Results 121 to 130 of about 864,269 (299)

Sparse Regression Codes: Recent Results and Future Directions

, 2020
-Sparse Superposition or Sparse Regression codes were recently introduced by Barron and Joseph for communication over the AWGN channel. The code is defined in terms of a design matrix; codewords are linear combinations of subsets of columns of the matrix.
Ramji Venkataramanan, Sekhar Tatikonda
core  

Sparse regression codes for multi-terminal source and channel coding

, 2012
—We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regres-sion and are called Sparse Superposition or Sparse Regression codes ...
Ramji Venkataramanan, Sekhar Tatikonda
core   +1 more source

Some remarks on sparse graph codes for packet loss recovery

, 2007
—In this paper we provide some novel design techniques for sparse-graph-based erasure correcting codes. The paper is focused on codes based on sparse graph belonging to the category of the so-called generalized low-density parity-check (G-LDPC) codes.
G. Liva, S. Scalise, M. Berioli, CHIANI, MARCO   +3 more
core   +1 more source

Millimeter-wave Human Security Imaging Based on Frequency-domain Sparsity and Rapid Imaging Sparse Array Architecture

Leida xuebao, 2018
This paper examines the processing of millimeter-wave imaging data based on sparse sampling and sparse array design for the rapid imaging of human security data.
Tian He, Li Daojing, Qi Chunchao
doaj   +1 more source

Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction

Advanced Robotics Research, EarlyView.
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith, Sunny Amatya, Seyed Yousef Soltanian, Jonathan Bush, Wenlong Zhang   +4 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

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

Sparse representations in audio & music: from coding to source separation [PDF]

, 2010
—Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME, Gribonval, R, Daudet, Laurent, Plumbley, MD, Davies, M.E.; id_orcid, Daudet, L., Blumensath, T., Plumbley, Mark D., Gribonval, Rémi, Davies, Mike E., Plumbley, M.D., Blumensath, Thomas, Gribonval, R., Blumensath, T, Daudet, L, Davies, M.E.   +15 more
core   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing

Advanced Robotics Research, EarlyView.
Autonomous racing tests embodied intelligence under extreme control limits of speed, actuation, and decision‐making. Inspired by human multimodality, we present Apex, an embodied agent deployed on a super‐formula racing car at Yas Marina. Integrating physics‐informed perception, multimodal learning, and tasking, Apex cuts lap times by 15% and achieves ...
Yanxin Zhou, Baichuan Lou, Wenhui Huang, Binbin Hu, Haohan Yang, Haochen Liu, Xiangkun He, Chee Kiong Ong, Yiran Zhang, Tianchu Su, Bolin Zhao, Xiaoyu Mo, Holden King Ho Li, Chen Lv   +13 more
wiley   +1 more source