1,720,984 research outputs found
Learning Pressure Sensor Drifts from Zero Deployability Budget
No full textThis work addresses the challenging problem of performing on-device online learning for a neural network to accomplish a regression task, under extreme memory and processing constraints, which a sensor typically presents. Specifically, we tackle the issue within the context of compensating the drifting behavior of the state of art LPS22DF absolute pressure sensor, operating under sporadic reference availability. The proposed solution is based on Gaussian Radial Basis Function networks and features a method for the allocation and removal of hidden neurons that dynamically updates the topology over the time. Additionally, we present an innovative adaptive distance threshold mechanism designed to ensure robust adaptivity of the model to sudden changes in input pattern distribution. The experimental assessment demonstrated significant error reductions ranging from 47.3% up to 93.4%, depending on reference availability frequency, when applied to sensors subject to different thermal stresses. The maximum memory footprint of 524 bytes (26 neurons) in all the performed experiments, proved the feasibility of performing the learning process directly within the sensor
In-Sensor Learning for Pressure Self-Calibration
No full textAn interesting research challenge for the TinyML community concerns the capability to achieve accurate online incremental learning for a regression task. This without relying on computational and memory demanding algorithms, such as back-propagation, while ensuring deployability on very assets-constrained devices. Pressure sensors are at the top of this challenge, because of the limited access to the ground truth reference, once deployed on tiny devices. This paper presents a viable, tiny solution to learn at any time how to provide compensations to the errors generated during the sensor lifetime, with minimal complexity while achieving satisfactory accuracy. The proposed solution is based on a Radial Basis Function network that is dynamically updated during online learning thanks to the sporadic availability of the reference values. The achieved results demonstrate robust performance, with an error reduction of up to 96% compared to the initial precision of the sensors and a remarkable accuracy even in scenarios with low availability over the time of the reference values. At maximum, only 6 nodes were required by the network corresponding to a memory footprint of 106 bytes
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
Erlotinib enhances antibody-dependent cellular cytotoxicity of wil-type erlotinib-sensitive NSCLC cell lines
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