Evidence map›Paper›PMID 38610358›Full record

ArticleSensors (Basel, Switzerland)2024

Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications.

Jorge I Canales-Verdial, Jamison R Wagner, Landon A Schmucker, Mark Wetzel, Philippe Proctor, Merlin Carson, Jian Meng, Nathan J Withers, Charles Thomas Harris, John J Nogan and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Effects of Gamma Irradiation on the Artificial Spiking Neurons Based on NbOSmall (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jorge I Canales-VerdialDepartment of Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Jamison R WagnerDepartment of Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Landon A SchmuckerDepartment of Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.ORCID 0009-0008-4404-9937
Mark WetzelDepartment of Nuclear Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Philippe ProctorDepartment of Engineering & Computer Science, Portland State University, Portland, OR 97201, USA.
Merlin CarsonDepartment of Engineering & Computer Science, Portland State University, Portland, OR 97201, USA.ORCID 0000-0002-2460-4496
Jian MengDepartment of Engineering & Computer Science, Portland State University, Portland, OR 97201, USA.
Nathan J WithersDepartment of Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Charles Thomas HarrisCenter for Integrated Nanotechnologies, Albuquerque, NM 87123, USA.ORCID 0000-0003-4946-7181
John J NoganCenter for Integrated Nanotechnologies, Albuquerque, NM 87123, USA.
Denise B WebbCenter for Integrated Nanotechnologies, Albuquerque, NM 87123, USA.
Adam A HechtDepartment of Nuclear Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Christof TeuscherDepartment of Engineering & Computer Science, Portland State University, Portland, OR 97201, USA.
Marek OsińskiDepartment of Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Payman Zarkesh-HaDepartment of Electrical & Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.ORCID 0000-0002-0571-9212

Funding

Defense Threat Reduction Agency HDTRA1-18-1-0009
6 · The paper itself

Abstract

A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector-matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.

Indexed as

memristor arraysneuromorphic computingradioisotope classificationradionuclide detectionsource localization

Identifiers

PMID38610358
PMCPMC11014144

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.