2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)最新文献_第3页

IoT Based Health Monitoring Using Smart Devices for Medical Emergency Services 使用智能设备进行医疗急救服务的基于物联网的健康监测

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.34

Farzana Shabnam, S. M. Azmi Hoque, Shahed Al Faiyad

引用次数: 2

A Multidimensional Pixel-wise Convolutional Neural Network for Hyperspectral Image Classification 用于高光谱图像分类的多维逐像素卷积神经网络

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.43

Yeahia Sarker, S. Fahim, S. Sarker, F. Badal, S. Das, Md. Nazrul Islam Mondal

{"title":"A Multidimensional Pixel-wise Convolutional Neural Network for Hyperspectral Image Classification","authors":"Yeahia Sarker, S. Fahim, S. Sarker, F. Badal, S. Das, Md. Nazrul Islam Mondal","doi":"10.1109/RAAICON48939.2019.43","DOIUrl":"https://doi.org/10.1109/RAAICON48939.2019.43","url":null,"abstract":"This paper presents a novel multidimensional pixel-wise convolutional neural network (MPCNN) to extract spatial and spectral-spatial information from the hyperspectral image (HSI). A hyperspectral image consists of narrow spatial and spectral band information based on the nature of visible materials and infrared regions of the electromagnetic spectrum. The release electromagnetic energy from visible material makes the specific wavelength which is used to classify the objects. The classification of hyperspectral image is one of the challenging task due to its narrow band energy formation. In this paper, we propose a MPCNN algorithm for classification of HSI based on two and three dimensional pixel-wise information. The term pixel defines the spectral vectors of proposed MPCNN that represents the ground material's energy radiation to the entire detection bands. This is done by using the convolutional neural network (CNN) to obtain spectral-spatial semantic feature information of hyperspectral image. The effectiveness of the proposed MPCNN is measured by classifying the objects in spatial and spectral-spatial domain and compared with different traditional CNN methods. The comparison result shows that the proposed MPCNN algorithm is capable to classify the hyperspectral image with 99.09% accuracy, while the MS-CLBP method achieves 91.51% accuracy.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"126 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120929608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Application of Transfer Learning to Detect Potato Disease from Leaf Image 迁移学习在马铃薯叶片图像病害检测中的应用

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.53

Farabee Islam, Md. Nazmul Hoq, C. M. Rahman

{"title":"Application of Transfer Learning to Detect Potato Disease from Leaf Image","authors":"Farabee Islam, Md. Nazmul Hoq, C. M. Rahman","doi":"10.1109/RAAICON48939.2019.53","DOIUrl":"https://doi.org/10.1109/RAAICON48939.2019.53","url":null,"abstract":"Potato is one of the most significant crops over the world. But production of potato is hampered due to some diseases which cause an increase of the cost as well as affect the life of the farmers. An automatic and early detection of these diseases will increase the production and help to digitize the system. Our main objective is to detect the potato diseases with a few leaf image data using advanced machine learning techniques. In this paper, we demonstrate that transfer learning technique could be used for early detection of potato diseases when it is difficult to collect thousands of new leaf images. Transfer learning uses already trained deep learning model's weight to solve new problem. The experiments included images of 152 healthy leaves, 1000 Late blight leaves, and 1000 early blight leaves. The program predicts with an accuracy of 99.43% in testing with 20% test data and 80% train data. We also compared sequential deep learning model with several pre-trained model applying transfer learning and found that transfer learning provided best result till date. Our output showed that transfer learning outperform all existing works on potato disease detection.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128684736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Smart Car Parking Management System 智能停车场管理系统

