Research on Hand Action Pattern Recognition of Bionic Limb Based on Surface Electromyography

Hands are important parts of a human body. It is not only the main tool for people to engage in productive labor, but also an important communication tool. When the hand moves, the human body produces a kind of signal named surface electromyography (sEMG), which is a kind of electrophysiological signal that accompanies muscle activity. It contains a lot of information about human movement consciousness. The bionic limb is driven by multi-degree-freedom control, which is got by converting the recognition result and this can meet the urgent need of people with disabilities for autonomous operation. A profound study of hand action pattern technology based on sEMG signals can achieve the ability of the bionic limb to distinguish the hand action fast and accurately. From the perspective of the pattern recognition of the bionic limb, this paper discussed the human hand action pattern recognition technology of sEMG. By analyzing and summarizing the current development of human hand movement recognition, the author proposed a bionic limb schema based on artificial neural network and the improved DT-SVM hand action recognition system. According to the research results, it is necessary to expand the type and total amount of hand movements and gesture recognition, in order to adapt to the objective requirements of the diversity of hand action patterns in the application of the bionic limb

Linking adverbials in English

Linking adverbials play an important role in textual cohesion. Applied linguistic studies (e.g. Altenberg & Tapper, 1998; Bolton, Nelson, & Hung, 2002; Chen, 2006) have shown that second language learners have difficulty in using linking adverbials appropriately. Biber, Johansson, Leech, Conrad, and Finegan (1999) is to date the only corpus-based reference grammar book which covers all three aspects of usage patterns of linking adverbials, i.e., form, meaning and position. But as the book provides a very comprehensive grammar, there is not room for a detailed account of all three aspects of usage patterns of a small grammar category such as linking adverbials. Also, the previous literature does not agree upon the terms used to refer to linking adverbials and the linguistic elements referred to by linking adverbials and other related terms. This two-stage study examined research questions of what linking adverbials are and how they are used in different registers using both a qualitative and quantitative approach. In Stage I, a random sample of 67 texts of a total of over 100,000 words from the Wellington Corpora of Written and Spoken New Zealand English (WWC and WSC) were manually analysed, which provided coverage of the target five registers of written academic prose, academic lectures, conversation, written news and broadcast news. In Stage II, the most frequent linking adverbials identified in written registers in Stage I were automatically searched in larger corpora, i.e., the whole WWC, BNC and COCA. The intonation unit was adopted as the analysis unit for spoken data and sound files were needed in deciding intonation units. Thus, automatic search for patterns in spoken data was not viable in Stage II. This study suggests a new definition of linking adverbials and explains the difference between linking adverbials and conjunctions. This study then provides a detailed account of usage patterns of not only the form, meaning and position of linking adverbials but also patterns of types of each form, meaning and position in five registers. Such usage patterns are compared and explained among different written and spoken registers and in different social settings. It is worth noting that a register-specific meaning categorization system and a semantic-pragmatic continuum are suggested in this study. The aforementioned findings contribute to theories of the nature of linking adverbials, and have implications for second language learning and teaching in EAP and ESP contexts and future corpus-based pedagogical grammar studies

Talbot effect for the Manakov System on the torus

In this paper, the Talbot effect for the multi-component linear and nonlinear systems of the dispersive evolution equations on a bounded interval subject to periodic boundary conditions and discontinuous initial profiles is investigated. Firstly, for a class of two-component linear systems satisfying the dispersive quantization conditions, we discuss the fractal solutions at irrational times. Next, the investigation to nonlinear regime is extended, we prove that, for the concrete example of the Manakov system, the solutions of the corresponding periodic initial-boundary value problem subject to initial data of bounded variation are continuous but nowhere differentiable fractal-like curve with Minkowski dimension $3/2$ at irrational times. Finally, numerical experiments for the periodic initial-boundary value problem of the Manakov system, are used to justify how such effects persist into the multi-component nonlinear regime. Furthermore, it is shown in the nonlinear multi-component regime that the interplay of different components may induce subtle different qualitative profile between the jump discontinuities, especially in the case that two nonlinearly coupled components start with different initial profile

New Revival Phenomena for Bidirectional Dispersive Hyperbolic Equations

In this paper, the dispersive revival and fractalization phenomena for bidirectional dispersive equations on a bounded interval subject to periodic boundary conditions and discontinuous initial profiles are investigated. Firstly, we study the periodic initial-boundary value problem of the linear beam equation with step function initial data, and analyze the manifestation of the revival phenomenon for the corresponding solution at rational times. Next, we extend the investigation to periodic initial-boundary value problems of more general bidirectional dispersive equations. We prove that, if the initial functions are of bounded variation, the dynamical evolution of such periodic problems depend essentially upon the large wave number asymptotics of the associated dispersion relations. Integral polynomial or asymptotically integral polynomial dispersion relations produce dispersive revival/fractalization rational/irrational dichotomies, whereas those with non-polynomial growth result in fractal profiles at all times. Finally, numerical experiments, in the concrete case of the nonlinear beam equation, are used to demonstrate how such effects persist into the nonlinear regime.Comment: 28 pages, 11 figure

Exception and rule: how the ‘Standard Method’ fails to govern England’s housing requirement

The assessment of housing requirement has become an important feature of planning practice in many contexts. The calculative practices by which the level of housing requirement is determined represent important instruments that seek to discipline markets to produce sufficient dwellings to meet the assessed level of requirement. However, behind the purportedly objective assessment of housing requirement are assumptions that pertain to a political imperative to deliver more dwellings. In this article we seek to reveal these political principles by unpacking the ‘Standard Method’ for the assessment of housing requirement which has applied in England since February 2019.</jats:p

Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)

The massive amounts of data generated by camera sensors motivate data processing inside pixel arrays, i.e., at the extreme-edge. Several critical developments have fueled recent interest in the processing-in-pixel-in-memory paradigm for a wide range of visual machine intelligence tasks, including (1) advances in 3D integration technology to enable complex processing inside each pixel in a 3D integrated manner while maintaining pixel density, (2) analog processing circuit techniques for massively parallel low-energy in-pixel computations, and (3) algorithmic techniques to mitigate non-idealities associated with analog processing through hardware-aware training schemes. This article presents a comprehensive technology-circuit-algorithm landscape that connects technology capabilities, circuit design strategies, and algorithmic optimizations to power, performance, area, bandwidth reduction, and application-level accuracy metrics. We present our results using a comprehensive co-design framework incorporating hardware and algorithmic optimizations for various complex real-life visual intelligence tasks mapped onto our P2M paradigm

Spatiotemporal Fusion of Land Surface Temperature Based on a Convolutional Neural Network

© 1980-2012 IEEE. Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) 0.971]