Search results for "computers"
showing 10 items of 3243 documents
Simultaneous segmentation and beam-hardening correction in computed microtomography of rock cores
2013
We propose a post-reconstruction correction procedure for the beam-hardening artifact that neither requires knowledge of the X-ray spectrum nor of the attenuation coefficients in multi-mineral geologic samples. The beam-hardening artifact in polychromatic X-ray computer tomography (CT) hampers segmentation of the phase assemblage in geologic samples. We show that in cylindrically shaped samples like rock cores, the X-ray attenuation value for a single phase depends mainly on the distance from the center of the cylinder. This relationship could be easily extracted from the CT data for every phase and used to infer the presence of these phases at all positions in the sample. Our new approach …
Effect of the high-level trigger for detecting long-lived particles at LHCb.
2022
Long-lived particles (LLPs) show up in many extensions of the Standard Model, but they are challenging to search for with current detectors, due to their very displaced vertices. This study evaluated the ability of the trigger algorithms used in the Large Hadron Collider beauty (LHCb) experiment to detect long-lived particles and attempted to adapt them to enhance the sensitivity of this experiment to undiscovered long-lived particles. A model with a Higgs portal to a dark sector is tested, and the sensitivity reach is discussed. In the LHCb tracking system, the farthest tracking station from the collision point is the scintillating fiber tracker, the SciFi detector. One of the challenges i…
Special Issue of Journal of Manufacturing Processes on Advancing Manufacturing Processes Research at NAMRC 46
2018
During the refereeing process of papers submitted to NAMRC 46 this year, nine high-quality papers have been selected and fast-tracked to a special issue of Journal of Manufacturing Processes (JMP) entitled “Advancing Manufacturing Processes Research at NAMRC 46”. The selection of the fast-track papers was based on authors’ preferences, quality of the papers, reviewers’ recommendations, Track Chairs’ picks, pre-selection by the Chair of NAMRI/SME Scientific Committee, and final approval by the JMP Editor. The nine papers published in JMP are therefore excluded from the Proceedings of NAMRC 46 in Procedia Manufacturing. Nevertheless, these papers are presented in person at NAMRC 46. Details o…
Special Issue of Journal of Manufacturing Processes on New Trends in Manufacturing Processes Research
2019
Special issue of journal of manufacturing processes on new trends in manufacturing processes research
Error-Based Interference Detection in WiFi Networks
2017
In this paper we show that inter-technology interference can be recognized by commodity WiFi devices by monitoring the statistics of receiver errors. Indeed, while for WiFi standard frames the error probability varies during the frame reception in different frame fields (PHY, MAC headers, payloads) protected with heterogeneous coding, errors may appear randomly at any point during the time the demodulator is trying to receive an exogenous interfering signal. We thus detect and identify cross-technology interference on off-the-shelf WiFi cards by monitoring the sequence of receiver errors (bad PLCP, bad PCS, invalid headers, etc.) and develop an Artificial Neural Network (ANN) to recognize t…
DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
2021
Abstract Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named De…
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
2019
Abstract Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we…
Preamble Transmission Prediction for mMTC Bursty Traffic : A Machine Learning based Approach
2020
The evolution of Internet of things (IoT) towards massive IoT in recent years has stimulated a surge of traffic volume among which a huge amount of traffic is generated in the form of massive machine type communications. Consequently, existing network infrastructure is facing challenges when handling rapidly growing traffic load, especially under bursty traffic conditions which may more often lead to congestion. By proactively predicting the occurrence of congestion, we can implement necessary means and conceivably avoid congestion. In this paper, we propose a machine learning (ML) based model for predicting successful preamble transmissions at a base station and subsequently forecasting th…
Artificial Neural Networks in Sports: New Concepts and Approaches
2001
Artificial neural networks are tools, which - similar to natural neural networks - can learn to recognize and classify patterns, and so can help to optimise context depending acting. These abilitie...
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…