scholarly journals Detecting Distance between Surfaces of Large Transparent Material Based on Low-Cost TOF Sensor and Deep Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Rong Zou ◽  
Yu Zhang ◽  
Junlan Gu ◽  
Jin Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Continuous Wave ◽  
Detection System ◽  
Low Cost ◽  
Deep Convolutional Neural Network ◽  
Large Area ◽  
Transparent Material ◽  
Depth Sensors ◽  
Transparent Materials

Detecting distance between surfaces of transparent materials with large area and thickness has always been a difficult problem in the field of industry. In this paper, a method based on low-cost TOF continuous-wave modulation and deep convolutional neural network technology is proposed. The distance detection between transparent material surfaces is converted to the problem of solving the intersection of the optical path and the transparent material’s front and rear surfaces. On this basis, the Gray code encoding and decoding operations are combined to achieve distance detection between surfaces. The problem of holes and detail loss of depth maps generated by low-resolution TOF depth sensors have been also effectively solved. The entire system is simple and can achieve thickness detection on the full surface area. Besides, it can detect large transparent materials with a thickness of over 30 mm, which far exceeds the existing optical thickness detection system for transparent materials.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

scholarly journals SBC-Based Cataract Detection System using Deep Convolutional Neural Network with Transfer Learning Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 4605-4613
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
System Development ◽  
Deep Convolutional Neural Network ◽  
Raspberry Pi ◽  
Significant Difference ◽  
Cataract Patients

This Raspberry Pi Single Board Computer-Based Cataract Detection System using Deep Convolutional Neural Network through GoogLeNet Transfer Learning and MATLAB digital image processing paradigm based on Lens Opacities Classification System III with Python application, which would capture the image of the eyes of cataract patients to detect the type of cataract without using dilating drops. Additionally, the system could also determine the severity, grade, color or area, and hardness of cataract. It would also display, save, search and print the partial diagnosis that can be done to the patients. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models was used as the methodologies of this study. Cataract patients and ophthalmologists of one of the eye clinics in City of Biñan, Laguna, as well as engineers and information technology professionals tested the system and also served as respondents to the conducted survey. Obtained results indicated that the detection of cataract and its characteristics using the system were accurate and reliable, which has a significant difference from the current eye examination for cataract. Generally, this would be a modern cataract detection system for all Cataract patients

scholarly journals Automatic Detection of Flood Using Remote Sensing Images

2020 ◽  
Vol 02 (01) ◽  
pp. 11-26 ◽  
Author(s):  
Amith Chandrakant Chawan ◽  
Vaibhav K Kakade ◽  
Jagannath K Jadhav
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Remote Sensing Images ◽  
Hazardous Area ◽  
High Resolution Images ◽  
Flood Detection

Remote sensing imaging (RSI) technology has recently been identified as an effective photogrammetric data acquisition platform to rapidly provide high resolution images due to its profitability, its ability to fly at low altitude and the ability to analysis in dangerous areas. The various kinds of classification techniques are have been used for flood extent mapping for finding the flood affected region, but based on the color region based analysis the classified hazardous area has very complex. Due to over the above issues in this work there significant enhancements have appeared in the classification of remote sensing images using Contiguous Deep Convolutional Neural Network (CDCNN).In the flood detection system the four different kinds of process like preprocessing, segmentation, feature extraction and the Contiguous Deep Convolutional Neural Network (CDCNN) has been executed for identifying the flood defected region. This works also investigates and compare with the possible methods with the proposed CDCNN for accurately identified by the Classification details of the RSI

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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