scholarly journals Wireless Localization Based on Deep Learning: State of Art and Challenges

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yun-Xia Ye ◽  
An-Nan Lu ◽  
Ming-Yi You ◽  
Kai Huang ◽  
Bin Jiang
Keyword(s):  
Deep Learning ◽  
Wireless Communication ◽  
Position Estimation ◽  
Learning Technology ◽  
Learning Models ◽  
Wireless Localization ◽  
Classical Models ◽  
Technical Details ◽  
Localization Ability ◽  
State Of Art

The problem of position estimation has always been widely discussed in the field of wireless communication. In recent years, deep learning technology is rapidly developing and attracting numerous applications. The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models. Consequently, wireless localization based on deep learning has attracted extensive research during the last decade. The research and applications on wireless localization technology based on deep learning are reviewed in this paper. Typical deep learning models are summarized with emphasis on their inputs, outputs, and localization methods. Technical details helpful for enhancing localization ability are also mentioned. Finally, some problems worth further research are discussed.

scholarly journals Deep Learning for Video Classification: A Review

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Classification Task ◽  
Future Research ◽  
Video Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Research Directions ◽  
Concise Review ◽  
State Of Art

<div><div><div><p>Video classification task has gained a significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition to the importance of video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are a number of existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers are either outdated, and therefore, do not include the recent state-of-art works or they have some limitations. In order to provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the data sets used. To make the review self- contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided, and the critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.</p></div></div></div>

scholarly journals Automatic Discovery of Railway Train Driving Modes Using Unsupervised Deep Learning

2019 ◽  
Vol 8 (7) ◽  
pp. 294
Author(s):  
Han Zheng ◽  
Zanyang Cui ◽  
Xingchen Zhang
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Capacity Utilization ◽  
Learning Models ◽  
Trajectory Data ◽  
Generative Adversarial Network ◽  
Automatic Driving ◽  
Adversarial Network ◽  
Classical Models ◽  
Unsupervised Deep Learning

Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the study of driving modes. However, in the absence of labeled data, discovering driving modes is challenging. In this paper, instead of classical models (railway-specified feature extraction and classical clustering), we used five deep unsupervised learning models to overcome this difficulty. In these models, adversarial autoencoders and stacked autoencoders are used as feature extractors, along with generative adversarial network-based and Kullback–Leibler (KL) divergence-based networks as clustering models. An experiment based on real and artificial datasets showed the following: (i) The proposed deep learning models outperform the classical models by 27.64% on average. (ii) Integrated trajectory data can improve the accuracy of unsupervised learning by approximately 13.78%. (iii) The different performance rankings of models based on indices with labeled data and indices without labeled data demonstrate the insufficiency of people’s understanding of the existing modes. This study also analyzes the relationship between the discovered modes and railway carrying capacity.

scholarly journals Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1951
Author(s):  
Fahad Jibrin Abdu ◽  
Yixiong Zhang ◽  
Maozhong Fu ◽  
Yuhan Li ◽  
Zhenmiao Deng
Keyword(s):  
Deep Learning ◽  
Radar Data ◽  
Radar Signal ◽  
Future Research ◽  
Learning Technology ◽  
Learning Approaches ◽  
Learning Models ◽  
Wave Radar ◽  
Adverse Weather ◽  
Radar Signals

The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

scholarly journals Deep Learning for Video Classification: A Review

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Classification Task ◽  
Future Research ◽  
Video Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Research Directions ◽  
Concise Review ◽  
State Of Art

<div><div><div><p>Video classification task has gained a significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition to the importance of video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are a number of existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers are either outdated, and therefore, do not include the recent state-of-art works or they have some limitations. In order to provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the data sets used. To make the review self- contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided, and the critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.</p></div></div></div>

scholarly journals Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 197
Author(s):  
Yong-Woon Kim ◽  
Yung-Cheol Byun ◽  
Addapalli V. N. Krishna
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Medical Image Analysis ◽  
Learning Technology ◽  
Learning Models ◽  
Computing Power ◽  
Semantic Image Segmentation ◽  
Voting Method ◽  
Segmentation Models

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.

scholarly journals C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA

2021 ◽  
Vol 251 ◽  
pp. 03040
Author(s):  
Sitong An ◽  
Lorenzo Moneta
Keyword(s):  
Deep Learning ◽  
Code Generation ◽  
Production Environment ◽  
Learning Models ◽  
Development Status ◽  
Technical Details ◽  
New System

We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and demonstrates its development status with a preliminary benchmark against popular tools.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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