Deep Residual Shrinkage Networks for EMG-based Gesture Identification

This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN

The Impact of COVID-19 Pandemic on Undergraduate Students’ Interest in the STEM Field

The deadly consequences of COVID-19 have been well documented, as have the social, emotional, and cognitive effects. These sequelae extend to the educational system. Much less investigated have been the potential positive outcomes of the pandemic. Given that STEM education relies heavily on hands-on laboratory experiences, STEM students may have been especially impacted by pandemic-imposed remote instruction. We surveyed 392 students at one liberal arts college querying why they continue studying in STEM or leave the STEM disciplines. Because the literature indicates that people of color and those from lower socioeconomic groups were more negatively affected by COVID-19, we hypothesized that students from traditionally marginalized groups in STEM would report greater adverse educational consequences of the pandemic as well; however, this was not borne out by the findings. Across demographic groups, students reported negative impacts of COVID-19, although in a few areas we found that more traditionally “privileged” groups complained of more negative outcomes than traditionally marginalized students did. What was most novel and dramatic in our results were the positive outcomes of the “lockdown” reported by students. These beneficial results were in the areas of enhanced resilience, improved social relationships, greater opportunities, academic improvement, and better mental health. Our paper concludes with recommendations for addressing the negative outcomes of COVID-19 and remote instruction, and for taking advantage of the unexpected positive effects

COVID-19 is Not All Bad News: Negative and Surprisingly Positive Reports from College STEM Students and Implications for STEM Instruction

The negative educational consequences of COVID-19 are well documented. Much less investigated have been any potential positive outcomes of the pandemic. We surveyed 392 students at one college querying why they continue studying STEM or leave the STEM disciplines and about the effects of COVID-19 on their education. STEM students may have been especially impacted by pandemic-imposed remote instruction given STEM’s reliance on hands-on laboratory experiences. Because the literature indicates that people of color and those from lower socioeconomic groups were more negatively affected by COVID-19, we hypothesized that students from these groups would report greater adverse educational consequences of the pandemic; however, this was not borne out by our findings. Across demographic groups, students reported negative impacts of COVID-19, although in a few areas we found that more traditionally “privileged” groups complained of more negative outcomes than traditionally “marginalized” students did. Most novel and dramatic in our results were the positive outcomes of the “lockdown” reported by students in the areas of enhanced resilience, improved social relationships, greater opportunities, academic improvement, and better mental health. We conclude with recommendations for addressing the negative outcomes of COVID-19 and remote instruction, and for taking advantage of the unexpected positive effects

A universal optical modulator for synthetic topologically tuneable structured matter

Topologically structured matter, such as metasurfaces and metamaterials, have given rise to impressive photonic functionality, fuelling diverse applications from microscopy and holography to encryption and communication. Presently these solutions are limited by their largely static nature and preset functionality, hindering applications that demand dynamic photonic systems with reconfigurable topologies. Here we demonstrate a universal optical modulator that implements topologically tuneable structured matter as virtual pixels derived from cascading low functionality tuneable devices, altering the paradigm of phase and amplitude control to encompass arbitrary spatially varying retarders in a synthetic structured matter device. Our approach opens unprecedented functionality that is user-defined with high flexibility, allowing our synthetic structured matter to act as an information carrier, beam generator, analyser, and corrector, opening an exciting path to tuneable topologies of light and matter

Joint Efficient UAV Trajectory and Velocity Optimization for IoT Data Collection Using a New Projection Algorithm

Unmanned aerial vehicle (UAV)-assisted networking and communications are increasingly used in different applications, especially in the data collection of distributed Internet of Things (IoT) systems; its advantages include great flexibility and scalability. However, due to the UAV’s very limited battery capacity, the UAV energy efficiency has become a bottleneck for longer working time and larger area coverage. Therefore, it is critical to optimize the path and speed of the UAV with less energy consumption, while guaranteeing data collection under the workload and time requirements. In this paper, as a key finding, by analyzing the speed–power and the speed–energy relationships of UAVs, we found that there should be different speed selection strategies under different scenarios (i.e., fixed time or fixed distance), which can lead to much-improved energy efficiency. Moreover, we propose CirCo, a novel algorithm that jointly optimizes UAV trajectory and velocity for minimized energy consumption. CirCo is based on an original projection method, turning a 3D problem (GN locations and transmission ranges on the 2D plane, plus the minimum transmission time requirements on the temporal dimensions) into a 2D problem, which could help to directly find the feasible UAV crossing window, which greatly reduces the optimization complexity. Moreover, CirCo can classify the projected conditions to calculate the optimal path and speed schedule under each category, so that the energy consumption of each situation can be fine-regulated. The experiments demonstrate that CirCo can save as much as 54.3% of energy consumption and 62.9% of flight time over existing approaches

