Numerical analysis of state of the art high performance thermoplastic composite as light weight bullet proof material

2019 ◽  
Vol 6 (9) ◽  
pp. 095333 ◽  
Author(s):  
Meenakshi S ◽  
Neeraj Srinivas ◽  
Y Sai Siddarth ◽  
Ch V S Kamal ◽  
Sudheendra K ◽  
...  
Keyword(s):  
Numerical Analysis ◽  
High Performance ◽  
State Of The Art ◽  
Thermoplastic Composite ◽  
Light Weight

scholarly journals Real-Time Facial Affective Computing on Mobile Devices

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 870 ◽  
Author(s):  
Yuanyuan Guo ◽  
Yifan Xia ◽  
Jing Wang ◽  
Hui Yu ◽  
Rung-Ching Chen
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Mobile Devices ◽  
High Performance ◽  
Affective Computing ◽  
State Of The Art ◽  
Light Weight ◽  
Computational Power ◽  
Art Methods ◽  
And Storage

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

Fused Ambipolar Aza-isoindigos with NIR absorption

2021 ◽  
Author(s):  
Liping Yao ◽  
Danlei Zhu ◽  
Hailiang Liao ◽  
Sheik Haseena ◽  
Mahesh kumar Ravva ◽  
...  
Keyword(s):  
Organic Semiconductors ◽  
High Performance ◽  
Low Cost ◽  
Light Weight ◽  
The Past ◽  
Pi Conjugated ◽  
Nir Absorption ◽  
Conjugated Organic Semiconductors

Due to their advantages of low-cost, light-weight, and mechanical flexibility, much attention has been focused on pi-conjugated organic semiconductors. In the past decade, although many materials with high performance has...

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 537
Author(s):  
Hongxiang Gu ◽  
Miodrag Potkonjak
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Evolution Strategies ◽  
Light Weight ◽  
Network Configuration ◽  
System Complexity ◽  
Physical Unclonable Functions ◽  
Output Stability ◽  
Output Mapping ◽  
Weight Design

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

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