Academic conferences serve as a useful platform for researchers and educators to share their work and ideas. Upon the paper acceptance, authors need to register their papers, pay registration fees, and present their work at conferences. International conferences often involve multiple parties of users and cross-border payments with multiple channels that incur additional service charges. Conference organisers and reviewers typically contribute voluntarily without any monetary rewards. The emergence of Web 3.0 technology, leveraging the decentralized and secure nature of blockchain, presents an opportunity for this domain. This research creates a decentralized academic conference management system using Web 3.0 (ConfMan Web 3.0) and Rust programming language that simplifies cross-border payments on different channels by using cryptocurrencies instead of fiat currency, thereby reducing service charges. It aims to provide rewards, recognition of contributions, and consolidated historical records to all conference contributors, including authors, reviewers, programme chairs, and organising committees. The Solana blockchain is used to store conference- related data, and a web application is developed for ConfMan Web 3.0. Various testings are conducted to evaluate its performance. The findings highlight the potential of Web 3.0 technology in transforming the academic conference management landscape.

We propose a novel optimization framework for video object segmentation, given the initial annotations of objects in the keyframes of an input video sequence. In this work, video data is represented by a Markov Random Field model, and segmentation is achieved by finding the minimum graph cut label assignment. More specifically, we first create a bilateral representation of the input video sequence which reduces the size of the graph that the min-cut must operate on. We then introduce dynamic appearance models to learn the segmentation likelihoods, and the reliability of likelihoods is measured to identify false likelihoods that may cause segmentation errors. Thus, the model accurately describes changes in the object's appearance that have evolved over time. Furthermore, we augment spatial and temporal connections using a soft higher-order potential, ensuring long-range label consistency in the segmentation. We provide extensive analysis and evaluation with respect to the influence of each component of the framework through the ablation study. Experiments on three benchmark datasets (DAVIS 2016, YouTube-Objects and SegTrack v2) show that our method achieves competitive performance compared to state-of-the-art while having the order of magnitude faster runtime.

Background This paper presents a Tower Crane Intelligent Lifting Path Planner (TCILP) for the lifting task in the environments which are highly complex. The environment can be old industrial plants existing over many decades, or sites with tentative storage spaces. Often, these environments do not have workable digital models or are impractical to have 3D representations are impractical. Methods The current investigation introduces the cutting-edge technology of laser scanning to convert the real environments into point cloud format regarded as the virtualized version of the construction sites or plants. The challenge is to deal with the huge data set of point clouds of multiple scans in order to produce a complete virtualized model. The tower crane is also virtualized for the path planning purpose. A parallelized Genetic Algorithm is employed to address the intelligent path planning issue for the lifting task of tower cranes in complicated environments taking the advantage of Graphics Processing Unit technology, which has high computing performance yet low cost. Results Optimal lifting paths are generated in several seconds.

In the past few years, augmented reality (AR) has rapidly advanced and has been applied in different fields. One of the successful AR applications is the immersive and interactive serious games, which can be used for education and learning purposes. In this project, a prototype of an AR serious game is developed and demonstrated. Gamers utilize a head-mounted device and a vibrotactile feedback jacket to explore and interact with the AR serious game. Fourteen vibration actuators are embedded in the vibrotactile feedback jacket to generate immersive AR experience. These vibration actuators are triggered in accordance with the designed game scripts. Various vibration patterns and intensity levels are synthesized in different game scenes. This article presents the details of the entire software development of the AR serious game, including game scripts, game scenes with AR effects design, signal processing flow, behavior design, and communication configuration. Graphics computations are processed using the graphics processing unit in the system. The performance of the AR serious game prototype is evaluated and analyzed. The computation loads and resource utilization of normal game scenes and heavy computation scenes are compared. With 14 vibration actuators placed at different body positions, various vibration patterns and intensity levels can be generated by the vibrotactile feedback jacket, providing different real-world feedback. The prototype of this AR serious game can be valuable in building large-scale AR or virtual reality educational and entertainment games. Possible future improvements of the proposed prototype are also discussed in this article.

This paper discusses automatic path planning for tower crane lifting in highly complex environments to be digitized using point cloud representation. A mathematical optimization technique is developed to identify the lifting path with GPU accelerated massively parallel genetic algorithm. A continuous collision detection method is designed for real time application of collision avoidance during the crane lifting process.

