1,721,149 research outputs found
An alternative interpretation of the behaviour law of matter by means of a generalized markovian stochastic process
No full textAn alternative interpretation of the behavior law of matter by means of a generalized stochastic process
No full textDiscrepancy between discrete models and continuous theoretical ones is a common concern with the behavior laws of matter. We propose an alternative frame in which the transition from a discrete to a continuous model becomes very natural. A statistical description of matter laws is given in this contexte
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A2Event: A Micro-Watt Programmable Frequency-Time Detector for Always-On Energy-Neutral Sensing
No full textInternet of Things (IoT) sensors are becoming more and more intelligence and unobtrusive, surrounding us in everyday life. A key requirement for those devices is to be “always-on” performing “smart” detection to catch only the events of interest when they occur. A recent and trend is to design low power circuit to extract features close to the sensors without an analog-digital conversion. The goal is to achieve place-and-forget deployment with perpetual operation: this poses the challenge of achieving micro-watt power consumption of the always-on analog event detector and of harvesting energy from environmental sources, to supply the smart sensor. This paper presents A2Event (Analog to Event), a microwatt programmable pattern recognition circuit for analog sensors, with up to 128 simultaneous time-frequency features exploiting mixed-signal low power design, which is able to generate an event when a targeted pattern is recognized. Moreover, the paper proposes a self-sustaining always-on smart audio detector that combines A2Event with energy harvesting to detect events of interests perpetually. Experimental results show that A2Event consumes only 26.89 μW in always-on mode, during the time-frequency feature-extraction, while the whole system consumes only 63 μW during pattern recognition including the power for a commercial MEMS microphone and the energy harvesting subsystem. We demonstrate that the whole smart sensor operates perpetually when powered with a small form factor flexible photovoltaic panel in indoor lighting conditions, achieving a detection accuracy of 100% in the detection of two different audio streams
Loi de comportement et processus stochastique
No full textLe but de ce travail est de présenter un nouveau contexte mathématique qui permette de modéliser d’une façon naturelle le comportement mécanique de la matière. Dans un tel modèle, il est très facile d’effectuer la transition entre la structure microscopique et le comportement macroscopique. Ce point de vue met en oeuvre une théorie mathématique alternative appelée RFS (Radically Frequentist Statistics) basée sur un concept idéalisé d’une très grande séquence finie de résultats
Energy-Positive Activity Recognition - From Kinetic Energy Harvesting to Smart Self-Sustainable Wearable Devices
No full textWearable, intelligent, and unobtrusive sensor nodes that monitor the human body and the surrounding environment have the potential to create valuable data for preventive human-centric ubiquitous healthcare. To attain this vision of unobtrusiveness, the smart devices have to gather and analyze data over long periods of time without the need for battery recharging or replacement. This article presents a software-configurable kinetic energy harvesting and power management circuit that enables self-sustainable wearable devices. By exploiting the kinetic transducer as an energy source and an activity sensor simultaneously, the proposed circuit provides highly efficient context-aware control features. Its mixed-signal nano-power context awareness allows reaching energy neutrality even in energy-drought periods, thus significantly relaxing the energy storage requirements. Furthermore, the asynchronous sensing approach also doubles as a coarse-grained human activity recognition frontend. Experimental results, using commercial micro-kinetic generators, demonstrate the flexibility and potential of this approach: the circuit achieves a quiescent current of 57 nA and a maximum load current of 300 mA, delivered with a harvesting efficiency of 79%. Based on empirically collected motion data, the system achieves an energy surplus of over 232 mJ per day in a wrist-worn application while executing activity recognition at an accuracy of 89% and a latency of 60 s
Model-based design for self-sustainable sensor nodes
Full text linkLong-term and maintenance-free operation is a critical feature for large-scale deployed battery-operated sensor nodes. Energy harvesting (EH) is the most promising technology to overcome the energy bottleneck of today’s sensors and to enable the vision of perpetual operation. However, relying on fluctuating environmental energy requires an application-specific analysis of the energy statistics combined with an in-depth characterization of circuits and algorithms, making design and verification complex. This article presents a model-based design (MBD) approach for EH-enabled devices accounting for the dynamic behavior of components in the power generation, conversion, storage, and discharge paths. The extension of existing compact models combined with data-driven statistical modeling of harvesting circuits allows accurate offline analysis, verification, and validation. The presented approach facilitates application-specific optimization during the development phase and reliable long-term evaluation combined with environmental datasets. Experimental results demonstrate the accuracy and flexibility of this approach: the model verification of a solar-powered wireless sensor node shows a determination coefficient () of 0.992, resulting in an energy error of only -1.57 % between measurement and simulation. Compared to state-of-practice methods, the MBD approach attains a reduction of the estimated state-of-charge error of up to 10.2 % in a real-world scenario. MBD offers non-trivial insights on critical design choices: the analysis of the storage element selection reveals a 2–3 times too high self-discharge per capacity ratio for supercapacitors and a peak current constrain for lithium-ion polymer batteries
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