For effective hand and finger rehabilitation using robotic devices, kinematic compatibility is essential for their clinical viability and acceptance. Within the current state-of-the-art kinematic chains, various solutions are proposed, each with a different emphasis on the balance between kinematic compatibility, their adjustability to a range of body types, and the capacity to derive clinically relevant information. A novel kinematic chain designed for metacarpophalangeal (MCP) joint mobilization in the long fingers is presented in this study, coupled with a mathematical model for real-time computation of joint angles and the corresponding torque. The proposed mechanism can seamlessly align with the human joint, maintaining efficient force transfer and avoiding any generation of parasitic torque. Patients with traumatic-hand injuries will benefit from the integration of this chain into the exoskeletal device designed for rehabilitation. An exoskeleton actuation unit, featuring a series-elastic architecture, has been assembled and put through preliminary testing with eight human subjects to ensure compliant human-robot interaction. Performance was examined by evaluating (i) the precision of MCP joint angle estimations, using a video-based motion tracking system as a benchmark, (ii) residual MCP torque when the exoskeleton's control yielded a null output impedance, and (iii) the precision of torque tracking. The experimental results indicated a root-mean-square error (RMSE) below 5 degrees for the estimations of the MCP angle. The estimated MCP residual torque did not exceed 7 mNm. Torque tracking, when confronted with sinusoidal reference profiles, yielded an RMSE below 8 mNm, indicating precise tracking. The device's results strongly suggest the need for further clinical evaluations.
The diagnosis of mild cognitive impairment (MCI), a precursor to Alzheimer's disease (AD), is a cornerstone for initiating treatments that aim to postpone the manifestation of AD. Previous research efforts have indicated the potential of functional near-infrared spectroscopy (fNIRS) in the diagnosis of mild cognitive impairment. While fNIRS data processing is crucial, discerning low-quality segments demands a high degree of proficiency. Furthermore, investigations into the impact of well-defined, multi-faceted functional near-infrared spectroscopy (fNIRS) characteristics on disease classification are scarce. This study subsequently proposed a simplified fNIRS preprocessing method to analyze fNIRS data, using multi-faceted fNIRS features within neural networks in order to explore the influence of temporal and spatial factors on differentiating Mild Cognitive Impairment from normal cognitive function. This research examined the capability of Bayesian optimization-tuned neural networks to identify 1D channel-wise, 2D spatial, and 3D spatiotemporal features within fNIRS data, aiming to distinguish MCI patients from control groups. The respective highest test accuracies for 1D, 2D, and 3D features were 7083%, 7692%, and 8077%. The fNIRS data collected from 127 participants was meticulously compared, revealing the 3D time-point oxyhemoglobin feature as a more promising indicator for the detection of mild cognitive impairment (MCI). Beyond that, this research presented a potential system for processing fNIRS data. The developed models did not require manual hyperparameter tuning, which facilitated broader utilization of the fNIRS modality for MCI classification using neural networks.
A novel data-driven indirect iterative learning control (DD-iILC) approach is introduced in this work for repetitive nonlinear systems. The technique integrates a proportional-integral-derivative (PID) feedback control scheme into the inner loop. A linear parametric iterative tuning algorithm, targeting set-point adjustment, is derived from an ideal, theoretically existent, nonlinear learning function, employing an iterative dynamic linearization (IDL) technique. Optimization of an objective function specific to the controlled system leads to the presentation of an adaptive iterative updating strategy for the parameters within the linear parametric set-point iterative tuning law. Considering the system's nonlinear and non-affine qualities, and the lack of a model, the IDL method is used in conjunction with a parameter adaptation strategy analogous to iterative learning laws. The DD-iILC project's final stage involves the incorporation of the local PID controller. The strategy of mathematical induction, in combination with contraction mapping, validates the convergence. The validity of the theoretical outcomes is ascertained via simulations on a numerical example, in addition to a permanent magnet linear motor.
The pursuit of exponential stability in time-invariant nonlinear systems with matched uncertainties, subject to the persistent excitation (PE) condition, presents a substantial hurdle. Regarding strict-feedback systems with mismatched uncertainties and unknown, time-varying control gains, this article achieves global exponential stabilization without a prerequisite PE condition. In the absence of persistence of excitation, the resultant control, incorporating time-varying feedback gains, is sufficient to guarantee global exponential stability of parametric-strict-feedback systems. The enhanced Nussbaum function allows for the extension of preceding outcomes to more general nonlinear systems, in which the time-varying control gain's magnitude and sign remain uncertain. The Nussbaum function's argument's positivity is guaranteed by the nonlinear damping design, enabling a straightforward technical analysis of the function's boundedness. Conclusively, the global exponential stability of parameter-varying strict-feedback systems, including the boundedness of the control input and update rate, and the asymptotic constancy of the parameter estimate, are verified. Numerical simulations are executed to assess the effectiveness and benefits of the proposed methods.
Value iteration (VI) adaptive dynamic programming for continuous-time nonlinear systems is examined in this article, addressing both convergence properties and error bounds. The proportional relationship between the total value function and the cost of a single integration step is established by positing a contraction assumption. Given an arbitrary positive semidefinite initial function, the convergence property of the VI is now demonstrated. Furthermore, the algorithm's implementation using approximators accounts for the compounding effect of errors introduced in each iterative step. Considering contraction, the error boundaries are specified, making sure the iterative solutions converge to a neighborhood of the optimal solution, and the correlation between the ideal solution and the computed solutions is also identified. For a more tangible understanding of the contraction assumption, a procedure is detailed for deriving a conservative estimate. Lastly, three simulated situations are shown to validate the theoretical results.
Learning to hash is widely adopted for visual retrieval applications because of its speed and storage efficiency. medical staff However, the familiar hashing approaches hinge on the condition that query and retrieval samples are positioned within a uniform feature space, all originating from the same domain. Ultimately, heterogeneous cross-domain retrieval tasks are not directly addressed by these strategies. This paper proposes a generalized image transfer retrieval (GITR) problem, which is hampered by two principal issues: 1) the potential for query and retrieval samples to be drawn from distinct domains, thereby introducing a significant domain distribution disparity, and 2) the possible heterogeneity or misalignment of features across these domains, leading to a separate feature gap. We introduce an asymmetric transfer hashing (ATH) framework designed to address the GITR problem, demonstrating its utility across unsupervised, semi-supervised, and supervised scenarios. The domain distribution gap, as identified by ATH, is characterized by the divergence between two asymmetric hash functions, and the feature gap is mitigated via a custom adaptive bipartite graph constructed from cross-domain datasets. Asymmetric hash functions and bipartite graphs, when jointly optimized, facilitate knowledge transfer, thereby avoiding the loss of information caused by feature alignment. The intrinsic geometrical structure of single-domain data is maintained, using a domain affinity graph, to lessen the impact of negative transfer. Extensive comparisons of our ATH method against state-of-the-art hashing methods, across different GITR subtasks and on both single-domain and cross-domain benchmarks, demonstrate its superiority.
Ultrasonography's non-invasive, radiation-free, and economical characteristics make it a vital, routine examination for breast cancer diagnosis. The inherent limitations of breast cancer diagnosis unfortunately constrain the accuracy of its detection. Employing breast ultrasound (BUS) imaging for a precise diagnosis would be highly beneficial. To classify breast cancer lesions and accurately diagnose the disease, numerous learning-based computer-aided diagnostic methods have been suggested. However, a significant portion of these techniques demand a predefined region of interest (ROI), followed by the classification of the lesion situated within that ROI. VGG16 and ResNet50, prominent instances of conventional classification backbones, showcase strong classification capabilities while eliminating the ROI requirement. Iranian Traditional Medicine Because of their lack of interpretability, these models face limitations in their clinical application. In ultrasound image analysis for breast cancer diagnosis, we propose a novel ROI-free model with interpretable feature representations. Recognizing the distinct spatial arrangements of malignant and benign tumors within differing tissue layers, we employ a HoVer-Transformer to embody this anatomical understanding. The proposed HoVer-Trans block performs a horizontal and vertical extraction of spatial information from the inter-layer and intra-layer data. this website Our open dataset GDPH&SYSUCC is dedicated to breast cancer diagnosis and released for BUS.