Multi-sensor information fusion for efficient smart transport vehicle tracking and positioning based on deep learning technique

2021 ◽  
Author(s):  
G. Suseendran ◽  
D. Akila ◽  
Hannah Vijaykumar ◽  
T. Nusrat Jabeen ◽  
R. Nirmala ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Vehicle Tracking ◽  
Learning Technique ◽  
Transport Vehicle ◽  
Sensor Information

scholarly journals Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4837 ◽  
Author(s):  
Stamatios Samaras ◽  
Eleni Diamantidou ◽  
Dimitrios Ataloglou ◽  
Nikos Sakellariou ◽  
Anastasios Vafeiadis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Rapid Evolution ◽  
Learning Technologies ◽  
Ease Of Use ◽  
Sensor Data ◽  
Wide Range ◽  
Challenging Tasks ◽  
Sensor Information ◽  
Novel Concept

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

Hybrid multitask multi-information fusion deep learning for household short-term load forecasting

2021 ◽  
pp. 1-1
Author(s):  
Lianjie Jiang ◽  
Xinli Wang ◽  
Wei Li ◽  
Lei Wang ◽  
Xiaohong Yin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

scholarly journals Design and Implementation of Track and Field Training Information Collection and Feedback System Based on Multi-sensor Information Fusion

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ling Li ◽  
Chengliang Li
Keyword(s):  
Information Fusion ◽  
Feedback System ◽  
Track And Field ◽  
Information Collection ◽  
Control Group ◽  
Average Performance ◽  
Field Training ◽  
Sensor Information ◽  
Experimental Group ◽  
Field Sports

AbstractTrack and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine.

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