This paper presents a novel GPU-based parallel algorithm to simplify triangular meshes. Existing GPU based methods usually produce simplified meshes with lower quality. This is generally because they put more emphasis on parallelism than mesh quality. After a thorough analysis of the existing methods, we propose a GPU based multiple-choice mechanism, which combines multiple-choice decimation and GPU-based parallel simplification, to balance the mesh quality and computational speed. As a result, our algorithm improves the quality of the simplified meshes and also achieves high speed-up provided by GPU. The experiments and the comparison with the prior art confirm the performance of the proposed algorithm.

Cooperative dual-crane lifting is an important but challenging process involved in heavy and critical lifting tasks. This paper considers the path planning for the cooperative dual-crane lifting. It aims to automatically generate optimal dual-crane lifting paths under multiple constraints, i.e., collision avoidance, coordination between the two cranes, and balance of the lifting target. Previous works often used oversimplified models for the dual-crane lifting system, the lifting environment, and the motion of the lifting target. They were thus limited to simple lifting cases and might even lead to unsafe paths in some cases. We develop a novel path planner for dual-crane lifting that can quickly produce optimized paths in complex 3-D environments. The planner has fully considered the kinematic structure of the lifting system. Therefore, it is able to robustly handle the nonlinear movement of the suspended target during lifting. The effectiveness and efficiency of the planner are enabled by three novel aspects: 1) a comprehensive and computationally efficient mathematical modeling of the lifting system; 2) a new multiobjective parallel genetic algorithm designed to solve the path planning problem; and 3) a new efficient approach to perform continuous collision detection for the dual-crane lifting target. The planner has been tested in complex industrial environments. The results show that the planner can generate dual-crane lifting paths that are easy for conductions and optimized in terms of costs for complex environments. Comparisons with two previous methods demonstrate the advantages of the planner, including safer paths, higher success rates, and the ability to handle general lifting cases.

We present a variational method for subdivision surface reconstruction from a noisy dense mesh. A new set of subdivision rules with continuous sharpness control is introduced into Loop subdivision for better modeling subdivision surface features such as semi-sharp creases, creases, and corners. The key idea is to assign a sharpness value to each edge of the control mesh to continuously control the surface features. Based on the new subdivision rules, a variational model with L1 norm is formulated to find the control mesh and the corresponding sharpness values of the subdivision surface that best fits the input mesh. An iterative solver based on the augmented Lagrangian method and particle swarm optimization is used to solve the resulting non-linear, non-differentiable optimization problem. Our experimental results show that our method can handle meshes well with sharp/semi-sharp features and noise.

An accurate and efficient clothoid approximation approach is presented in this paper using Bézier curves based on the minimization of curvature profile difference. Compared with existing methods, the proposed approach is able to guarantee higher order geometric continuity with smaller approximation error in terms of position, orientation, and curvature. The approximation scheme takes place in three stages. First, a subset of clothoids with specific winding angle constraints referred to as elementary clothoids is approximated using quintic Bézier curves. Then, a basic clothoid defined in the first quadrant is formulated, which is composed of a series of transformed elementary clothoids. An adaptive sampling stra-tegy is applied to ensure that the resulting Bézier segments are computed within a specified accuracy and all the required information can be obtained offline and stored in a lookup table. Finally, a general clothoid with arbitrary parameters can be conveniently approximated based on the lookup table through appropriate geometric transformations. A comparison with the recent circular interpolation and rational Bézier curve based approximation shows that the proposed approach is able to achieve equivalent or greater computational efficiency in most scenarios.

This paper presents a variational algorithm for feature-preserved mesh denoising. At the heart of the algorithm is a novel variational model composed of three components: fidelity, regularization and fairness, which are specifically designed to have their intuitive roles. In particular, the fidelity is formulated as an L1 data term, which makes the regularization process be less dependent on the exact value of outliers and noise. The regularization is formulated as the total absolute edge-lengthed supplementary angle of the dihedral angle, making the model capable of reconstructing meshes with sharp features. In addition, an augmented Lagrange method is provided to efficiently solve the proposed variational model. Compared to the prior art, the new algorithm has crucial advantages in handling large scale noise, noise along random directions, and different kinds of noise, including random impulsive noise, even in the presence of sharp features. Both visual and quantitative evaluation demonstrates the superiority of the new algorithm.

