Results 121 to 130 of about 927,730 (287)

Evaluation of the sparse coding shrinkage noise reduction algorithm for the hearing impaired

, 2012
Although there are numerous single-channel noise reduction strategies to improve speech perception in a noisy environment, most of them can only improve speech quality but not improve speech intelligibility for normal hearing (NH) or hearing impaired (HI)
Sang, Jinqiu
core   +1 more source

Atomically Precise Ag11 and Ag12 Nanocluster‐Assembled 2D Materials for Memristive and Neuromorphic Functionality

Advanced Materials, EarlyView.
Two‐dimensional Ag11 and Ag12 cluster‐assembled materials (CAMs) are synthesized, offering atomically precise platforms with tunable electronic properties. The resulting materials exhibit robust memristive switching and neuromorphic response, demonstrating their promise for advanced nanoelectronic and memory applications.
Noohul Alam, Towhidur Rahaman, Céline Besnard, Arnulf Rosspeintner, Latevi Max Lawson Daku, Saood Khan, Soumya Jyoti Ray, Thomas Bürgi   +7 more
wiley   +1 more source

Volatile Memristive Devices With Tunable Temporal Dynamics For Event‐Based Sensing

Advanced Materials, EarlyView.
Tunable volatile memristive devices can serve various neural‐inspired tasks that require different time windows of information retention. The ionic‐based volatility of the presented Pt/a‐STO/TaOx/Ta device stack can be reproducibly and controllably tuned in multiple ways.
Dimitrios Spithouris, Johannes Hellwig, Hugh Greatorex, Clemens Wittberg, Panagiotis Koutsogiannis, César Magén, Elisabetta Chicca, Regina Dittmann   +7 more
wiley   +1 more source

Non-negative sparse coding [PDF]

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2003
Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non-negative matrix factorization.
openaire   +4 more sources

Bayesian modelling of music: algorithmic advances and experimental studies of shift-invariant sparse coding

, 2006
In order to perform many signal processing tasks such as classification,pattern recognition and coding, it is helpful to specify a signal model interms of meaningful signal structures. In general, designing such a modelis complicated and for many signals
Blumensath, Thomas
core  

Descriptors to Dynamics: A Materials and Device Perspective on in‐Materio Physical Reservoir Computing for Neuromorphic Edge Intelligence

Advanced Materials, EarlyView.
Intrinsic material dynamics are harnessed as computational resources for neuromorphic in‐materio physical reservoir computing. Defects, ionic motion, interfaces, percolation, geometry, and biasing shape transient states that provide fading memory, nonlinearity, and high‐dimensional projection for simple readout. A descriptor‐to‐dynamics framework links
Kshitij RB Singh, Alif Syafiq Kamarol Zaman, Muzhen Xu, Yuki Usami, Saptarshi Das, Hirofumi Tanaka   +5 more
wiley   +1 more source

Directional Latent Hybridization: Beyond Random Noise in Physics‐Informed Generative Inverse Design of Nonlinear Metamaterials

Advanced Materials Technologies, EarlyView.
A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn, Jun Young Choi, Sung‐Hoon Ahn   +2 more
wiley   +1 more source

Toward Perception‐Native Electronic Skin: Bio‐Inspired In‐/Near‐Sensor and Neuromorphic Computing for Humanoid Robots

Advanced Materials Technologies, EarlyView.
Dense tactile streams from across the humanoid body converge on collide in a central wiring and data bottleneck. By relocating computation closer to and then into the skin itself, near‐ and in‐sensor architectures, together with neuromorphic computing, chart a path toward perception‐native electronic skin, in which the conversion of stimulus into ...
Mijin Kim, Chang‐Jae Beak, Jiyoung Park, Youngdo Jung, Sunjong Oh, Sin‐Hyung Lee, Bo‐Yeon Lee   +6 more
wiley   +1 more source

Binary Sparse Coding for Interpretability

CoRR
Sparse autoencoders (SAEs) are used to decompose neural network activations into sparsely activating features, but many SAE features are only interpretable at high activation strengths. To address this issue we propose to use binary sparse autoencoders (BAEs) and binary transcoders (BTCs), which constrain all activations to be zero or one. We find that
Lucia Quirke, Stepan Shabalin, Nora Belrose   +2 more
openaire   +2 more sources

SMALLbox - An Evaluation Framework for Sparse Representations and Dictionary Learning Algorithms [PDF]

, 2010
International ...
Plumbley, Mark D., Plumbley, MD, Mark D. Plumbley, Davies, Matthew, Matthew E. P. Davies, Damnjanovic, Ivan, Davies, MEP, Ivan Damnjanovic, Damnjanovic, I   +8 more
core   +1 more source