously aligns two images acquired from different modalities. Images from disparate modalities, sensed by various instruments, frequently exhibit a wide array of distinct features, posing a challenge in establishing accurate correspondences. Psychosocial oncology Although deep learning has facilitated the development of various deep networks for the alignment of multi-modal images, their lack of interpretability remains a major constraint. Our first step in this paper is to model the multi-modal image registration problem with a disentangled convolutional sparse coding (DCSC) model. In this model, the multi-modal features involved in alignment (RA features) are completely segregated from those not performing alignment functions (nRA features). By leveraging RA features exclusively for deformation field prediction, we can effectively eliminate the interference from nRA features, thereby boosting registration accuracy and efficiency. The DCSC model's optimization strategy for isolating RA and nRA features is subsequently encoded into a deep network, the Interpretable Multi-modal Image Registration Network (InMIR-Net). To accurately isolate RA and non-RA (nRA) features, we further implement an accompanying guidance network (AG-Net) to supervise RA feature extraction within the InMIR-Net. A universal approach to rigid and non-rigid multi-modal image registration is provided by the InMIR-Net framework. Extensive experimentation validates the effectiveness of our approach for rigid and non-rigid registrations across diverse multi-modal image datasets, featuring RGB/depth, RGB/near-infrared, RGB/multi-spectral, T1/T2-weighted magnetic resonance, and CT/magnetic resonance image combinations. Within the repository https://github.com/lep990816/Interpretable-Multi-modal-Image-Registration, the codes for Interpretable Multi-modal Image Registration are situated.
Wireless power transfer (WPT) often benefits from the high permeability of materials like ferrite, leading to enhanced power transfer efficiency. For the WPT system of inductively coupled capsule robots, the ferrite core's placement is confined to the power receiving coil (PRC) to maximize coupling. Concerning the power transmitting coil (PTC), ferrite structure design receives minimal examination, instead concentrating solely on magnetic focusing without a comprehensive design process. This research introduces a new ferrite structure for PTC, which prioritizes the concentration of magnetic fields, as well as the mitigation and shielding of leaked magnetic fields. A unified design combines the ferrite concentrating and shielding components, creating a closed path with low magnetic reluctance for magnetic lines, thus improving inductive coupling and PTE performance. Through the combined application of analyses and simulations, the proposed configuration's parameters are fashioned and fine-tuned, focusing on metrics such as average magnetic flux density, uniformity, and shielding effectiveness. For the purpose of performance enhancement validation, PTC prototypes with different ferrite layouts were developed, tested, and their results compared. The experimental results definitively indicate a notable enhancement in the average power output to the load, escalating from 373 milliwatts to 822 milliwatts, and a commensurate increase in PTE from 747 percent to 1644 percent, displaying a relative percentage difference of 1199 percent. Moreover, a slight boost has been observed in power transfer stability, climbing from 917% to 928%.
Multiple-view (MV) visualizations have become a standard practice for visual communication and exploratory data visualization tasks. However, the current MV visualisations predominantly designed for desktops, often prove inadequate for the consistently shifting and diversified screen sizes of contemporary displays. We detail a two-stage adaptation framework in this paper, designed to automate the retargeting and semi-automate the tailoring of a desktop MV visualization to fit displays of varying sizes. We model layout retargeting as an optimization process, and suggest a simulated annealing technique to automatically retain the arrangement of multiple views. Next, we equip each view with the ability to fine-tune its visual appearance using a rule-based automatic configuration process, complemented by an interactive interface designed for adjusting chart-oriented encoding modifications. To show the effectiveness and adaptability of our proposed technique, a selection of MV visualizations is presented, showcasing their successful adaptation from large desktop displays to smaller screen formats. We also present the outcomes of a user study, evaluating the performance of our visualization techniques against existing methods. The outcome clearly indicates that visualizations generated by our approach were preferred by participants, who considered them easier to use than other methods.
We investigate the simultaneous estimation of event-triggered state and disturbance in Lipschitz nonlinear systems, where the state vector incorporates an unknown time-varying delay. AP-III-a4 State and disturbance estimation, for the first time, is now robustly achievable using an event-triggered state observer. Only the output vector's information is utilized by our method under the stipulated event-triggered condition. Previous methods for estimating both state and disturbance simultaneously, using augmented state observers, assumed the continuous availability of the output vector data. This approach diverges from that model. This noteworthy attribute, therefore, minimizes the pressure on communication resources, while upholding a satisfactory level of estimation performance. To address the novel challenge of event-triggered state and disturbance estimation, and to overcome the obstacle of unknown time-varying delays, we introduce a novel event-triggered state observer and derive a sufficient condition for its viability. We introduce algebraic transformations and employ inequalities, such as the Cauchy matrix inequality and the Schur complement lemma, to surmount the technical obstacles in observer parameter synthesis. This allows the formulation of a convex optimization problem for systematically determining observer parameters and optimal disturbance attenuation. Ultimately, we put the method to the test by utilizing two numerical examples.
Unveiling the causal architecture linking various variables from observational data stands as a critical endeavor within numerous scientific disciplines. Despite the emphasis on global causal graph discovery by most algorithms, the local causal structure (LCS), despite its significant practical applications and relative simplicity, remains less explored. Challenges in LCS learning stem from the need to accurately determine neighborhoods and precisely orient edges. LCS algorithms, founded on conditional independence tests, demonstrate diminished accuracy due to the influence of noise, the variety of data generation mechanisms, and the scarcity of data samples in real-world applications, leading to the ineffectiveness of conditional independence tests. Additionally, the Markov equivalence class is the sole obtainable result; consequently, some edges remain undirected. In this paper, we present GraN-LCS, a gradient-descent-based approach to learning LCS, which simultaneously determines neighbors and orients edges, thus enabling more accurate LCS exploration. The GraN-LCS system establishes the causal graph search problem as minimizing an acyclicity-penalized score function, optimizable through gradient-based methods. A multilayer perceptron (MLP), constructed by GraN-LCS, simultaneously fits all other variables against a target variable. Acyclicity-constrained local recovery loss is defined to encourage exploration of local graphs and the identification of direct causes and effects related to the target variable. To enhance effectiveness, preliminary neighborhood selection (PNS) is employed to outline the initial causal structure, followed by incorporating an L1-norm-based feature selection on the initial layer of the multi-layer perceptron (MLP) to reduce the scope of candidate variables and to achieve a sparse weight matrix. Finally, GraN-LCS produces an LCS, derived from a sparse weighted adjacency matrix learned using MLPs. Employing both artificial and actual data sets, we test the effectiveness of the system, benchmarking against top-performing baseline models. Investigating the influence of key GraN-LCS parts through an ablation study reveals their integral contribution.
This paper explores the quasi-synchronization phenomenon in fractional multiweighted coupled neural networks (FMCNNs), specifically considering discontinuous activation functions and parameter mismatches.