The placement of knot vector and the determination of control points are two fundamental issues in B-spline surface reconstruction. This paper presents a variational approach to construct B-spline surfaces from a set of data points. The approach finds the optimal placement of knots and control points simultaneously while most previous methods determine the knots heuristically or in a separate step. Moreover, different from most previous methods using least squares metric, our approach adapts L_1-norm with total variation (TV) as regularization in the fitting procedure, which enables the approach to handle both Gaussian noise and outliers in the same manner and is able to automatically optimize the placement of knot vector to faithfully reconstruct the sharp features. A numerical solver based on the augmented Lagrangian method is also proposed in the paper to efficiently solve the TV-L_1 optimization. Experimental results demonstrate the effectiveness and efficiency of the proposed variational B-spline surface reconstruction.

This paper reports our 3D reconstruction work of mouse brain cells from a confocal image stack. Gradients at volumetric points are used for boundary surface extraction and structure visualization from 3D volumetric confocal microscopic images. We present a method to automatically evaluate the suitability of gradients for 3D boundary identification. The approach developed is applied for 3D reconstruction of mouse brain cells from confocal image stack

Volume rendering is a computerized visualization technique that can be used to identify critical portions and specific regions of interest in various applications including medicine or biology. Localization and segmentations can be conducted and rendered dynamically such that navigation and operations within the volumetric dataset can be implemented. This paper describes our work on immersive VR image processing and visualization. A system is being developed with motion tracking and a pen-like stylus in this project in order to assist the bio-medical science research. This will provide a six degree-of-freedom 3D motion control that eases the control for volume manipulation and data extraction in a 3D environment. Together with a stereovision system, it will provide the user with a full 3D interactive environment in virtual reality (VR) for medical and cell biological applications

The construction of a smooth surface interpolating a mesh of arbitrary topological type is an important problem in many graphics applications. This paper presents a two-phase process, based on a topological modification of the control mesh and a subsequent Catmull-Clark subdivision, to construct a smooth surface that interpolates some or all of the vertices of a mesh with arbitrary topology. It is also possible to constrain the surface to have specified tangent planes at an arbitrary subset of the vertices to be interpolated. The method has the following features: 1) It is guaranteed to always work and the computation is numerically stable, 2) there is no need to solve a system of linear equations and the whole computation complexity is O(K) where K is the number of the vertices, and 3) each vertex can be associated with a scalar shape handle for local shape control. These features make interpolation using Catmull-Clark surfaces simple and, thus, make the new method itself suitable for interactive free-form shape design.

This paper presents the process of a motion control system design and implementation for a teleminulation system. The robotic manipulator/hand is a nonlinear system with coupled effects among the joints. By applying the frequency response method, the nonlinear system is modeled as decoupled linear ones, in which each joint is controlled independently. The experimental results show the feasibility of the modeling process. Based on the obtained transfer function of each joint, the digital PID controllers are designed. The motion control system for the telemanipulation system is then implemented using the software package MATLAB, Simulink, Real Time Workshop, xPC Target and a C/C++ compiler, and the hardware I/O boards, amplifiers, etc. The presented developing procedure shows a convenient way to implement a real time robotic control system.

Coronary heart disease (CHD) is one of the leading killers worldwide. Lipoprotein and apolipoprotein play an important role in developing CHDs. To tackle the challenging task of CHD lipoprotein and atherosclerotic research, collaborative work is crucial. With an interdiscipline in nature, such research involves bio-chemistry, cardiovascular biology, lipoprotein structure, fluorescent imaging, 3D modelling, data mining, etc. This project aims to create a Web based and integrated solution to support a multi-disciplinary knowledge discovery process for the study of cardiovascular and atherosclerotic biology. The emphasis of the project is to address (1) the sharing of cardiovascular clinical data; and (2) the system integration for CHD cardiovascular research. The system currently under development is mainly implemented on a PC Window platform with an intranet-centric design. An Apache HTTP server, a MySQL SQL server, Perl, and R Language, are employed for the system development independent to hardware platforms. The system offers easy information management and data sharing by multiple parties across Internet. Major datasets include lipoprotein phenotype/genotype and laser confocal images. A user-friendly interface is designed allowing users to deposit, retrieve, visualize and analyze the CHD information from different sites.