Secrecy Performance Enhancement of Artificial Noise Injection Scheme-based FSO Systems

2021 ◽  
Author(s):  
Aman Sikri ◽  
Aashish Mathur ◽  
Gyandeep Verma
Keyword(s):  
Performance Enhancement ◽  
Artificial Noise ◽  
Injection Scheme ◽  
Noise Injection

scholarly journals Aerial Intelligent Reflecting Surface Enabled Terahertz Covert Communications in Beyond-5G Internet of Things

2021 ◽  
Author(s):  
Milad Tatar Mamaghani ◽  
Yi Hong
Keyword(s):  
Wireless Communications ◽  
Internet Of Things ◽  
Optimization Problem ◽  
Data Dissemination ◽  
Performance Enhancement ◽  
Average Energy ◽  
Access Point ◽  
Artificial Noise ◽  
User Scheduling ◽  
Reflecting Surface

<div>Unmanned aerial vehicles (UAVs) are envisioned to be extensively employed for assisting wireless communications in Internet of Things (IoT). </div><div>On the other hand, terahertz (THz) enabled intelligent reflecting surface (IRS) is expected to be one of the core enabling technologies for forthcoming beyond-5G wireless communications that promise a broad range of data-demand applications. In this paper, we propose a UAV-mounted IRS (UIRS) communication system over THz bands for confidential data dissemination from an access point (AP) towards multiple ground user equipments (UEs) in IoT networks. Specifically, the AP intends to send data to the scheduled UE, while unscheduled UEs may behave as potential adversaries. To protect information messages and the privacy of the scheduled UE, we aim to devise an energy-efficient multi-UAV covert communication scheme, where the UIRS is for reliable data transmissions, and an extra UAV is utilized as a cooperative jammer generating artificial noise (AN) to degrade unscheduled UEs detection, improving communication covertness.</div><div>This poses a novel max-min optimization problem in terms of minimum average energy efficiency (mAEE), targetting to improve covert throughput and reduce UAVs' propulsion energy consumption subject to some practical constraints such as covertness which is determined analytically. Since the optimization problem is non-convex, we tackle it via the block successive convex approximation (BSCA) approach to iteratively solve a sequence of approximated convex sub-problems, designing the binary user scheduling, AP's power allocation, maximum AN jamming power, IRS beamforming, and both UAVs' trajectory and velocity planning. Finally, we present a low-complex overall algorithm for system performance enhancement with complexity and convergence analysis. Numerical results are provided to verify our analysis and demonstrate significant outperformance of our design over other existing benchmark schemes.</div>

Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks

2021 ◽  
Author(s):  
Sai Kiran Cherupally ◽  
Jian Meng ◽  
Adnan Siraj Rakin ◽  
Shihui Yin ◽  
Injune Yeo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Black Box ◽  
Accuracy Improvement ◽  
Training Scheme ◽  
Injection Scheme ◽  
Noise Injection ◽  
Noise Data ◽  
The Impact

Abstract We present a novel deep neural network (DNN) training scheme and RRAM in-memory computing (IMC) hardware evaluation towards achieving high robustness to the RRAM device/array variations and adversarial input attacks. We present improved IMC inference accuracy results evaluated on state-of-the-art DNNs including ResNet-18, AlexNet, and VGG with binary, 2-bit, and 4-bit activation/weight precision for the CIFAR-10 dataset. These DNNs are evaluated with measured noise data obtained from three different RRAM-based IMC prototype chips. Across these various DNNs and IMC chip measurements, we show that our proposed hardware noise-aware DNN training consistently improves DNN inference accuracy for actual IMC hardware, up to 8% accuracy improvement for the CIFAR-10 dataset. We also analyze the impact of our proposed noise injection scheme on the adversarial robustness of ResNet-18 DNNs with 1-bit, 2-bit, and 4-bit activation/weight precision. Our results show up to 6% improvement in the robustness to black-box adversarial input attacks.

scholarly journals Artificial Noise Injection for Securing Single-Antenna Systems

2017 ◽  
Vol 66 (10) ◽  
pp. 9577-9581 ◽  
Author(s):  
Biao He ◽  
Yechao She ◽  
Vincent K. N. Lau
Keyword(s):  
Artificial Noise ◽  
Single Antenna ◽  
Noise Injection ◽  
Antenna Systems

scholarly journals Aerial Intelligent Reflecting Surface Enabled Terahertz Covert Communications in Beyond-5G Internet of Things

2021 ◽  
Author(s):  
Milad Tatar Mamaghani ◽  
Yi Hong
Keyword(s):  
Wireless Communications ◽  
Internet Of Things ◽  
Optimization Problem ◽  
Data Dissemination ◽  
Performance Enhancement ◽  
Average Energy ◽  
Access Point ◽  
Artificial Noise ◽  
User Scheduling ◽  
Reflecting Surface

