Open-Set Object Recognition Using Mechanical Properties During Interaction

while most of the tactile robots are operated in close-set conditions, it is challenging for them to operate in open-set conditions where test objects are beyond the robots' knowledge. We proposed an open-set recognition framework using mechanical properties to recongise known objects and incrementally label novel objects. The main contribution is a clustering algorithm that exploits knowledge of known objects to estimate cluster centre and sizes, unlike a typical algorithm that randomly selects them. The framework is validated with the mechanical properties estimated from a real object during interaction. The results show that the framework could recognise objects better than alternative methods contributed by the novelty detector. Importantly, our clustering algorithm yields better clustering performance than other methods. Furthermore, the hyperparameters studies show that cluster size is important to clustering results and needed to be tuned properly

Convolution theorems associated with quaternion linear canonical transform and applications

Novel types of convolution operators for quaternion linear canonical transform (QLCT) are proposed. Type one and two are defined in the spatial and QLCT spectral domains, respectively. They are distinct in the quaternion space and are consistent once in complex or real space. Various types of convolution formulas are discussed. Consequently, the QLCT of the convolution of two quaternionic functions can be implemented by the product of their QLCTs, or the summation of the products of their QLCTs. As applications, correlation operators and theorems of the QLCT are derived. The proposed convolution formulas are used to solve Fredholm integral equations with special kernels. Some systems of second-order partial differential equations, which can be transformed into the second-order quaternion partial differential equations, can be solved by the convolution formulas as well. As a final point, we demonstrate that the convolution theorem facilitates the design of multiplicative filters

The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow

This paper introduces the idea of the color Clifford Hardy signal, which can be used to process color images. As a complex analytic function's high-dimensional analogue, the color Clifford Hardy signal inherits many desirable qualities of analyticity. A crucial tool for getting the color and structural data is the local feature representation of a color image in the color Clifford Hardy signal. By looking at the extended Cauchy-Riemann equations in the high-dimensional space, it is possible to see the connection between the different parts of the color Clifford Hardy signal. Based on the distinctive and important local amplitude and local phase generated by the color Clifford Hardy signal, we propose five methods to identify the edges of color images with relation to a certain color. To prove the superiority of the offered methodologies, numerous comparative studies employing image quality assessment criteria are used. Specifically by using the multi-scale structure of the color Clifford Hardy signal, the proposed approaches are resistant to a variety of noises. In addition, a color optical flow detection method with anti-noise ability is provided as an example of application.Comment: 13 page

SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views

We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction approaches usually produce incomplete or distorted results. Moreover, their inability of generalizing to unseen new scenes impedes their application in practice. Contrarily, SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3). SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction. Moreover, several strategies are introduced to effectively leverage sparse views for high-quality reconstruction, including 1) a multi-level geometry reasoning framework to recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color blending scheme for more reliable color prediction; 3) a consistency-aware fine-tuning scheme to control the inconsistent regions caused by occlusion and noise. Extensive experiments demonstrate that our approach not only outperforms the state-of-the-art methods, but also exhibits good efficiency, generalizability, and flexibility.Comment: Project page: https://www.xxlong.site/SparseNeuS

Advances in nanomaterial-based targeted drug delivery systems

Nanomaterial-based drug delivery systems (NBDDS) are widely used to improve the safety and therapeutic efficacy of encapsulated drugs due to their unique physicochemical and biological properties. By combining therapeutic drugs with nanoparticles using rational targeting pathways, nano-targeted delivery systems were created to overcome the main drawbacks of conventional drug treatment, including insufficient stability and solubility, lack of transmembrane transport, short circulation time, and undesirable toxic effects. Herein, we reviewed the recent developments in different targeting design strategies and therapeutic approaches employing various nanomaterial-based systems. We also discussed the challenges and perspectives of smart systems in precisely targeting different intravascular and extravascular diseases