Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation

Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images. Among these methods, explicit kernel estimation approaches have demonstrated unprecedented performance at handling unknown degradations. Nonetheless, a number of limitations constrain their efficacy when used by downstream SR models. Specifically, this family of methods yields i) excessive inference time due to long per-image adaptation times and ii) inferior image fidelity due to kernel mismatch. In this work, we introduce a learning-to-learn approach that meta-learns from the information contained in a distribution of images, thereby enabling significantly faster adaptation to new images with substantially improved performance in both kernel estimation and image fidelity. Specifically, we meta-train a kernel-generating GAN, named MetaKernelGAN, on a range of tasks, such that when a new image is presented, the generator starts from an informed kernel estimate and the discriminator starts with a strong capability to distinguish between patch distributions. Compared with state-of-the-art methods, our experiments show that MetaKernelGAN better estimates the magnitude and covariance of the kernel, leading to state-of-the-art blind SR results within a similar computational regime when combined with a non-blind SR model. Through supervised learning of an unsupervised learner, our method maintains the generalizability of the unsupervised learner, improves the optimization stability of kernel estimation, and hence image adaptation, and leads to a faster inference with a speedup between 14.24 to 102.1x over existing methods.Comment: Preprint: Accepted at the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024

Hysteresis Modeling of Amplified Piezoelectric Stack Actuator for the Control of the Microgripper

This paper presents Bouc-Wen hysteresis modelling and tracking control of piezoelectric stack APA120S. The actuator is used to control a microgripper. A modified Bouc-Wen non-symmetric model is applied to study the behaviour of the system in static and dynamic state. The good agreement between predicted and measured curve showed that the Bouc-Wen model is an effective mean for modelling the hysteresis of piezoelectric actuator system. Subsequently, the inverse Bouc-Wen model is formulated and applied to cancel the non-linear hysteresis. In perspective of a control design, it is desirable to linearize the non-linear Bouc-Wen model to produce a static system. Finally, in order to increase damping of the actuator system and to improve the control accuracy, a cascaded PID controller is designed with consideration of the dynamics and static behaviour of the actuator. Experiment result shows that error is of only 5% if PID is cascaded with hysteresis compensation. Therefore, hysteresis compensation with PID controller greatly improves the micromanipulation accuracy of the microgripper actuated by piezoelectric stack

How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor

Neural architecture search has proven to be a powerful approach to designing and refining neural networks, often boosting their performance and efficiency over manually-designed variations, but comes with computational overhead. While there has been a considerable amount of research focused on lowering the cost of NAS for mainstream tasks, such as image classification, a lot of those improvements stem from the fact that those tasks are well-studied in the broader context. Consequently, applicability of NAS to emerging and under-represented domains is still associated with a relatively high cost and/or uncertainty about the achievable gains. To address this issue, we turn our focus towards the recent growth of publicly available NAS benchmarks in an attempt to extract general NAS knowledge, transferable across different tasks and search spaces. We borrow from the rich field of meta-learning for few-shot adaptation and carefully study applicability of those methods to NAS, with a special focus on the relationship between task-level correlation (domain shift) and predictor transferability; which we deem critical for improving NAS on diverse tasks. In our experiments, we use 6 NAS benchmarks in conjunction, spanning in total 16 NAS settings -- our meta-learning approach not only shows superior (or matching) performance in the cross-validation experiments but also successful extrapolation to a new search space and tasks

The Art of Tactile Sensing: A State of Art Survey

This paper describes about tactile sensors, its transduction methods, state-of-art and various application areas of these sensors. Here we are taking in consideration the sense of touch. This provides the robots with tactile perception. In most of the robotic application the sense of touch is very helpful. The ability of robots to touch and feel the object, grasping an object by controlled pressure, mainly to categorize the surface textures. Tactile sensors can measure the force been applied on an area of touch. The data which is interpreted from the sensor is accumulated by the array of coordinated group of touch sensors. The sense of touch in human is distributed in four kinds by tactile receptors: Meissner corpuscles, the Merkel cells, the Rufina endings, and the Pacinian corpuscles. There has many innovations done to mimic the behaviour of human touch. The contact forces are measured by the sensor and this data is used to determine the manipulation of the robot

Multi-phase model for moisture transport in wood supported by X-ray computed tomography data

This study investigates the dynamics of moisture transport in Scots pine (Pinus sylvestris L.) heartwood and sapwood, under alternating drying and wetting cycles, incorporating interactions between bound water, free water, and water vapor using a multi-phase model. Cylindrical specimens oriented longitudinally, radially, and tangentially were subjected to controlled relative humidity (RH) steps of 33%, 94%, and 64% at 23 C. High-resolution X-ray computed tomography (CT) provided detailed, time-resolved measurements of moisture distributions within the wood. A multi-phase model was developed that couples Fickian diffusion (for bound water and vapor) with Darcy’s law (for free water), supplemented by phase-conversion terms that account for evaporation and sorption. Key parameters, including absolute and relative permeabilities, direction-dependent vapor diffusivity reductions, thermal conductivity tensors, and free water transport formulations, were determined by matching predicted moisture profiles to the CT measurements. Among concentration and mixed concentration-pressure formulations for free water model, the mixed approach produced the most accurate match. The CT images revealed a rapid depletion of free water during the initial drying step, followed by distinct variations in bound water content as the RH was raised and lowered. Numerical simulations closely replicated these trends, indicating that the calibrated model effectively represents moisture transport both above and below the fiber saturation point

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112