A novel voice coil motor-driven compliant micropositioning stage based on flexure mechanism

This paper presents a 2-degrees of freedom flexure-based micropositioning stage with a flexible decoupling mechanism. The stage is composed of an upper planar stage and four vertical support links to improve the out-of-plane stiffness. The moving platform is driven by two voice coil motors, and thus it has the capability of large working stroke. The upper stage is connected with the base through six double parallel four-bar linkages mechanisms, which are orthogonally arranged to implement the motion decoupling in the x and y directions. The vertical support links with serially connected hook joints are utilized to guarantee good planar motion with heavy-loads. The static stiffness and the dynamic resonant frequencies are obtained based on the theoretical analyses. Finite element analysis is used to investigate the characteristics of the developed stage. Experiments are carried out to validate the established models and the performance of the developed stage. It is noted that the developed stage has the capability of translational motion stroke of 1.8 mm and 1.78 mm in working axes. The maximum coupling errors in the x and y directions are 0.65% and 0.82%, respectively, and the motion resolution is less than 200 nm. The experimental results show that the developed stage has good capability for trajectory tracking

Fruitful Decades for Canthin-6-ones from 1952 to 2015:Biosynthesis, Chemistry, and Biological Activities

In this review, more than 60 natural canthin-6-one alkaloids and their structures are considered. The biosynthesis, efficient and classic synthetic approaches, and biological activities of canthin-6-one alkaloids, from 1952 to 2015, are discussed. From an analysis of their structural properties and an investigation of the literature, possible future trends for canthin-6-one alkaloids are proposed. The information reported will be helpful in future research on canthin-6-one alkaloids

Spike-Event X-ray Image Classification for 3D-NoC-Based Neuromorphic Pneumonia Detection

The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency

A Virtual Reality Whiteboard System for Remote Collaboration Using Natural Handwriting

The COVID-19 pandemic has increased the significance of remote collaboration. This study proposes a virtual reality (VR) whiteboard system that enables remote collaboration among multiple participants using natural handwriting. In total, three experiments were conducted to investigate, respectively, collaboration efficiency, user experience, and system delay. First, we compared the collaboration efficiency of the traditional whiteboard, the electronic whiteboard, and the proposed virtual reality whiteboard in a series of controlled experiments. It was discovered that the VR whiteboard significantly improves collaboration efficiency in comparison to the mouse-based electronic whiteboard and is comparable to the traditional whiteboard. Second, we assessed the user experience with a survey scale (questionnaires). The subsequent results demonstrate that the VR whiteboard provides a superior user experience in terms of efficiency, usability, etc., compared to the traditional whiteboard. We also measured an end-to-end latency of approximately 115 milliseconds, which is sufficient for remote collaboration

A Virtual Reality Whiteboard System for Remote Collaboration Using Natural Handwriting

The COVID-19 pandemic has increased the significance of remote collaboration. This study proposes a virtual reality (VR) whiteboard system that enables remote collaboration among multiple participants using natural handwriting. In total, three experiments were conducted to investigate, respectively, collaboration efficiency, user experience, and system delay. First, we compared the collaboration efficiency of the traditional whiteboard, the electronic whiteboard, and the proposed virtual reality whiteboard in a series of controlled experiments. It was discovered that the VR whiteboard significantly improves collaboration efficiency in comparison to the mouse-based electronic whiteboard and is comparable to the traditional whiteboard. Second, we assessed the user experience with a survey scale (questionnaires). The subsequent results demonstrate that the VR whiteboard provides a superior user experience in terms of efficiency, usability, etc., compared to the traditional whiteboard. We also measured an end-to-end latency of approximately 115 milliseconds, which is sufficient for remote collaboration

Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System

Recent years have witnessed a rapid growth of Artificial Intelligence (AI) in biomedical fields. However, an accurate and secure system for pneumonia detection and diagnosis is urgently needed. We present the optimization and implementation of a collaborative learning algorithm for an AI-Enabled Real-time Biomedical System (AIRBiS), where a convolution neural network is deployed for pneumonia (i.e., COVID-19) image classification. With augmentation optimization, the federated learning (FL) approach achieves a high accuracy of 95.66%, which outperforms the conventional learning approach with an accuracy of 94.08%. Using multiple edge devices also reduces overall training time

A data-driven method for feature assessment of historical settlements: A case study of Northeast Hubei, China

Formulating criteria for the assessment system of historic settlements is challenging due to complex geographical conditions and evaluator knowledge limitations, leading to subjective bias in the assessment process. To address this issue, this study proposes a data-driven method for assessing the features of historical settlements to carry out scientific and refined assessment and result analysis. Focusing on Northeast Hubei as the study area, this paper selects 3 historical settlements for validation and analysis. The results of the study show that (1) the data-driven method expands the methodological chain of assessing historical settlement features, and improves the assessment efficiency and scientificity of the assessment results by applying it to the new assessment process; (2) Through comparing the assessment results of the validation cases and data samples, the study establishes a comprehensive quantitative ranking of the assessment of historical settlement features and identifies the main influencing factors, thus enhancing the precision of result analysis; (3) By comparing the resulting assessment framework with the current assessment system, this study confirms the advantages of the proposed framework in identifying nuanced features and aligning with geographical conditions, thereby verifying the effectiveness of the data-driven method

Simulation and Experiment of Gas-Solid Flow in a Safflower Sorting Device Based on the CFD-DEM Coupling Method

To study the movement characteristics and separation mechanism of safflower petals and their impurities under the action of airflow and lower the impurity rate in the cleaning operation process, integration of computational fluid dynamics (CFD) and discrete element method (DEM) codes was performed to study the motion and sorting behavior of impurity particles and safflower petals under different airflow inclination angles, dust removal angles and inlet airflow velocities by establishing a true particle model. In this model, the discrete particle phase was applied by the DEM software, and the continuum gas phase was described by the ANSYS Fluent software. The Box-Behnken experimental design with three factors and three levels was performed, and parameters such as inlet airflow velocity, airflow inclined angle, and dust remover angle were selected as independent variables that would influence the cleaning impurity rate and the cleaning loss rate. A mathematical model was established, and then the effects of various parameters and their interactions were analyzed. The test results show that the cleaning effect is best when the inlet airflow velocity is 7 m/s, the airflow inclined angle is 0°, and the dust remover angle is 25°. Confirmatory tests showed that the average cleaning impurity rate and cleaning loss rate were 0.69% and 2.75%, respectively, which dropped significantly compared with those from previous optimization. An experimental device was designed and set up; the experimental results were consistent with the simulation results, indicating that studying the physical behavior of safflower petals-impurity separation in the airflow field by using the DEM-CFD coupling method is reliable. This result provides a basis for follow-up studies of separation and cleaning devices for lightweight materials such as safflower petals