Search Results for author:
Found 15 papers, 2 papers with code
Near-Field Channel Estimation in Dual-Band XL-MIMO with Side Information-Assisted Compressed Sensing
Specifically, we formulate the dual-band near-field communication model based on the fact that high-frequency systems are likely to be deployed with lower-frequency systems.
Block-Dominant Compressed Sensing for Near-Field Communications: Fundamentals, Solutions and Future Directions
Near-field (NF) communications draw much attention in the context of extremely large-scale antenna arrays (ELAA).
Block-Sparse Tensor Recovery
This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment.
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency.
Joint Block-Sparse Recovery Using Simultaneous BOMP/BOLS
We consider the greedy algorithms for the joint recovery of high-dimensional sparse signals based on the block multiple measurement vector (BMMV) model in compressed sensing (CS).
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares
In this paper, we propose a blind-block orthogonal least squares-based compressive spectrum sensing (B-BOLS-CSS) algorithm, which utilizes a novel blind stopping rule to cut the cords to these prior information.
Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound.
Downlink Massive MIMO Channel Estimation via Deep Unrolling : Sparsity Exploitations in Angular Domain
In frequency division duplex (FDD) massive MIMO systems, reliable downlink channel estimation is essential for the subsequent data transmission but is realized at the cost of massive pilot overhead due to hundreds of antennas at base station (BS).
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Z-Code++ creates new state of the art on 9 out of 13 text summarization tasks across 5 languages.
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Blind Orthogonal Least Squares based Compressive Spectrum Sensing
As an enabling technique of cognitive radio (CR), compressive spectrum sensing (CSS) based on compressive sensing (CS) can detect the spectrum opportunities from wide frequency bands efficiently and accurately by using sub-Nyquist sampling rate.
Scalable and Efficient MoE Training for Multitask Multilingual Models
By combining the efficient system and training methods, we are able to significantly scale up large multitask multilingual models for language generation which results in a great improvement in model accuracy.
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR system alike will be propagated to the next task in the pipeline.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Readability for Automatic Speech Recognition Transcription
In this work, we propose a novel NLP task called ASR post-processing for readability (APR) that aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Single-Shot Specular Surface Reconstruction With Gonio-Plenoptic Imaging
Unlike a conventional plenoptic camera, our system captures the BRDF variation of the object surface in a single image in addition to the light field information from the scene, which allows us to recover very fine 3D structures of the surface.
