Results 91 to 100 of about 57,147 (259)

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   +2 more sources

From Fiber Bundles to Architected Membranes: Triply Periodic Minimal Surface Architectures for Biohybrid Artificial Lungs

Advanced Materials, EarlyView.
Additively manufactured triply periodic minimal surface (TPMS) membranes offer an architecture‐driven alternative to hollow fiber bundles in artificial lungs. Multiphysics simulations and endothelialized prototypes show that the 3D‐printable membrane architecture improves gas exchange, blood flow distribution, and hemocompatibility, enabling ...
Michael Pflaum, Kai P. Barbian, Florian Neuhaus, Gerrit Sitarz, Carolin Nölke, Sebastian V. Jansen, John Linkhorst, Lukas T. Hirschwald, Sebastian Brosch, Christian Certa, Ulrich Steinseifer, Matthias Wessling, Jutta Arens, Arjang Ruhparwar, Bettina Wiegmann   +14 more
wiley   +1 more source

Flax Composites With Improved Interfacial Strength Through Microbially Induced Mineral Precipitation

Advanced Materials, EarlyView.
A bio‐inspired biomineralization strategy introduces an additional hierarchy to flax fiber composites. By controlling microbe‐mediated mineral particle deposition through tuned salt concentrations, stress transfer within the natural fiber composite is enhanced.
Deniz Sayinbas, Ingo Nettersheim, Jeong‐Joo Oh, Marie‐Eve Aubin‐Tam, Julie Teuwen, Kunal Masania   +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

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

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source

Sparse Coding-Enabled Low-Fluence Multi-Parametric Photoacoustic Microscopy. [PDF]

IEEE Trans Med Imaging, 2022
Wang Z, Zhou Y, Hu S.
europepmc   +1 more source

Learning from sparse codes

2016 IEEE International Conference on Image Processing (ICIP), 2016
In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image ...
Karygianni, Sofia, Frossard, Pascal
openaire   +1 more source

Task‐Oriented Path Planning Towards Autonomous Sensor Network Deployment in Rainforest Canopies

Advanced Robotics Research, EarlyView.
An informative path planner for drone‐based deployment of wireless sensor networks in rainforest canopies is presented, addressing key challenges in large‐scale biodiversity monitoring. By integrating canopy surface detection with connectivity‐aware sampling under strict flight constraints, the approach enables efficient and reliable aerial sensor ...
Rita Santos Raminhos, Salua Hamaza
wiley   +1 more source

Distance Constraint Sparse/Group Sparse Coding for Automatic Image Labeling

工程科学与技术, 2016
:In order to bridge the semantic gap in automatic image labeling,and effectively leverage image features,two feature selection algorithms based on distance constraint sparse / group sparse coding (DCSC/DCGSC) were presented to solve the problem of image ...
臧淼, 徐惠民, 张永梅
doaj