Results 101 to 110 of about 2,367,946 (319)

Bioinspired Adaptive Sensors: A Review on Current Developments in Theory and Application

Advanced Materials, EarlyView.
This review comprehensively summarizes the recent progress in the design and fabrication of sensory‐adaptation‐inspired devices and highlights their valuable applications in electronic skin, wearable electronics, and machine vision. The existing challenges and future directions are addressed in aspects such as device performance optimization ...
Guodong Gong, You Zhou, Qingxiu Li, Wenyu Zhao, Sunyingyue Geng, Hangfei Li, Yan‐Bing Leng, Shirui Zhu, Guanglong Ding, Yongbiao Zhai, Ziyu Lv, Ye Zhou, Su‐Ting Han   +12 more
wiley   +1 more source

Deep learning approaches for neural decoding across architectures and recording modalities

Briefings Bioinform., 2020
Decoding behavior, perception or cognitive state directly from neural signals is critical for brain-computer interface research and an important tool for systems neuroscience.
J. Livezey, Joshua I. Glaser
semanticscholar   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Neural decoding in blocked and shuffled sequences. [PDF]

, 2019
(A) Neural decoding accuracies were analyzed across time points along the stimulus duration and stimulus presentations in blocked and shuffled sequences.
David S. Vicario (7326128), Efe Soyman (7326125)   +1 more
core   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Examining high level neural representations of cluttered scenes [PDF]

, 2010
Humans and other primates can rapidly categorize objects even when they are embedded in complex visual scenes (Thorpe et al., 1996; Fabre-Thorpe et al., 1998). Studies by Serre et al., 2007 have shown that the ability of humans to detect animals in brief
Serre, Thomas, Embark, Hamdy, Freiwald, Winrich, Meyers, Ethan, Poggio, Tomaso, Kreiman, Gabriel   +5 more
core  

Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces

APL Bioengineering
Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications ...
Yun-Ting Kuo, Han-Lin Wang, Bo-Wei Chen, Ching-Fu Wang, Yu-Chun Lo, Sheng-Huang Lin, Po-Chuan Chen, You-Yin Chen   +7 more
doaj   +1 more source

MIND: Model Independent Neural Decoder [PDF]

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019
Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismatch. Recently proposed Deep learning based neural decoders address this problem by leveraging a model-free approach via gradient-based training.
Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan   +3 more
openaire   +2 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Controlled beam search for neural machine translation using subword units leveraging phrase-based statistical machine translation outputs

Discover Computing
The decoding phase is a crucial component in machine translation systems, alongside the creation of the model. Beam search is the most commonly used algorithm for decoding in these systems.
Emre Satir, Hasan Bulut
doaj   +1 more source