Producing alternating gait on uncoupled feline hindlimbs: Muscular unloading rule on a biomimetic robot

Studies on decerebrate walking cats have shown that phase transition is strongly related to muscular sensory signals at limbs. To further investigate the role of such signals terminating the stance phase, we developed a biomimetic feline platform. Adopting link lengths and moment arms from an Acinonyx jubatus, we built a pair of hindlimbs connected to a hindquarter and attached it to a sliding strut, simulating solid forelimbs. Artificial pneumatic muscles simulate biological muscles through a control method based on EMG signals from walking cats (Felis catus). Using the bio-inspired muscular unloading rule, where a decreasing ground reaction force triggers phase transition, stable walking on a treadmill was achieved. Finally, an alternating gait is possible using the unloading rule, withstanding disturbances and systematic muscular changes, not only contributing to our understanding on how cats may walk, but also helping develop better legged robots.The authors acknouledge the Japanese Research Grant KAKENHI Kiban 23220004 and 25540117.This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.1080/01691864.2013.87049

Producing alternating gait on uncoupled feline hindlimbs: muscular unloading rule on a biomimetic robot

Studies on decerebrate walking cats have shown that phase transition is strongly related to muscular sensory signals at limbs. To further investigate the role of such signals terminating the stance phase, we developed a biomimetic feline platform. Adopting link lengths and moment arms from an Acinonyx jubatus, we built a pair of hindlimbs connected to a hindquarter and attached it to a sliding strut, simulating solid forelimbs. Artificial pneumatic muscles simulate biological muscles through a control method based on EMG signals from walking cats (Felis catus). Using the bio-inspired muscular unloading rule, where a decreasing ground reaction force triggers phase transition, stable walking on a treadmill was achieved. Finally, an alternating gait is possible using the unloading rule, withstanding disturbances and systematic muscular changes, not only contributing to our understanding on how cats may walk, but also helping develop better legged robots.The authors acknouledge the Japanese Research Grant KAKENHI Kiban 23220004 and 25540117.This is the author accepted manuscript. The final version is available from Taylor & Francis via http://dx.doi.org/10.1080/01691864.2013.87049

Infrared and Visible Image Fusion Based on Oversampled Graph Filter Banks

The infrared image (RI) and visible image (VI) fusion method merges complementary information from the infrared and visible imaging sensors to provide an effective way for understanding the scene. The graph filter bank-based graph wavelet transform possesses the advantages of the classic wavelet filter bank and graph representation of a signal. Therefore, we propose an RI and VI fusion method based on oversampled graph filter banks. Specifically, we consider the source images as signals on the regular graph and decompose them into the multiscale representations with M-channel oversampled graph filter banks. Then, the fusion rule for the low-frequency subband is constructed using the modified local coefficient of variation and the bilateral filter. The fusion maps of detail subbands are formed using the standard deviation-based local properties. Finally, the fusion image is obtained by applying the inverse transform on the fusion subband coefficients. The experimental results on benchmark images show the potential of the proposed method in the image fusion applications

An online Hebbian learning rule that performs Independent Component Analysis

Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but only higher moments of its amplitude distribution. Moreover, they require some preprocessing of the data (whitening) so as to remove second order correlations. In this paper, we are interested in understanding the neural mechanism responsible for solving ICA. We present an online learning rule that exploits delayed correlations in the input. This rule performs ICA by detecting joint variations in the firing rates of pre- and postsynaptic neurons, similar to a local rate-based Hebbian learning rule

Architecting specifications for test case generation

The Specification and Description Language (SDL) together with its associated tool sets can be used for the generation of Tree and Tabular Combined Notation (TTCN) test cases. Surprisingly, little documentation exists on the optimal way to specify systems so that they can best be used for the generation of tests. This paper, elaborates on the different tool supported approaches that can be taken for test case generation and highlights their advantages and disadvantages. A rule based SDL specification style is then presented that facilitates the automatic generation of tests

Fronto-cerebellar connectivity mediating cognitive processing speed

Processing speed is an important construct in understanding cognition. This study was aimed to control task specificity for understanding the neural mechanisms underlying cognitive processing speed. Forty young adult subjects performed attention tasks of two modalities (auditory and visual) and two levels of task rules (compatible and incompatible). Block-design fMRI captured BOLD signals during the tasks. Thirteen regions of interest were defined with reference to publicly available activation maps for processing speed tasks. Cognitive speed was derived from task reaction times, which yielded six sets of connectivity measures. Mixed-effect LASSO regression revealed six significant paths suggestive of a cerebello-frontal network predicting the cognitive speed. Among them, three are long range (two fronto-cerebellar, one cerebello-frontal), and three are short range (fronto-frontal, cerebello-cerebellar, and cerebello-thalamic). The long-range connections are likely to relate to cognitive control, and the short-range connections relate to rule-based stimulus-response processes. The revealed neural network suggests that automaticity, acting on the task rules and interplaying with effortful top-down attentional control, accounts for cognitive speed

Semiotics, Analogical Legal Reasoning, and the Cf. Citation: Getting Our Signals Uncrossed

The Bluebook\u27s introductory citation signals are essential to effective legal discourse. The choice of signal can influence not only the interpretation of cited cases, but also the path of the law. In this Article, Professor Ira Robbins examines one commonly used signal: the cf. After exploring its semiotic function, he details the multitude of ways in which this signal has been used and misused. He argues that lawyers\u27 and judges\u27 careless use of the cf. leads to confusing and often incoherent developments in the law, and concludes by proposing a precise working definition for this irksome, but potentially powerful, citation signal

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres

Exploring adaptation & self-adaptation in autonomic computing systems

This panel paper sets out to discuss what self-adaptation means, and to explore the extent to which current autonomic systems exhibit truly self-adaptive behaviour. Many of the currently cited examples are clearly adaptive, but debate remains as to what extent they are simply following prescribed adaptation rules within preset bounds, and to what extent they have the ability to truly learn new behaviour. Is there a standard test that can be applied to differentiate? Is adaptive behaviour sufficient anyway? Other autonomic computing issues are also discussed