scholarly journals Trigger primitive generation algorithm in the CMS barrel muon chambers during HL-LHC

2021 ◽  
Vol 16 (12) ◽  
pp. C12009
Author(s):  
N. Trevisani

Abstract This contribution presents an update on the Analytical Method (AM) algorithm for trigger primitive (TP) generation in the CMS Drift Tube (DT) chambers during the High Luminosity LHC operation (HL-LHC or LHC phase 2). The algorithm has been developed and validated both in software with an emulation approach, and through hardware implementation tests. The algorithm is mainly divided into the following steps: a grouping (pattern recognition) step that finds the path of a given muon, a fitting step to extract the track parameters (position and bending angle), and a correlation step that matches the information from the different super-layers and with signal from the resistive plate chambers. Agreement between the software emulation and the firmware implementation has been verified using different data samples, including a sample of real muons collected during 2016 data taking. In this contribution, an update of the grouping step using a pseudo-Bayes classifier will be discussed.

2021 ◽  
Vol 11 (11) ◽  
pp. 4722
Author(s):  
Botan Wang ◽  
Xiaolong Chen ◽  
Yi Wang ◽  
Dong Han ◽  
Baohong Guo ◽  
...  

This work reports the latest observations on the behavior of two Multigap Resistive Plate Chambers (MRPC) under wide high-luminosity exposures, which motivate the development and in-beam test of the sealed MRPC prototype assembled with low-resistive glass. The operation currently being monitored, together with previous simulation results, shows the impact of gas pollution caused by avalanches in gas gaps, and the necessity to shrink the gas-streaming volume. With the lateral edge of the detector sealed by a 3D-printed frame, a reduced gas-streaming volume of ~170 mL has been achieved for a direct gas flow to the active area. A high-rate test of the sealed MRPC prototype shows that, ensuring a 97% efficiency and 70 ps time resolution, the sealed design results in a stable operation current behavior at a counting rate of 3–5 kHz/cm2. The sealed MRPC will become a potential solution for future high luminosity applications.


2019 ◽  
Vol 153 ◽  
pp. 79-83 ◽  
Author(s):  
Wooseok Choi ◽  
Kibong Moon ◽  
Myonghoon Kwak ◽  
Changhyuck Sung ◽  
Jongwon Lee ◽  
...  

2011 ◽  
Vol 215 (1) ◽  
pp. 143-146 ◽  
Author(s):  
B. Bittner ◽  
J. Dubbert ◽  
S. Horvat ◽  
M. Kilgenstein ◽  
O. Kortner ◽  
...  
Keyword(s):  

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Ya Lin ◽  
Zhongqiang Wang ◽  
Xue Zhang ◽  
Tao Zeng ◽  
Liang Bai ◽  
...  

Abstract An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.


1996 ◽  
Author(s):  
Nickolay N. Evtikhiev ◽  
Boris N. Onyky ◽  
Dmitry V. Repin ◽  
Igor B. Scherbakov ◽  
Rostislav S. Starikov ◽  
...  

Author(s):  
C. H. CHEN

The area of statistical pattern recognition has experienced about three decades of continued progress. In this paper, early development of the area is examined by reviewing the activities of the first decade. Recent progress in major topics of statistical pattern recognition including classification rules, feature extraction, contextual analysis and cluster analysis, are then reviewed. Particular notes are made on the important contributions of the late Prof. Fu, which have profound impact on the development of statistical pattern recognition. The future outlook of the area is indeed bright as many applications making use of statistical pattern recognition are explored to seek more reliable recognition performance. Efforts on efficient hardware implementation and less costly recognition software packages as well as the combined statistical-structural approach are also likely to increase significantly.


Author(s):  
Tobias Sombra ◽  
Rose Santini ◽  
Emerson Morais ◽  
Walmir Couto ◽  
Alex Zissou ◽  
...  

Quantitative evaluation of a dataset can play an important role in pattern recognition of technical-scientific research involving behavior and dynamics in social networks. As an example, are the adaptive feature weighting approaches by naive Bayes text algorithm. This work aims to present an exploratory data analysis with a quantitative approach that involves pattern recognition using the Mendeley research network; to identify logics given the popularity of document access. To better analyze the results, the work was divided into four categories, each with three subcategories, that is, five, three, and two output classes. The name for these categories came up due to data collection, which also presented documents with open access, dismembering proceedings, and journals for two more categories. As a result, the performance for the test examples showed a lower error rate related to the subcategory two output classes in the criterion of popularity by using the naive Bayes algorithm in Mendeley.


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