EFFECTS OF DIFFERENT RESISTED SPRINT RUNNING METHODS ON STRIDE LENGTH, STRIDE FREQUENCY, AND CG VERTICAL OSCILLATION

Sprint velocity can be increased thanks to specific strength improvements (Korchemny, 1985). The training principle of specificity states that for a training session to be effective, it must maintain similar characteristics to the sport requirements (Sale, 2003). With the use of resisted sprint running methods, possible benefits are specific strength improvements and an increase in stride length (Faccioni, 1994). However, these methods have not been scientifically proven yet (Sheppard, 2004)

A novel weight-driven ATN-based SQL sentence generator to accommodate AI-based reinforcement learning

This paper presents a novel approach for generating SQL queries through a weight-driven framework using a modified ATN of ANTLR4’s runtime components. Our objective is to enhance ATN capabilities for SQL generation by incorporating the functionality to accommodate adaptive learning solutions. We successfully designed and implemented a system that assigns weights to ATN transitions, including token weight assignment when presented with multiple valid tokens to choose from whilst traversing set-transitions. These weights have interfaces for dynamic adjustments based on heuristics and user-defined strategies.Our methodology involves modifying ANTLR4’s core components to include weight management and traversal algorithms. We leverage heuristics to guide weight adjustments, addressing loop structures and recursive depth control in a system controlled by weights. Additionally, we establish mechanisms for weight persistence and optimization. Experimental evaluation using a simplistic SQL grammar demonstrates the effectiveness of our approach. We observe that weights can steer the parsing process towards desired outcomes, and that convergence occurs as the exploration-exploitation balance is optimized through parameter tuning. This research lays the groundwork for integrating reinforcement learning with our weight-driven ATN system. This holds promise for tackling complex challenges in structured data analysis that might not be readily apparent through human inspection alone. While our current work primarily focuses on heuristics, future efforts will explore the next stage of our research to further enhance the decision-making capabilities of our framework using reinforcement learning

Dynamic AI-IoT:enabling updatable AI models in ultra-low-power 5G IoT devices

This article addresses the challenge of integrating dynamic AI capabilities into ultralow-power (ULP) IoT devices, a critical necessity in the rapidly evolving landscape of 5G and potential 6G technologies. We introduce the Dynamic AI-IoT architecture, a novel framework designed to eliminate the need for cumbersome firmware updates. This architecture leverages Narrowband IoT (NB-IoT) to facilitate smooth cloud interactions and incorporates tailored firmware extensions for enabling dynamic interactions with Tiny Machine Learning (TinyML) models. A sophisticated memory management mechanism, grounded in memory alignment and dynamic AI operations resolution, is introduced to efficiently handle AI tasks. Empirical experiments demonstrate the feasibility of implementing a Dynamic AI-IoT system using ULP IoT devices on a 5G testbed. The results show model updates taking less than one second and an average inference time of approximately 46 ms

Design, Implementation, and Empirical Validation of a Framework for Remote Car Driving Using a Commercial Mobile Network

Despite the fact that autonomous driving systems are progressing in terms of their automation levels, the achievement of fully self-driving cars is still far from realization. Currently, most new cars accord with the Society of Automotive Engineers (SAE) Level 2 of automation, which requires the driver to be able to take control of the car when needed: for this reason, it is believed that between now and the achievement of fully automated self-driving car systems, there will be a transition, in which remote driving cars will be a reality. In addition, there are tele-operation-use cases that require remote driving for health or safety reasons. However, there is a lack of detailed design and implementation available in the public domain for remote driving cars: therefore, in this work we propose a functional framework for remote driving vehicles. We implemented a prototype, using a commercial car. The prototype was connected to a commercial 4G/5G mobile network, and empirical experiments were conducted, to validate the prototype’s functions, and to evaluate its performance in real-world driving conditions. The design, implementation, and empirical evaluation provided detailed technical insights into this important research and innovation area.This research was funded in part by the EU Horizon 2020 5G-PPP 5G-INDUCE project (“Open cooperative 5G experimentation platforms for the industrial sector NetApps”) under grant number H2020-ICT-2020-2/101016941, by the EU Horizon Europe INCODE project (“Programming platform for intelligent collaborative deployments over heterogeneous edge-IoT environments”) under grant number HORIZON-CL4-2022-DATA-01-03/101093069, and by the EU Horizon Europe project INCODE: programming platform for intelligent collaborative deployments over heterogeneous edge-IoT environments (HORIZON-CL4-2022-DATA-01-03/101093069)

Scalable software switch based service function chaining for 5G network slicing

Service Function Chaining (SFC) is a key enabler for network slicing in the Fifth-Generation (5G) mobile networks. Despite the ongoing standardisation activities and open source projects in addressing SFC, built-in 5G network support for SFC has not been sufficiently addressed on 5G Multi-tenant infrastructures. This paper proposes an Service Function Forwarder (SFF) and Classifier which is able to provide network slicing capabilities to the Service Data Plane in this type of infrastructures. The proposed prototype has been implemented as an extension of the popular Open Virtual Switch (OVS). The results of the empirical validation demonstrate that the proposed prototype is able to deal simultaneously with up to 8192 network slices with a maximum delay of 11 microseconds and 0% packet loss processing traffic at speeds up to 20 Gbps in a 5G architecture. The performance values achieved in this work are compliant with the 5G KPI expectation

Face verification algorithms for UAV applications:an empirical comparative analysis

Unmanned Aerial Vehicles (UAVs) are revolutionising diverse computer vision use case domains, from public safety surveillance to Search and Rescue (SAR), and other emergency management and disaster relief operations. The growing need for accurate face verification algorithms has prompted an exploration of synergies between UAVs and face verification. This promises cost-effective, wide-area, non-intrusive person verification. Real-world human-centric use cases such as a ”Drone Guard Angel” for vulnerable people can contribute to public safety management and offload significant police resources. These scenarios demand efficient face verification to distinguish correctly the end users for authentication, authorisation and customised services. This paper investigates the suitability of existing solutions, and analyses five state-of-the-art candidate face verification algorithms. Informed by the advantages and disadvantages of existing solutions, the paper proposes an extended dataset and a refined face verification pipeline. Subsequently, it conducts empirical evaluation of these algorithms using the proposed pipeline and dataset in terms of inference times and the distribution of the similarity indexes. Furthermore, this paper provides essential guidance for algorithm selection and deployment in UAV-based applications. Two candidate algorithms, ArcFace and FaceNet512, have emerged as the top performers. The choice between them will depend on the specific use case requirements