A training sequence parallel detection technology based on timeslot sliding window
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1687-1499
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
A training sequence parallel detection technology based on timeslot sliding window ; volume:2014 ; number:1 ; day:16 ; month:7 ; year:2014 ; pages:1-14 ; date:12.2014
EURASIP journal on wireless communications and networking / European Association for Speech, Signal and Image Processing ; 2014, Heft 1 (16.7.2014), 1-14, 12.2014
- Classification
-
Elektrotechnik, Elektronik
- Creator
-
Shi, Jian
Huang, Jianfeng
Liu, Kaihua
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/1687-1499-2014-119
- URN
-
urn:nbn:de:101:1-2021081507204827904097
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:44 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Shi, Jian
- Huang, Jianfeng
- Liu, Kaihua
- SpringerLink (Online service)
Other Objects (12)
Language recognition in the sliding window model
Derandomization for Sliding Window Algorithms with Strict Correctness∗
Computing Minimal Unique Substrings for a Sliding Window
Sliding Window Empirical Mode Decomposition -its performance and quality
Sliding Window Top-K Monitoring over Distributed Data Streams
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives
Diagnosis of model errors with a sliding time‐window Bayesian analysis
Graph-based Sliding Window Localization and Map Refinement for Automated Vehicles
How secure is exponent-blinded RSA–CRT with sliding window exponentiation?
Versatile time-window sliding machine learning techniques for stock market forecasting
Two-level sliding-window VBR control algorithm for video on demand streaming
Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation
Language recognition in the sliding window model
Derandomization for Sliding Window Algorithms with Strict Correctness∗
Computing Minimal Unique Substrings for a Sliding Window
Sliding Window Empirical Mode Decomposition -its performance and quality
Sliding Window Top-K Monitoring over Distributed Data Streams
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives
Diagnosis of model errors with a sliding time‐window Bayesian analysis
Graph-based Sliding Window Localization and Map Refinement for Automated Vehicles
How secure is exponent-blinded RSA–CRT with sliding window exponentiation?
Versatile time-window sliding machine learning techniques for stock market forecasting
Two-level sliding-window VBR control algorithm for video on demand streaming
Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation
Language recognition in the sliding window model
Derandomization for Sliding Window Algorithms with Strict Correctness∗
Computing Minimal Unique Substrings for a Sliding Window
Sliding Window Empirical Mode Decomposition -its performance and quality
Sliding Window Top-K Monitoring over Distributed Data Streams
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives
Diagnosis of model errors with a sliding time‐window Bayesian analysis
Graph-based Sliding Window Localization and Map Refinement for Automated Vehicles
How secure is exponent-blinded RSA–CRT with sliding window exponentiation?
Versatile time-window sliding machine learning techniques for stock market forecasting
Two-level sliding-window VBR control algorithm for video on demand streaming