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.49

Lomat Haider Chowdhury, Zarar Mahmud, Intishar-Ul Islam, I. Jahan, Salekul Islam

{"title":"Smart Car Parking Management System","authors":"Lomat Haider Chowdhury, Zarar Mahmud, Intishar-Ul Islam, I. Jahan, Salekul Islam","doi":"10.1109/RAAICON48939.2019.49","DOIUrl":"https://doi.org/10.1109/RAAICON48939.2019.49","url":null,"abstract":"The demand for vehicles increases with the increase of the population. A densely replenished country like Bangladesh faces many challenges in managing this increased number of vehicles. Especially in city areas, where the road-side parking is not legitimate. Parking management can play an important role to diminish congestion on the roads. In our country, most of the parking areas are maintained by a manual parking system. In this paper, our aim is to design and develop a smart car parking system to solve the chaos, perplexity and long queues at the entry and exit of a parking space located inside public buildings including shopping malls and office spaces. In the smart car parking system, a radio frequency identification (RFID) card is used for every vehicle to store the information of the entrance. Time is automatically enumerated from the entry time to exit time, and thus fare will be shown to the client for his used parking space. In this paper, we have developed a system composed of both hardware and software components. Our system provides a user-friendly interface for the employees who maintain the system. The system also generates different reports including revenue and usage reports directly from the database using the software section. There will be an LED display in the parking that will show the number of available parking slots. Finally, we have measured the performance in terms of response time by deploying the system in the local server and remote Cloud server.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128628260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Real-Time Bangla License Plate Recognition System using Faster R-CNN and SSD: A Deep Learning Application 使用更快R-CNN和SSD的实时孟加拉车牌识别系统:深度学习应用

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.45

Tariqul Islam, Risul Islam Rasel

{"title":"Real-Time Bangla License Plate Recognition System using Faster R-CNN and SSD: A Deep Learning Application","authors":"Tariqul Islam, Risul Islam Rasel","doi":"10.1109/RAAICON48939.2019.45","DOIUrl":"https://doi.org/10.1109/RAAICON48939.2019.45","url":null,"abstract":"Traffic control and vehicle owner identification become major problems in Bangladesh. Most of the time it is difficult to identify the driver or the owner of the vehicles who violate the traffic rules or do any accidental work on the road. Moreover, it is very time-consuming for a traffic police officer to physically check the license plate of every vehicle. So, an automatic license plate recognition system is a much-needed solution to solve these problems. The existing Bangla license plate recognition systems are mostly based on character segmentation and these methods are not implemented in real-time. In this study, two separate Deep Convolutional Neural Network (DCNN) models are used to identify the license plate and the characters on the license plate from the real-time video streaming. The first CNN model detects the license plate from the live video of a vehicle on the road. Than it crop the license plate area from the video frames. The cropped frame is then fed into the second CNN to detect the characters on that license plate. The characters are detected as individual objects. After detecting all the characters and numbers on the license plate, they are rearranged according to their position on the plate. To train the proposed model total of 292 images are collected used. Moreover, an open-sourced Bangla handwritten character dataset named BanglaLekha-Isolated is also used to train the model with synthetic character data. The trained model is tested using 18 live videos and 6 still image data. Finally, the proposed methodology gains a 100% precision on detecting the license plate, and 91.67% precision for detecting the characters on the license plate for the given test dataset.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126507469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A simplified, novel and efficient approach to operate a group of elevators using a common control 一种简化、新颖和有效的方法，使用一个共同的控制器来操作一组电梯

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.56

Tanvir Mahmud, S. Saiduzzaman, Sadman Sakib Ahbab, Sk Shafaat Saud Nikor

引用次数: 1

Multitasking Spider Hexapod Robot 多任务蜘蛛六足机器人

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/raaicon48939.2019.58

Md. Imran Uddin, M. Alamgir, Joydeep Chakrabarty, Md. Iqbal Hossain, Md. Arif Abdulla Samy

{"title":"Multitasking Spider Hexapod Robot","authors":"Md. Imran Uddin, M. Alamgir, Joydeep Chakrabarty, Md. Iqbal Hossain, Md. Arif Abdulla Samy","doi":"10.1109/raaicon48939.2019.58","DOIUrl":"https://doi.org/10.1109/raaicon48939.2019.58","url":null,"abstract":"A hexapod robot is a type of spider robot which uses six mechanical limbs for movements. It is more versatile than wheel robots and it can traverse many critical terrains areas. This robot has been designed to solve some critical problems which are manually very hard and risky for human beings. This robot can pass through any risky slope and terrain areas. It can climb any obstacle or object and balance on rough surfaces as well. By using suction cups, it can climb any buildings. This model has been improved for climbing techniques, camera system (360° view), walking angles and payload. SOC [System on Chip] has been used to control the camera for monitoring the affected earthquake area and give the information by video capturing footage. For instance, when the hexapod robot will climbing and walk through the affected earthquake areas, the camera will be capturing the affecting moment and broadcast by live streaming on YouTube with the help of the MQTT broker. This hexapod can carry the 20 lb load on its top of the body frame. Climbing over an obstacle has been improved for this hexapod model compared to other hexapods. Critical angle climbing is one of the most important improvements for this robot and it can walk smoothly near about 60° angles of the surfaces with a good balance. In risky areas, it can detect dark and light surfaces with the help of infrared sensors, as well as it can detect sound by using the sound sensor module and reach there for human safety purposes where an earthquake, fire incidents will occur. The robot was pragmatically assessed and above 85% efficiency for application, mechanism, and the system has been observed compared with other researches.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"53 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120920807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Feature Reduction and Classification of Hyperspectral Image Based on Multiple Kernel PCA and Deep Learning 基于多核PCA和深度学习的高光谱图像特征约简与分类

