2,060 research outputs found

    Radiotherapy dosimetry with ultrasound contrast agents

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    Radiotherapy dosimetry with ultrasound contrast agents

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    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Sensitivity of NEXT-100 detector to neutrinoless double beta decay

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    Nesta tese estúdiase a sensibilidade do detector NEXT-100 á desintegración dobre beta sen neutrinos. Existe un gran interese na busca desta desintegración xa que podería respostar preguntas fundamentais en física de neutrinos. O detector constitúe a terceira fase do experimento NEXT, colaboración na que se desenrolou esta tese. A continuación inclúese un resumo de cada un dos capítulos nos que se divide a tese. Comézase introducindo o marco teórico e experimental nas seccións Física de neutrinos, A busca da desintegración dobre beta sen neutrinos e O experimento NEXT. Posteriormente descríbense a parte principal do análise da tese en Simulación do detector, Procesamento de datos e Sensibilidade do detector NEXT-100

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Trusted Provenance with Blockchain - A Blockchain-based Provenance Tracking System for Virtual Aircraft Component Manufacturing

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    The importance of provenance in the digital age has led to significant interest in utilizing blockchain technology for tamper-proof storage of provenance data. This thesis proposes a blockchain-based provenance tracking system for the certification of aircraft components. The aim is to design and implement a system that can ensure the trustworthy, tamper-resistant storage of provenance documents originating from an aircraft manufacturing process. To achieve this, the thesis presents a systematic literature review, which provides a comprehensive overview of existing works in the field of provenance and blockchain technology. After obtaining strategies to utilize blockchain for the storage of provenance data on the blockchain, a system was designed to meet the requirements of stakeholders in the aviation industry. The thesis utilized a systematic approach to gather requirements by conducting interviews with stakeholders. The system was implemented using a combination of smart contracts and a graphical user interface to provide tamper-resistant, traceable storage of relevant data on a transparent blockchain. An evaluation based on the requirements identified during the requirement engineering process found that the proposed system meets all identified requirements. Overall, this thesis offers insight into a potential application of blockchain technology in the aviation industry and provides a valuable resource for researchers and industry professionals seeking to leverage blockchain technology for provenance tracking and certification purpose

    R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics

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    Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous adaptation of Deep Reinforcement Learning (DRL) models in dynamic environments. This research is fundamentally motivated by the need to understand and address the challenges of on-device real-time DRL, which involves balancing timing and algorithm performance under memory constraints, as exposed through our extensive empirical studies. This intricate balance requires co-optimizing two pivotal parameters of DRL training -- batch size and replay buffer size. Configuring these parameters significantly affects timing and algorithm performance, while both (unfortunately) require substantial memory allocation to achieve near-optimal performance. This paper presents R^3, a holistic solution for managing timing, memory, and algorithm performance in on-device real-time DRL training. R^3 employs (i) a deadline-driven feedback loop with dynamic batch sizing for optimizing timing, (ii) efficient memory management to reduce memory footprint and allow larger replay buffer sizes, and (iii) a runtime coordinator guided by heuristic analysis and a runtime profiler for dynamically adjusting memory resource reservations. These components collaboratively tackle the trade-offs in on-device DRL training, improving timing and algorithm performance while minimizing the risk of out-of-memory (OOM) errors. We implemented and evaluated R^3 extensively across various DRL frameworks and benchmarks on three hardware platforms commonly adopted by autonomous robotic systems. Additionally, we integrate R^3 with a popular realistic autonomous car simulator to demonstrate its real-world applicability. Evaluation results show that R^3 achieves efficacy across diverse platforms, ensuring consistent latency performance and timing predictability with minimal overhead.Comment: Accepted by RTSS 202

    Potential of machine learning/Artificial Intelligence (ML/AI) for verifying configurations of 5G multi Radio Access Technology (RAT) base station

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    Abstract. The enhancements in mobile networks from 1G to 5G have greatly increased data transmission reliability and speed. However, concerns with 5G must be addressed. As system performance and reliability improve, ML and AI integration in products and services become more common. The integration teams in cellular network equipment creation test devices from beginning to end to ensure hardware and software parts function correctly. Radio unit integration is typically the first integration phase, where the radio is tested independently without additional network components like the BBU and UE. 5G architecture and the technology that it is using are explained further. The architecture defined by 3GPP for 5G differs from previous generations, using Network Functions (NFs) instead of network entities. This service-based architecture offers NF reusability to reduce costs and modularity, allowing for the best vendor options for customer radio products. 5G introduced the O-RAN concept to decompose the RAN architecture, allowing for increased speed, flexibility, and innovation. NG-RAN provided this solution to speed up the development and implementation process of 5G. The O-RAN concept aims to improve the efficiency of RAN by breaking it down into components, allowing for more agility and customization. The four protocols, the eCPRI interface, and the functionalities of fronthaul that NGRAN follows are expressed further. Additionally, the significance of NR is described with an explanation of its benefits. Some benefits are high data rates, lower latency, improved spectral efficiency, increased network flexibility, and improved energy efficiency. The timeline for 5G development is provided along with different 3GPP releases. Stand-alone and non-stand-alone architecture is integral while developing the 5G architecture; hence, it is also defined with illustrations. The two frequency bands that NR utilizes, FR1 and FR2, are expressed further. FR1 is a sub-6 GHz frequency band. It contains frequencies of low and high values; on the other hand, FR2 contains frequencies above 6GHz, comprising high frequencies. FR2 is commonly known as the mmWave band. Data collection for implementing the ML approaches is expressed that contains the test setup, data collection, data description, and data visualization part of the thesis work. The Test PC runs tests, executes test cases using test libraries, and collects data from various logs to analyze the system’s performance. The logs contain information about the test results, which can be used to identify issues and evaluate the system’s performance. The data collection part describes that the data was initially present in JSON files and extracted from there. The extraction took place using the Python code script and was then fed into an Excel sheet for further analysis. The data description explains the parameters that are taken while training the models. Jupyter notebook has been used for visualizing the data, and the visualization is carried out with the help of graphs. Moreover, the ML techniques used for analyzing the data are described. In total, three methods are used here. All the techniques come under the category of supervised learning. The explained models are random forest, XG Boost, and LSTM. These three models form the basis of ML techniques applied in the thesis. The results and discussion section explains the outcomes of the ML models and discusses how the thesis will be used in the future. The results include the parameters that are considered to apply the ML models to them. SINR, noise power, rxPower, and RSSI are the metrics that are being monitored. These parameters have variance, which is essential in evaluating the quality of the product test setup, the quality of the software being tested, and the state of the test environment. The discussion section of the thesis explains why the following parameters are taken, which ML model is most appropriate for the data being analyzed, and what the next steps are in implementation
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