| 1 |
Unsupervised quantification of entity consistency between photos and text in real-world news ...
|
|
Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
|
|
BASE
|
|
Show details
|
|
|
| 2 |
COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets
|
|
|
|
In: ISSN: 1662-5137 ; Frontiers in Systems Neuroscience ; https://hal.archives-ouvertes.fr/hal-03318691 ; Frontiers in Systems Neuroscience, Frontiers, 2021, 15, pp.653975. ⟨10.3389/fnsys.2021.653975⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Online activation of L1 Danish orthography enhances spoken word recognition of Swedish
|
|
|
|
In: ISSN: 0332-5865 ; Nordic Journal of Linguistics ; https://hal-amu.archives-ouvertes.fr/hal-03283527 ; Nordic Journal of Linguistics, 2021, pp.1-19. ⟨10.1017/S0332586521000056⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Hand-gesture recognition based on EMG and event-based camera sensor fusion: a benchmark in neuromorphic computing
|
|
|
|
In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.archives-ouvertes.fr/hal-02617084 ; Frontiers in Neuroscience, Frontiers, 2020, pp.36 ; https://www.frontiersin.org/ (2020)
|
|
Abstract:
International audience ; Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and respond accordingly. The ability to discriminate human gestures can help in several applications such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and numbers of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN+MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of 5 gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chips constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20\% and 40\%, with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30x and 600x more efficient. The proposed work represents a new benchmark that moves the neuromorphic computing towards a real-world scenario.
|
|
Keyword:
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]; [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; EMG signal Processing; Event-based camera; hand-gesture recognition; Neuromorphic Engineering; Sensor Fusion; Spiking Neural Networks (SNN)
|
|
URL: https://hal.archives-ouvertes.fr/hal-02617084
|
|
BASE
|
|
Hide details
|
|
|
| 7 |
Neuroplasticity in Visual Word Recognition: An Exploration of Learning-Related Behavioural and Neural Changes ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 8 |
The temporal dynamics of first and second language processing: ERPs to spoken words in Mandarin-English bilinguals
|
|
|
|
In: Brain and Mind Institute Researchers' Publications (2020)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Traitement neuronal des voix et familiarité : entre reconnaissance et identification du locuteur
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
The Role of Surface and Underlying Forms When Processing Tonal Alternations in Mandarin Chinese: A Mismatch Negativity Study
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Challenges in Audio Processing of Terrorist-Related Data
|
|
|
|
In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02415176 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Challenges in Audio Processing of Terrorist-Related Data
|
|
|
|
In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02387373 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Does the prosodic emphasis of sentential context cause deeper lexical-semantic processing?
|
|
|
|
In: ISSN: 2327-3798 ; EISSN: 2327-3801 ; Language, Cognition and Neuroscience ; https://hal.univ-lille.fr/hal-01917002 ; Language, Cognition and Neuroscience, Taylor and Francis, 2019, 34, pp.29-42. ⟨10.1080/23273798.2018.1499945⟩ (2019)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Acoustic event, spoken keyword and emotional outburst detection
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Event Structure In Vision And Language
|
|
|
|
In: Publicly Accessible Penn Dissertations (2019)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Investigating the Electrophysiology of Long-Term Priming in Spoken Word Recognition
|
|
|
|
In: ETD Archive (2018)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Non-linguistic Vocalization Recognition Based on Convolutional, Long Short-Term Memory, Deep Neural Networks
|
|
Qiu, Liang. - : eScholarship, University of California, 2018
|
|
In: Qiu, Liang. (2018). Non-linguistic Vocalization Recognition Based on Convolutional, Long Short-Term Memory, Deep Neural Networks. UCLA: Electrical Engineering 0303. Retrieved from: http://www.escholarship.org/uc/item/1pz29229 (2018)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Abstract Concepts and Pictures of Real-World Situations Activate One Another.
|
|
|
|
In: Psychology Publications (2018)
|
|
BASE
|
|
Show details
|
|
|
|
|