<div>Unmanned aerial vehicles (UAVs) are envisioned to be extensively employed for assisting wireless communications in Internet of Things (IoT). </div><div>On the other hand, terahertz (THz) enabled intelligent reflecting surface (IRS) is expected to be one of the core enabling technologies for forthcoming beyond-5G wireless communications that promise a broad range of data-demand applications. In this paper, we propose a UAV-mounted IRS (UIRS) communication system over THz bands for confidential data dissemination from an access point (AP) towards multiple ground user equipments (UEs) in IoT networks. Specifically, the AP intends to send data to the scheduled UE, while unscheduled UEs may behave as potential adversaries. To protect information messages and the privacy of the scheduled UE, we aim to devise an energy-efficient multi-UAV covert communication scheme, where the UIRS is for reliable data transmissions, and an extra UAV is utilized as a cooperative jammer generating artificial noise (AN) to degrade unscheduled UEs detection, improving communication covertness.</div><div>This poses a novel max-min optimization problem in terms of minimum average energy efficiency (mAEE), targetting to improve covert throughput and reduce UAVs' propulsion energy consumption subject to some practical constraints such as covertness which is determined analytically. Since the optimization problem is non-convex, we tackle it via the block successive convex approximation (BSCA) approach to iteratively solve a sequence of approximated convex sub-problems, designing the binary user scheduling, AP's power allocation, maximum AN jamming power, IRS beamforming, and both UAVs' trajectory and velocity planning. Finally, we present a low-complex overall algorithm for system performance enhancement with complexity and convergence analysis. Numerical results are provided to verify our analysis and demonstrate significant outperformance of our design over other existing benchmark schemes.</div>

Performance Enhancement, Creativity and Mental Training

2003 ◽  
Author(s):  
M. Bar-Eli ◽  
O. Lowengart ◽  
J. Goldberg ◽  
S. Epstein ◽  
R. D. Fosbury
Keyword(s):  
Performance Enhancement ◽  
Mental Training

Effective performance improvement of organic thin film transistors with multi-layer modifications

2020 ◽  
Vol 91 (3) ◽  
pp. 30201
Author(s):  
Hang Yu ◽  
Jianlin Zhou ◽  
Yuanyuan Hao ◽  
Yao Ni
Keyword(s):  
Thin Film ◽  
Thin Film Transistors ◽  
Performance Enhancement ◽  
Organic Thin Film Transistors ◽  
Electrical Performance ◽  
Hole Injection ◽  
Organic Thin Film ◽  
Effective Performance ◽  
Significant Performance ◽  
P Type

Organic thin film transistors (OTFTs) based on dioctylbenzothienobenzothiophene (C8BTBT) and copper (Cu) electrodes were fabricated. For improving the electrical performance of the original devices, the different modifications were attempted to insert in three different positions including semiconductor/electrode interface, semiconductor bulk inside and semiconductor/insulator interface. In detail, 4,4′,4′′-tris[3-methylpheny(phenyl)amino] triphenylamine (m-MTDATA) was applied between C8BTBTand Cu electrodes as hole injection layer (HIL). Moreover, the fluorinated copper phthalo-cyanine (F16CuPc) was inserted in C8BTBT/SiO2 interface to form F16CuPc/C8BTBT heterojunction or C8BTBT bulk to form C8BTBT/F16CuPc/C8BTBT sandwich configuration. Our experiment shows that, the sandwich structured OTFTs have a significant performance enhancement when appropriate thickness modification is chosen, comparing with original C8BTBT devices. Then, even the low work function metal Cu was applied, a normal p-type operate-mode C8BTBT-OTFT with mobility as high as 2.56 cm2/Vs has been fabricated.

Performance enhancement of normal contact ratio gearing system through correction factor

2019 ◽  
Vol 13 (3) ◽  
pp. 5242-5258
Author(s):  
R. Ravivarman ◽  
K. Palaniradja ◽  
R. Prabhu Sekar
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Correction Factor ◽  
Bending Strength ◽  
Performance Enhancement ◽  
Transmission Ratio ◽  
Contact Ratio ◽  
Element Analysis ◽  
Normal Contact ◽  
Root Region

As lined, higher transmission ratio drives system will have uneven stresses in the root region of the pinion and wheel. To enrich this agility of uneven stresses in normal-contact ratio (NCR) gearing system, an enhanced system is desirable to be industrialized. To attain this objective, it is proposed to put on the idea of modifying the correction factor in such a manner that the bending strength of the gearing system is improved. In this work, the correction factor is modified in such a way that the stress in the root region is equalized between the pinion and wheel. This equalization of stresses is carried out by providing a correction factor in three circumstances: in pinion; wheel and both the pinion and the wheel. Henceforth performances of this S+, S0 and S- drives are evaluated in finite element analysis (FEA) and compared for balanced root stresses in parallel shaft spur gearing systems. It is seen that the outcomes gained from the modified drive have enhanced performance than the standard drive.

Performance Enhancement in OSI Network Model using Fuzzy Queue

2011 ◽  
Vol 4 (4) ◽  
pp. 377-386
Author(s):  
B.Palpandi B.Palpandi ◽  
◽  
Dr. G.Geetharamani Dr. G.Geetharamani ◽  
J.Arun Pandian
Keyword(s):  
Network Model ◽  
Performance Enhancement
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