13 accepted papers
Flexible tendon-driven multi-segment robotic bronchoscopes can reach peripheral lung regions for minimally invasive diagnosis and therapy. However, long tendon transmissions introduce friction, elasticity, and backlash, which couple the motion of adjacent segments and reduce operational accuracy and…
Missing values in high-dimensional, mixed-type datasets pose significant challenges for data imputation, particularly under Missing Not At Random (MNAR) mechanisms. Existing methods struggle to integrate local and global data characteristics, limiting performance in MNAR and high-dimensional setting
Deep-learning models have allowed effective end-to-end SE systems in the Speech Enhancement (SE) field. Most of these methods are trained using a fixed reconstruction loss in a supervised setting. Often these losses do not perfectly represent the desired perceptual quality metrics, resulting in sub-…
Robot-assisted surgery has significantly advanced surgical precision, yet the development of autonomous surgical robots remains hindered by their limited understanding of complex surgical actions. Current systems lack the ability to effectively perceive and interpret intricate surgical relationships
This research addresses the challenge of achieving high-precision tracking of time-varying trajectories under nonlinear disturbances and motion constraints in microsurgical robots. A hybrid control framework integrating fuzzy adaptive sliding mode control with radial basis function neural networks i
In intraocular microsurgery with minute operational scales, instruments pass through non-visible regions of the anterior segment, where robot-assisted surgery, which heavily relies on visual perception, fails to determine the instrument’s attitude relative to the eyeball. This compromises surgical f
Prior robot-assisted cranium-milling studies only considered controlling the force in the skull’s vertical direction and neglected the milling cutter’s feed force. Additionally, achieving stable force control in multiple directions is challenging for robots due to the uneven skull surface. Here a hy…
Cable-driven continuum robots in bronchoscopic procedures hold immense potential to revolutionize the diagnosis and treatment of lung cancer. However, robotic bronchoscopes in current studies are typically large in size and inflexible. Therefore, this article introduces a novel cable-driven continuu…
Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and ope…
Deep learning has been successfully used to automate the modeling process that trains a network/model from a given experimental dataset to calculate the output directly using high-dimensional complex raw data. However, the trained network is an inverse of the welding process (forward process) that p
Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by restricting function classes or model parameters and the inverse transformat…
Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses…
Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs…