Jiayu Li

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Indiana University ∙ Syracuse University

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Low-complexity and High-performance Receive Beamforming for Secure Directional Modulation Networks against an Eavesdropping-enabled Full-duplex Attacker

In this paper, we present a novel scenario for directional modulation (D...
12/06/2020 ∙ by Yin Teng, et al. ∙ 0 ∙ share

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Rank Position Forecasting in Car Racing

Forecasting is challenging since uncertainty resulted from exogenous fac...
10/04/2020 ∙ by Bo Peng, et al. ∙ 6 ∙ share

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SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting

Subgraph counting aims to count occurrences of a template T in a given n...
09/23/2020 ∙ by Langshi Chen, et al. ∙ 0 ∙ share

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Enhanced Secrecy Rate Maximization for Directional Modulation Networks via IRS

Intelligent reflecting surface (IRS) is of low-cost and energy-efficienc...
08/12/2020 ∙ by Feng Shu, et al. ∙ 0 ∙ share

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Energy-efficient Alternating Iterative Secure Structure of Maximizing Secrecy Rate for Directional Modulation Networks

In a directional modulation (DM) network, the issues of security and pri...
07/16/2019 ∙ by Linlin Sun, et al. ∙ 0 ∙ share

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Performance Analysis of Directional Modulation with Finite-quantized RF Phase Shifters in Analog Beamforming Structure

The radio frequency (RF) phase shifter with finite quantization bits in ...
04/03/2019 ∙ by Feng Shu, et al. ∙ 0 ∙ share

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A GraphBLAS Approach for Subgraph Counting

Subgraph counting aims to count the occurrences of a subgraph template T...
03/11/2019 ∙ by Langshi Chen, et al. ∙ 0 ∙ share

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ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

To facilitate efficient embedded and hardware implementations of deep ne...
12/31/2018 ∙ by Ao Ren, et al. ∙ 10 ∙ share

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A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM

Many model compression techniques of Deep Neural Networks (DNNs) have be...
11/05/2018 ∙ by Shaokai Ye, et al. ∙ 10 ∙ share

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Progressive Weight Pruning of Deep Neural Networks using ADMM

Deep neural networks (DNNs) although achieving human-level performance i...
10/17/2018 ∙ by Shaokai Ye, et al. ∙ 16 ∙ share

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ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs

Weight pruning methods of deep neural networks (DNNs) have been demonstr...
07/29/2018 ∙ by Tianyun Zhang, et al. ∙ 0 ∙ share

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Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs

Both industry and academia have extensively investigated hardware accele...
03/28/2018 ∙ by Caiwen Ding, et al. ∙ 0 ∙ share

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On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory intensive and computati...
03/14/2018 ∙ by Yanzhi Wang, et al. ∙ 0 ∙ share

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