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.59

M. Hossain, Md. Ali Hossain

{"title":"Feature Reduction and Classification of Hyperspectral Image Based on Multiple Kernel PCA and Deep Learning","authors":"M. Hossain, Md. Ali Hossain","doi":"10.1109/RAAICON48939.2019.59","DOIUrl":"https://doi.org/10.1109/RAAICON48939.2019.59","url":null,"abstract":"In recent years, the classification of Hyper Spectral Image (HSI) is a big challenge for its multidimensional property. So it is burning question to reduce the dimension of HSIs. There are several ways to reduce the dimension of hyperspectral images like Principle Component Analysis (PCA), Kernel Principle Component Analysis (KPCA), Kernel Entropy Component Analysis (KECA) and so on. In this paper, we proposed a modified version of KPCA using multiple kernels like Linear, Radial Basis Function (RBF), Cosine, Sigmoid. Then fused their spectral and special properties by doing the classification of the HSIs using Hybrid Spectral Net (HybridSN) Model which is a recently trending modified deep neural network algorithm of Convolutional Neural Network (CNN). Finally, this paper demonstrates experimental results to show the effects and performance on classification of using different kernels of KPCA algorithm with other algorithms such as Non-negative Matrix Factorization(NMF), Independent Component Analysis (ICA) and Singular Value Decomposition(SVD) on well-known hyperspectral dataset.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116935924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Real Time Tracking of Driver Fatigue and Inebriation Maintaining a Strict Driving Schedule 驾驶员疲劳和醉酒的实时跟踪保持严格的驾驶时间表

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.6

Sanjay Dey, Mohammad Towhidul Islam, S. Chowdhury, Muhammad Islam, Md. Ali Hossain, S. Das

{"title":"Real Time Tracking of Driver Fatigue and Inebriation Maintaining a Strict Driving Schedule","authors":"Sanjay Dey, Mohammad Towhidul Islam, S. Chowdhury, Muhammad Islam, Md. Ali Hossain, S. Das","doi":"10.1109/RAAICON48939.2019.6","DOIUrl":"https://doi.org/10.1109/RAAICON48939.2019.6","url":null,"abstract":"This paper is concerned about the methods of road safety by addressing potential causes such as drowsiness and inebriation maintaining a strict schedule by recognizing the driver's face. Increasing unawareness towards traffic rules yields more and more accidents by the day. Drowsiness results from the monotony towards driving and inebriation results from the unawareness or unwillingness to abide by the traffic rules. This conundrum victimizes both the person inside and outside the vehicle. However, drowsiness prevention requires a method of detecting the deterioration of the vehicle operator's attention in a legitimate way along with an alerting mechanism. Though the existing solutions are developed through some unique methods, there are still some issues addressing yawn, blink issues, and alcoholism which have not been considered in their systems. This study aims to develop an improved and innovative approach to solving this issue. A train model developed by histogram oriented gradient (HOG) and linear support vector machine (SVM) extracts the eye and mouth position and calculates the eye aspect ratio (EAR), mouth aspect ratio (MAR) and MQ-3 sensor for measuring the degree of concentration of alcohol in the air. These data are then compared with the threshold value which is developed from a data-set of the aspect ratio of sleeping or drowsy face models.","PeriodicalId":102214,"journal":{"name":"2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125607711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mitigating and Detecting DDoS attack on IoT Environment 缓解和检测物联网环境中的DDoS攻击

2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON) Pub Date : 2019-11-01 DOI: 10.1109/RAAICON48939.2019.5

M. Rohit, Sakif Md. Fahim, A. A. Khan

引用次数: 4