Assessment of Countermovement Jump: What Should We Report?

The purpose of the present study was (i) to explore the reliability of the most commonly used countermovement jump (CMJ) metrics, and (ii) to reduce a large pool of metrics with acceptable levels of reliability via principal component analysis to the significant factors capable of providing distinctive aspects of CMJ performance. Seventy-nine physically active participants (thirty-seven females and forty-two males) performed three maximal CMJs while standing on a force platform. Each participant visited the laboratory on two occasions, separated by 24-48 h. The most reliable variables were performance variables (CV = 4.2-11.1%), followed by kinetic variables (CV = 1.6-93.4%), and finally kinematic variables (CV = 1.9-37.4%). From the 45 CMJ computed metrics, only 24 demonstrated acceptable levels of reliability (CV <= 10%). These variables were included in the principal component analysis and loaded a total of four factors, explaining 91% of the CMJ variance: performance component (variables responsible for overall jump performance), eccentric component (variables related to the breaking phase), concentric component (variables related to the upward phase), and jump strategy component (variables influencing the jumping style). Overall, the findings revealed important implications for sports scientists and practitioners regarding the CMJ-derived metrics that should be considered to gain a comprehensive insight into the biomechanical parameters related to CMJ performance.Ministry of Education, Science & Technological Development, Serbia 415-03-68/2022-14/20015

Effects of the Dielectric Environment on the Electrical Properties of Graphene

This thesis provides the study of graphene’s electrostatic interaction with the substrate surrounding it. Mathematical models based on current experimental configurations of graphene field-effect transistors (FET) are developed and analyzed. The conductivity and mobility of charge carriers in graphene are examined in the presence of impurities trapped in the substrate near graphene. The impurities encompass a wide range of possible structures and parameters, including different types of impurities, their distance from graphene, and the spatial correlation between them. Furthermore, we extend our models to analyze the influence of impurities on the fluctuations of the electrostatic potential and the charge carrier density in the plane of graphene. The results of our mathematical models are compared with current experimental results in the literature

TinyReptile: TinyML with Federated Meta-Learning

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge. Federated learning (FL) enables decentralized agents to jointly learn a global model without sharing sensitive local data. However, a common global model may not work for all devices due to the complexity of the actual deployment environment and the heterogeneity of the data available on each device. In addition, the deployment of TinyML hardware has significant computational and communication constraints, which traditional ML fails to address. Considering these challenges, we propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning, to collaboratively learn a solid initialization for a neural network (NN) across tiny devices that can be quickly adapted to a new device with respect to its data. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The evaluations on various TinyML use cases confirm a resource reduction and training time saving by at least two factors compared with baseline algorithms with comparable performance.Comment: Accepted by The International Joint Conference on Neural Network (IJCNN) 202

Event Processing and Stream Reasoning with ETALIS

This thesis presents the ETALIS Language for Events (ELE), a declarative rule-based language for Event Processing (EP) and Stream Reasoning (SR). ELE features a well-defined semantics, and provides strong event processing and reasoning capabilities. In this work we present ELE and show how its EP and SR capabilities have the potential to provide powerful real time intelligence. We provide a prototype implementation of the language, and present evaluation results for a few implemented scenarios