宽带低频放大器的设计

文中讨论了宽带低频放大器的设计。宽带网络采用有源低通滤波器和有源高通滤波器组合而成。在电路设计中,有源滤波电路和放大电路采用同一放大模块,这样简化了电路,缩小了体积,提高了产品的可靠性。通过分析低通滤波器和高通滤波器的设计过程,给出了试验数据、滤波器波形和宽带低频放大器电路图,再经过调整得到较好的通频带波形。该放大器在10～85kHz这个范围内,有较好的增益平坦度和稳定的性能,电压增益为60dB,满足设计要求。     

On the fragility of skilled performance: What governs choking under pressure?

Experiments 1-2 examined generic knowledge and episodic memories of putting in novice and expert golfers. Impoverished episodic recollection of specific putts among experts indicated that skilled putting is encoded in a procedural form that supports performance without the need for step-by-step attentional control. According to explicit monitoring theories of choking, such proceduralization makes putting vulnerable to decrements under pressure. Experiments 3-4 examined choking and the ability of training conditions to ameliorate it in putting and a nonproceduralized alphabet arithmetic skill analogous to mental arithmetic. Choking occurred in putting but not alphabet arithmetic. In putting, choking was unchanged by dual-task training but eliminated by self-consciousness training. These findings support explicit monitoring theories of choking and the popular but infrequently tested belief that attending to proceduralized skills hurts performance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A matrix gradient algorithm for identification of parameterized time‐varying parameters

This paper considers the problem of estimating time‐varying parameters which can be parameterized by a series of arbitrary known basis functions. It is shown that this problem is equivalent to the observer design problem for a “matrix” dynamic system. A “matrix” gradient algorithm, which mimics the well‐known “vector” gradient algorithm, is proposed to estimate the unknown matrix. The contribution of this paper is to show that convergence of the proposed matrix algorithm is guaranteed by the persistent excitations of both the regressor and the basis functions. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Cyanobacterial blooms: statistical models describing risk factors for national-scale lake assessment and lake management

Cyanobacterial toxins constitute one of the most high risk categories of waterborne toxic biological substances. For this reason there is a clear need to know which freshwater environments are most susceptible to the development of large populations of cyanobacteria. Phytoplankton data from 134 UK lakes were used to develop a series of Generalised Additive Models and Generalised Additive Mixed Models to describe which kinds of lakes may be susceptible to cyanobacterial blooms using widely available explanatory variables. Models were developed for log cyanobacterial biovolume. Water colour and alkalinity are significant explanatory variables and retention time and TP borderline significant (R²-adj = 21.9%). Surprisingly, the models developed reveal that nutrient concentrations are not the primary explanatory variable; water colour and alkalinity were more important. However, given suitable environments (low colour, neutral-alkaline waters), cyanobacteria do increase with both increasing retention time and increasing TP concentrations, supporting the observations that cyanobacteria are one of the most visible symptoms of eutrophication, particularly in warm, dry summers. The models can contribute to the assessment of risks to public health, at a regional- to national level, helping target lake monitoring and management more cost-effectively at those lakes at the highest risk of breaching World Health Organisation guideline levels for cyanobacteria in recreational waters. The models also inform restoration options available for reducing cyanobacterial blooms, indicating that, in the highest risk lakes (alkaline, low colour lakes), risks can generally be lessened through management aimed at reducing nutrient loads and increasing flushing during summer.     

Stepping over obstacles with humanoid robots

The wide potential applications of humanoid robots require that the robots can walk in complex environments and overcome various obstacles. To this end, we address the problem of humanoid robots stepping over obstacles in this paper. We focus on two aspects, which are feasibility analysis and motion planning. The former determines whether a robot can step over a given obstacle, and the latter discusses how to step over, if feasible, by planning appropriate motions for the robot. We systematically examine both of these aspects. In the feasibility analysis, using an optimization technique, we cast the problem into global optimization models with nonlinear constraints, including collision-free and balance constraints. The solutions to the optimization models yield answers to the possibility of stepping over obstacles under some assumptions. The presented approach for feasibility provides not only a priori knowledge and a database to implement stepping over obstacles, but also a tool to evaluate and compare the mobility of humanoid robots. In motion planning, we present an algorithm to generate suitable trajectories of the feet and the waist of the robot using heuristic methodology, based on the results of the feasibility analysis. We decompose the body motion of the robot into two parts, corresponding to the lower body and upper body of the robot, to meet the collision-free and balance constraints. This novel planning method is adaptive to obstacle sizes, and is, hence, oriented to autonomous stepping over by humanoid robots guided by vision or other range finders. Its effectiveness is verified by simulations and experiments on our humanoid platform HRP-2.     

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images

Objectives

To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.

Materials and methods

The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects.

Results

The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively.

Conclusion

Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

     

Auxetic Cardiac Patches with Tunable Mechanical and Conductive Properties toward Treating Myocardial Infarction

An auxetic conductive cardiac patch (AuxCP) for the treatment of myocardial infarction (MI) is introduced. The auxetic design gives the patch a negative Poisson's ratio, providing it with the ability to conform to the demanding mechanics of the heart. The conductivity allows the patch to interface with electroresponsive tissues such as the heart. Excimer laser microablation is used to micropattern a re‐entrant honeycomb (bow‐tie) design into a chitosan‐polyaniline composite. It is shown that the bow‐tie design can produce patches with a wide range in mechanical strength and anisotropy, which can be tuned to match native heart tissue. Further, the auxetic patches are conductive and cytocompatible with murine neonatal cardiomyocytes in vitro. Ex vivo studies demonstrate that the auxetic patches have no detrimental effect on the electrophysiology of both healthy and MI rat hearts and conform better to native heart movements than unpatterned patches of the same material. Finally, the AuxCP applied in a rat MI model results in no detrimental effect on cardiac function and negligible fibrotic response after two weeks in vivo. This approach represents a versatile and robust platform for cardiac biomaterial design and could therefore lead to a promising treatment for MI.     

Evaluating the effectiveness of blended learning using the ARCS model

While the educational disruption caused by the Covid-19 pandemic underscores the importance of blended learning in higher education, research on the effectiveness of blended learning is still inconclusive. Drawing from the motivational design model of the ARCS (i.e., attention, relevance, confidence, and satisfaction), this study attempts to fill the gap to evaluate effectiveness of blended learning from a multi-dimensional perspective. Participants were randomly assigned into three experimental groups (i.e., face-to-face, pure online, and blended). A questionnaire survey was administered in each group after the trial courses. The data was analysed by using the one-way ANOVA with post hoc tests. The results showed that blended learning outperformed pure online learning in enhancing students' attention, confidence, and satisfaction perceptions. Additionally, blended learning had a higher level of satisfaction perception than face-to-face learning. Follow-up interviews were also conducted to provide an in-depth understanding of how blended learning motivated students during the learning process. Considering that blended learning may become a new normal in higher education after the Covid-19 pandemic, the findings of the present study provide evidences to support the effectiveness of the blended learning approach in addressing students' motivational needs.