532 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Efficient wireless coverage of in-building environments with low electromagnetic impact
The city of tomorrow is a major integrating stake, which crosses a set of major broad spectrum domains. One of these areas is the instrumentation of this city and the ubiquity of the exchange of data, which will give the pulse of this city (sensors) and its breathing in a hyper-connected world within indoor and outdoor dense areas (data exchange, 5G and 6G).
Within this context, the proposed doctorate project has the objective to realize cost- and energy- effective, short-range communication systems for the capillary wireless coverage of in-door environments with low electromagnetic impact and for highly dense outdoor networks.
The result will be reached through the combined use of:
1) Radio over Fiber (RoF) Technology, to bring the Radio Frequency (RF) signal to the different areas to be covered.
2) Beamforming antennas to send in real time the RF power just in the direction(s) where it is really necessary
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Safe Halt as Fail-safe Concept for Automated Driving Systems
In order to guide a vehicle to the destination of a driving mission, various tasks shall be performed. These tasks include tactical and strategic planning of the driving mission and longitudinal and lateral vehicle motion control.
Driver assistance systems support a human vehicle driver in performing these tasks. If faults occur in these systems, the vehicle driver is informed of the system limitations and shall take over the control of the vehicle.
This fallback to a human driver is not an option in automated vehicles.
If system limitations occur in these vehicles, a automated fallback level shall take over vehicle control. The automated driving system shall therefore be fail-safe. Fail-safe means that when faults occur, the automated driving system no longer has any function to perform a driving mission, but shall maintain the vehicle in a safe state and transition the vehicle into a Minimal Risk Condition (MRC). For this purpose, a situation-dependent MRC is selected. It is characterized by the global MRC concerning the length of the maneuver and the residual risk of the MRC itself. For the research project UNICARagil, the concept Safe Halt is proposed. This concept is intended to satisfy the requirements mentioned above. In the state of the art, an evaluation of this concept had not been included. This missing evaluation is performed in this thesis. The concept relies on pre-planned implicit emergency trajectories generated by a planning module.
A unique concept feature is an independent environment perception system to ensure the Minimal Risk Maneuver (MRM) up to the MRC. Based on the pre-planned implicit emergency trajectory and the data of the independent environment perception system, Safe Halt plans trajectories up to the MRC.
Thus, with this concept, even in the presence of failures to the environment perception system and to the strategic and tactical planning of an automated driving system, the safe state can be maintained, and the vehicle can be transitioned to a MRC. A methodology is presented to evaluate the concept of Safe Halt. For this purpose, the fault tolerance regimes of an automated vehicle are defined. Next, a reference implementation for Safe Halt is provided. For this, requirements for a Safe Halt in a generic automated driving system are identified first. These are supplemented by specific requirements from the application in the UNICARagil automated driving system. Finally, concepts and a synthesized reference solution are created for a Safe Halt in the UNICARagil ADS. The solution is verified with test criteria and test cases. A final evaluation of the Safe Halt concept shows a high effectiveness for the size of the subset of fault combinations of an automated driving system for which Safe Halt enables a fail-safe property.
The requirements for Safe Halt are verified, and the specific requirements are met by the reference solution. The concept Safe Halt is thus suitable for an automated driving system to maintain a safe state. Validation of the concept in public road traffic is recommended
General Course Catalog [2022/23 academic year]
General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
Feebly Interacting Particles: FIPs 2022 workshop report
Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs
AI/ML Algorithms and Applications in VLSI Design and Technology
An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations
Feebly-interacting particles: FIPs 2022 Workshop Report
Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs
Feebly-interacting particles: FIPs 2022 workshop report
Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs
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