scholarly journals Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 210
Author(s):  
Dongsuk Park ◽  
Seungeui Lee ◽  
SeongUk Park ◽  
Nojun Kwak
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Continuous Wave ◽  
Classification Model ◽  
Training Time ◽  
Fmcw Radar ◽  
Low Altitude ◽  
Real Time Identification ◽  
Short Time ◽  
Time Systems

With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%.

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

scholarly journals Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2166
Author(s):  
Kyungeun Park ◽  
Jeongpyo Lee ◽  
Youngok Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Continuous Wave ◽  
Two Dimensional ◽  
Fmcw Radar ◽  
Localization Scheme ◽  
Wave Radar ◽  
Conventional Scheme ◽  
24 Ghz

In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.

scholarly journals Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning

2020 ◽  
Vol 10 (15) ◽  
pp. 5284 ◽  
Author(s):  
Houdi Xiao ◽  
Zhipeng Qu ◽  
Mingyun Lv ◽  
Yi Jiang ◽  
Chuanzhi Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Design Method ◽  
Pattern Generation ◽  
Generation Process ◽  
The Future ◽  
Short Time ◽  
New Generation ◽  
The Military ◽  
Self Adaptive

Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.

Ransomware Traffic Classification Using Deep Learning Models

2020 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Arivudainambi D. ◽  
Varun Kumar K.A. ◽  
Vinoth Kumar R. ◽  
Visu P.
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Classification Model ◽  
Traffic Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Novel Method

Ransomware is a malware which affects the systems data with modern encryption techniques, and the data is recovered once a ransom amount is paid. In this research, the authors show how ransomware propagates and infects devices. Live traffic classifications of ransomware have been meticulously analyzed. Further, a novel method for the classification of ransomware traffic by using deep learning methods is presented. Based on classification, the detection of ransomware is approached with the characteristics of the network traffic and its communications. In more detail, the behavior of popular ransomware, Crypto Wall, is analyzed and based on this knowledge, a real-time ransomware live traffic classification model is proposed.

Real-Time FMCW Radar X-Band Signal Acquisition and Visualization

2019 ◽  
pp. 165-179
Author(s):  
Akbar Eslami
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Continuous Wave ◽  
Processing System ◽  
Fmcw Radar ◽  
Weather Radars ◽  
X Band ◽  
Recent Developments ◽  
Visualization Systems ◽  
Pulse Doppler

The recent developments in the remote sensing technologies have resulted in large amounts of data transmitted from spaceborne sensors. To keep up with the volume, speed, and variety of these data, new data acquisition and visualization systems need to be developed. This chapter focuses on some design and development considerations for a real-time data acquisition and visualization of X-band in a frequency-modulated continuous wave (FMCW) radar. Relevant issues such as high-speed network, parallel data processing system, and large-scale storage system are discussed. Ideally, the acquisition system should be capable of concurrent processing at low cost and visualization technique should be in the same time scale with other conventional 2D visualization of X-band weather radars. Benefits of this type of radar are that it is not just safe and inexpensive, but also serves as a means in filling in gaps of higher-powered pulse-doppler radars when used in conjunction with them.

Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning

Energy ◽  
2021 ◽  
pp. 122319
Author(s):  
Ning Mao ◽  
Amirah Nabilah Azman ◽  
Guangxin Ding ◽  
Yubo Jin ◽  
Can Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Liquid Flow ◽  
Black Box ◽  
Doppler Velocimetry ◽  
Ultrasound Doppler ◽  
Gas Liquid Flow ◽  
Real Time Identification

Real time identification of anomalous events in coastal regions using deep learning techniques

2021 ◽  
Author(s):  
Varalakshmi Perumal ◽  
SureshKumar Murugaiyan ◽  
Pavithran Ravichandran ◽  
R. Venkatesan ◽  
R. Sundar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Coastal Regions ◽  
Learning Techniques ◽  
Real Time Identification

scholarly journals Multi-Input Deep Learning Based FMCW Radar Signal Classification

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1144
Author(s):  
Daewoong Cha ◽  
Sohee Jeong ◽  
Minwoo Yoo ◽  
Jiyong Oh ◽  
Dongseog Han
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Continuous Wave ◽  
Autonomous Driving ◽  
Radar Data ◽  
Classification Performance ◽  
Radar Signal ◽  
Fmcw Radar ◽  
Input Structure ◽  
Single Input

In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.

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