scholarly journals QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices

2021 ◽  
Vol 5 (4) ◽  
pp. 1-30
Author(s):  
Saideep Tiku ◽  
Prathmesh Kale ◽  
Sudeep Pasricha
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Indoor Localization ◽  
Cyber Physical Systems ◽  
Learning Models ◽  
Crucial Aspect ◽  
Localization Accuracy ◽  
Physical Systems ◽  
Accurate Localization ◽  
Localization Model

Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future. Such services are poised to reinvent the process of navigation and tracking of people and assets in a variety of indoor and subterranean environments. The growing ownership of computationally capable smartphones has laid the foundations of portable fingerprinting-based indoor localization through deep learning. However, as the demand for accurate localization increases, the computational complexity of the associated deep learning models increases as well. We present an approach for reducing the computational requirements of a deep learning-based indoor localization framework while maintaining localization accuracy targets. Our proposed methodology is deployed and validated across multiple smartphones and is shown to deliver up to 42% reduction in prediction latency and 45% reduction in prediction energy as compared to the best-known baseline deep learning-based indoor localization model.

scholarly journals Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Quentin Cabanes ◽  
Benaoumeur Senouci ◽  
Amar Ramdane-Cherif
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computation Time ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Research Technology ◽  
Standard Design ◽  
Hardware Architectures ◽  
Voxel Grid

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems

2021 ◽  
pp. 42-67
Author(s):  
Evren Daglarli
Keyword(s):  
Artificial Intelligence ◽  
Cyber Physical Systems ◽  
Black Box ◽  
The Internet ◽  
Learning Models ◽  
Physical Systems ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Meta Learning ◽  
Black Box Models

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.

scholarly journals Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing

2020 ◽  
Vol 2 (1) ◽  
pp. 34-36 ◽  
Author(s):  
Jay Lee ◽  
Moslem Azamfar ◽  
Jaskaran Singh ◽  
Shahin Siahpour
Keyword(s):  
Deep Learning ◽  
Cyber Physical Systems ◽  
Smart Manufacturing ◽  
Physical Systems ◽  
Digital Twin

scholarly journals Identifying Malware on Cyber Physical Systems by incorporating Semi-Supervised Approach and Deep Learning

2019 ◽  
Vol 322 ◽  
pp. 012012
Author(s):  
Shaila Sharmeen ◽  
Shamsul Huda ◽  
Jemal Abawajy
Keyword(s):  
Deep Learning ◽  
Cyber Physical Systems ◽  
Physical Systems

DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber-Physical Systems

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Beibei Li ◽  
Yuhao Wu ◽  
Jiarui Song ◽  
Rongxing Lu ◽  
Tao Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Physical Systems ◽  
Physical Systems

Dependable Visual Light-Based Indoor Localization with Automatic Anomaly Detection for Location-Based Service of Mobile Cyber-Physical Systems

2019 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Yang Liu ◽  
Xiaoming Chen ◽  
Dileep Kadambi ◽  
Ajinkya Bari ◽  
Xin Li ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Indoor Localization ◽  
Cyber Physical Systems ◽  
Location Based Service ◽  
Physical Systems ◽  
Visual Light
Sign in / Sign up

Export Citation Format

Share Document