1,721,057 research outputs found
Carbon for sensing devices
No full textThis book reveals why carbon is playing such an increasingly prominent role as a sensing material. The various steps that transform a raw material in a sensing device are thoroughly presented and critically discussed. The authors deal with all aspects of carbon-based sensors, starting from the various hybridization and allotropes of carbon, with specific focus on micro and nanosized carbons (e.g., carbon nanotubes, graphene) and their growth processes. The discussion then moves to the role of functionalization and the different routes to achieve it. Finally, a number of sensing applications in various fields are presented, highlighting the connection with the basic properties of the various carbon allotropes. Readers will benefit from this book’s bottom-up approach, which starts from the local bonding in carbon solids and ends with sensing applications, linking the local hybridization of carbon atoms and its modification by functionalization to specific device performance. This book is a must-have in the library of any scientist involved in carbon based sensing application. • Provides comprehensive coverage of carbon for sensing devices, from molecular bonding and its modification by functionalization to device application; • Discusses all forms of carbon for sensing devices, including carbon nanotubes and graphene, and explains applications to numerous fields; • Includes coverage of the most sophisticated and up to date fabrication methodologies
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
A 0.07 mm^2 Asynchronous Logic CMOS Pulsed Receiver Based on Radio Events Self-Synchronization
No full textThis paper presents an ultra-low-power radio receiver implemented only with CMOS logic gates used as basic building blocks and proves its operation. The self-timed duty-cycled system is self-synchronized with the input radio signal, runs a noise-robust baseband detection and does not require any reference besides power supply. Based on S-OOK modulation, the 350-450 MHz digital radio RX occupies an area of 0.07 mm 2 in a 130 nm RFCMOS technology and achieves a 0.1% sensitivity of -63 dBm at 95 kbps, 380 MHz center frequency and 40 μW active power consumption at 1.1 V power supply. At 1.0 V it achieves -62 dBm sensitivity and 33 μW active power at ~ 0.1% error rate. The compact receiver, whose architecture is parametric and technology scalable, suits energy harvested and miniaturized biomedical applications. The paper also presents the potential advantages of asynchronous logic pulse radio and introduces an ad-hoc VHDL model demonstrating RTL-/gate-level accurate error-rate predictions capabilities based on digital simulation only, i.e., without requiring electrical-level co-simulation
Translating node of Ranvier currents to extraneural electrical fields: a flexible FEM modeling approach
Full text linkSimulations of electroneurogram recording could help find the optimal set of electrodes and algorithms for selective neural recording. However, no flexible methods are established for selective neural recording as for neural stimulation. This paper proposes a method to couple a compartmental and a FEM nerve model, implemented in NEURON and COMSOL, respectively, to translate Node of Ranvier currents into extraneural electric fields. The study simulate ex-vivo experimental conditions, and the method allows flexibility in electrode geometries and nerve topologies. This model has been made available in a public repository4. So far, the model behavior complies with available experimental results and expectations from literature. There is good agreement in terms of signal amplitude and waveform, and computational times are acceptable, leaving room for flexible simulation studies complementary to animal tests
Preliminary study of pilot stress and mental workload monitoring through physiological signals
Full text linkThe aviation industry is moving towards single-pilot operations due to increased operating expenses and a shortage of pilots. The necessity of developing a digital cockpit assistant leads to discovering methods to assess the stress and mental workload of pilots. This study used twenty-eight healthy volunteers to conduct preliminary computerised cognitive tasks while recording their physiological data for PPG, EDA, and temperature under four different stress and workload situations. The results highlight how they are sensible to a binary classification between a relaxed and more cognitively demanding condition
On the impact of the stem electrical impedance in neural network algorithms for plant monitoring applications
Full text linkSmart agriculture offers an environmental-friendly path with respect to unsustainable farming that depletes the nutrients in the soil leading to a persistent degradation of ecosystems caused by population growth. Artificial Intelligence (AI) can help mitigate this issue by predicting plant health status to reduce the use of chemicals and optimize water usage. This paper proposes a custom framework to train neural networks and a comparison among different models to point out the impact and the importance of the stem electrical impedance in addition to environmental parameters for plant monitoring applications. In particular, the paper demonstrates how stem electrical impedance improves the accuracy of the proposed neural network application for plant status classification. The data set is composed of electrical impedance spectra and environmental data acquired on four tobacco plants for a two-month-long experiment. In this paper, we describe the acquisition system architecture, the feature composition of the data set, a general overview of the developed framework, and the training of the neural networks showing the different results considering both the stem impedance and the environmental parameters
A latchup topology to investigate novel particle detectors.
No full textHere the latchup effect is described as a novel approach to detect and read out particles by means of a solid-state device exploiting latchup topology. The paper first describes the state-of-the-art of the project and its development over the latest years, then the present and future studies are proposed. An elementary cell composed of two transistors connected in a thyristor structure is shown. A first prototype uses MOS transistors, resulting an even more promising and challenging configuration than that obtained via bipolar transistors. A second version of the circuit exploits a commercial SiC MESFET as sensing device. As the MOS transistors are widely used at present in microelectronics, a latchup topology is proposed as a novel structure for future applications in particle detection, amplification of signal sensors and radiation monitoring
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