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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...
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Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous fac...
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SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting
Subgraph counting aims to count occurrences of a template T in a given n...
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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...
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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...
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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 ...
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A GraphBLAS Approach for Subgraph Counting
Subgraph counting aims to count the occurrences of a subgraph template T...
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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...
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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...
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Progressive Weight Pruning of Deep Neural Networks using ADMM
Deep neural networks (DNNs) although achieving human-level performance i...
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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...
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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...
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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...
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