Joint Efficient UAV Trajectory and Velocity Optimization for IoT Data Collection Using a New Projection Algorithm

Unmanned aerial vehicle (UAV)-assisted networking and communications are increasingly used in different applications, especially in the data collection of distributed Internet of Things (IoT) systems; its advantages include great flexibility and scalability. However, due to the UAV’s very limited battery capacity, the UAV energy efficiency has become a bottleneck for longer working time and larger area coverage. Therefore, it is critical to optimize the path and speed of the UAV with less energy consumption, while guaranteeing data collection under the workload and time requirements. In this paper, as a key finding, by analyzing the speed–power and the speed–energy relationships of UAVs, we found that there should be different speed selection strategies under different scenarios (i.e., fixed time or fixed distance), which can lead to much-improved energy efficiency. Moreover, we propose CirCo, a novel algorithm that jointly optimizes UAV trajectory and velocity for minimized energy consumption. CirCo is based on an original projection method, turning a 3D problem (GN locations and transmission ranges on the 2D plane, plus the minimum transmission time requirements on the temporal dimensions) into a 2D problem, which could help to directly find the feasible UAV crossing window, which greatly reduces the optimization complexity. Moreover, CirCo can classify the projected conditions to calculate the optimal path and speed schedule under each category, so that the energy consumption of each situation can be fine-regulated. The experiments demonstrate that CirCo can save as much as 54.3% of energy consumption and 62.9% of flight time over existing approaches

Vectorial adaptive optics for advanced imaging systems

Vectorial adaptive optics (V-AO) is a cutting-edge technique extending conventional AO into the vectorial domain encompassing both polarization and phase feedback correction for optical systems. However, previous V-AO approaches focus on point correction. In this letter, we extend this AO approach into the imaging domain. We show how V-AO can benefit an aberrated imaging system to enhance not only scalar imaging but also the quality of vectorial information. Two important criteria, vectorial precision and uniformity are put forward and used in practice to evaluate the performance of the correction. These experimental validations pave the way for real-world imaging for V-AO technology and its applications

The effects of flipped classrooms on undergraduate pharmaceutical marketing learning: A clustered randomized controlled study.

BackgroundRecently, flipped classrooms (FCs) have gradually been used in Chinese higher education settings. However, few studies have focused on the effects of FCs on interdisciplinary curricula. The purpose of this study was to examine the impact of an FC on the engagement, performance, and perceptions of students and on teacher-student interaction in a pharmaceutical marketing course.DesignA clustered randomized controlled study was conducted, with 137 junior-year pharmacy undergraduates using an FC serving as the intervention group, in contrast to students using lecture-based learning (LBL) as the control group. Flanders' interaction analysis system (FIAS) was used to measure teacher-student interaction, and questionnaires regarding attitudes toward and satisfaction with the teaching model were administered.ResultsThe students in the FC group scored significantly higher than those in the LBL group (88.21±5.95 vs. 80.05±5.59, t = -8.08, p = 0.000) on pharmaceutical marketing. The multiple linear regression results showed that the FC model had a significant impact on student performance (β = 8.16, pConclusionCompared with LBL methods, implementing the FC model improved student performance, increased teacher-student interaction and generated positive student attitudes toward the experience. As an effective pedagogical model, it can also stimulate pharmacy students' learning interest and improve their self-learning abilities

Characterization of the physical origins of acoustic emission (AE) from natural fiber reinforced polymers (NFRPs) machining processes

Natural fiber reinforced polymers (NFRPs) are environmentally friendly and are receiving growing attention in the industry. However, the multi-scale structure of natural fibers and the random distribution of the fibers in the matrix material severely impede the machinability of NFRPs, and real-time monitoring is essential for quality assurance. This paper reports a synchronous in situ imaging and acoustic emission (AE) analysis of the NFRP machining process to connect the temporal features of AE to the underlying dynamics and process instability, all happen within milliseconds during the NFRP cutting. This approach allows directly observing the surface modification and chip formation from a high-speed camera (HSC) during NFRP cutting processes. The analysis of the HSC images suggests that the complex fiber structure and the random distribution introduce an unsteady, almost a freeze-and-release type motion pattern of the cutting tool with varying depths of cut at the machining interface. More pertinently, a prominent burst pattern of AE from time domain was found to emanate due to the sudden penetration of the tool into the surface of the NFRP workpiece (increasing the depth of cut), as well as a release motion of the tool from its momentary freeze position. These findings open the possibility of tracking AE signals to assess the effective specific energy and surface quality that are affected by these unsteady motion patterns