Microarray is an important tool and powerful technique that is used to analyze the expression of DNA in organisms for large scale gene sequences and gene expressions. Microarray technology allows massively parallel, high throughput profiling of gene expression in a single hybridization experiment. Processing of microarray images provides the input for further analysis of the extracted microarray data. This work deals on the basic principles on the methods used to grid an image. Gridding has become a prominent objective in microarray image analysis. To grid an image various methods such as grid alignment, sub grid detection, Bayesian Model, hill climbing approach, genetic algorithm and optimal multilevel thresholding has been taken for this study. This paper focuses on the various methods that are widely used to grid the image.
{"title":"A Review on Gridding Techniques of Microarray Images","authors":"Karthik Sa, Manjunath Ss, Prakyath Dp, Prashanth S, Vamshi Krishna, Siddartha","doi":"10.1109/ICATIECE45860.2019.9063822","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063822","url":null,"abstract":"Microarray is an important tool and powerful technique that is used to analyze the expression of DNA in organisms for large scale gene sequences and gene expressions. Microarray technology allows massively parallel, high throughput profiling of gene expression in a single hybridization experiment. Processing of microarray images provides the input for further analysis of the extracted microarray data. This work deals on the basic principles on the methods used to grid an image. Gridding has become a prominent objective in microarray image analysis. To grid an image various methods such as grid alignment, sub grid detection, Bayesian Model, hill climbing approach, genetic algorithm and optimal multilevel thresholding has been taken for this study. This paper focuses on the various methods that are widely used to grid the image.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121498968","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063804
Tanay Karve
A SmartDriving system has been developed by using classical Image Processing and Cartesian Geometry. This smart system aims to outperform the conventional driving system based on steering wheel and pedals, by using an onboard mini camera and powerful algorithms running on an onboard computer. This SmartDriving system eliminates the need of legs for driving, thus making it convenient for the wheelchair-ridden. It also prevents deaths due to accidents as the prime cause of deaths in accidents, the steering wheel, is replaced by the said system. The vehicle is maneuvered as if an imaginary steering wheel is held in air, and controlled with usual left/right turning. The acceleration is controlled by the Euclidean distance between the hands holding the imaginary steering wheel. Braking is controlled by converging the distance between the hands. SmartDriving system aims to replace conventional driving methods to make driving accessible, easier, safer and smarter for everyone.
{"title":"Smart Vehicle Driving System using Computer Vision based Hand Motion Tracking","authors":"Tanay Karve","doi":"10.1109/ICATIECE45860.2019.9063804","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063804","url":null,"abstract":"A SmartDriving system has been developed by using classical Image Processing and Cartesian Geometry. This smart system aims to outperform the conventional driving system based on steering wheel and pedals, by using an onboard mini camera and powerful algorithms running on an onboard computer. This SmartDriving system eliminates the need of legs for driving, thus making it convenient for the wheelchair-ridden. It also prevents deaths due to accidents as the prime cause of deaths in accidents, the steering wheel, is replaced by the said system. The vehicle is maneuvered as if an imaginary steering wheel is held in air, and controlled with usual left/right turning. The acceleration is controlled by the Euclidean distance between the hands holding the imaginary steering wheel. Braking is controlled by converging the distance between the hands. SmartDriving system aims to replace conventional driving methods to make driving accessible, easier, safer and smarter for everyone.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115272089","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063625
John Renz B. Bodollo, John Daniel V. Cortez, Edrick Raven P. Maraya, Ervin V. Navarro, Ralf Quintin L. Saquing, R. Tolentino
This study implements 3d face tracking and skeletal tracking in Microsoft Kinect Xbox One. Once the user is detected by the sensor head point from skeletal tracking, and a computed point D from chin point and eyebrow midpoint from 3d face tracking will be used to create the Line of Sight vector. Also, appliance points are always specified by the location of the appliance in the room with respect to the Kinect. Different appliance vectors will be created through vector subtraction. Angles between the Line of Sight vector and each of the appliance vector were computed through scalar product and compared to obtain the smallest angle. Once the smallest angle was obtained it was compared to a 15-degree threshold. If it’s within the threshold, then the appliance is selected.
本研究在微软Kinect Xbox One上实现了三维人脸跟踪和骨骼跟踪。一旦用户被传感器检测到头部点来自骨骼跟踪，从下巴点和眉毛中点从3d面部跟踪计算点D将被用来创建视线向量。此外，设备点总是由设备在房间中相对于Kinect的位置来指定。通过向量减法，将创建不同的器具向量。通过标量积计算视线矢量与各应用矢量之间的夹角，并进行比较，求出最小夹角。一旦获得最小的角度，就将其与15度阈值进行比较。如果在阈值范围内，则选择该设备。
{"title":"Selection of Appliance Using Skeletal Tracking and 3D Face Tracking for Gesture Control Home Automation","authors":"John Renz B. Bodollo, John Daniel V. Cortez, Edrick Raven P. Maraya, Ervin V. Navarro, Ralf Quintin L. Saquing, R. Tolentino","doi":"10.1109/ICATIECE45860.2019.9063625","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063625","url":null,"abstract":"This study implements 3d face tracking and skeletal tracking in Microsoft Kinect Xbox One. Once the user is detected by the sensor head point from skeletal tracking, and a computed point D from chin point and eyebrow midpoint from 3d face tracking will be used to create the Line of Sight vector. Also, appliance points are always specified by the location of the appliance in the room with respect to the Kinect. Different appliance vectors will be created through vector subtraction. Angles between the Line of Sight vector and each of the appliance vector were computed through scalar product and compared to obtain the smallest angle. Once the smallest angle was obtained it was compared to a 15-degree threshold. If it’s within the threshold, then the appliance is selected.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121356062","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063781
D. Tl, Vijayalakshmi Mn, A. S
Forest fire is a natural calamity which causes immense loss to the ecology. The high severity fire causes most loss to the vegetation. So this fire has to be detected within forest region in order to save vegetation. Currently frameworks are available with the image processing system which does analysis over the static forest fire image which provides information of fire hot spots. The fire motion/movement analysis with forest zone analysis is a challenging task with such static images. This problem can be solved with continuous monitoring of the fire with videos. Hence proposed framework provides novel forest fire flame movement analysis system based on spatiotemporal features using videos.
{"title":"Speculation of Forest Fire Using Spatial and Video Data","authors":"D. Tl, Vijayalakshmi Mn, A. S","doi":"10.1109/ICATIECE45860.2019.9063781","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063781","url":null,"abstract":"Forest fire is a natural calamity which causes immense loss to the ecology. The high severity fire causes most loss to the vegetation. So this fire has to be detected within forest region in order to save vegetation. Currently frameworks are available with the image processing system which does analysis over the static forest fire image which provides information of fire hot spots. The fire motion/movement analysis with forest zone analysis is a challenging task with such static images. This problem can be solved with continuous monitoring of the fire with videos. Hence proposed framework provides novel forest fire flame movement analysis system based on spatiotemporal features using videos.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121681392","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063846
P. Shruthi
Certifying the quality of food product is the major concern of the country. The citizens of the country are recommended to use only quality assured products. The same thing need to be applied for the wine industry also. The quality of wine need to be assessed and it should be classified into different category based on the quality assessment. Data mining is the right approach to achieve this as it extracts the useful information by analyzing the data set. In this paper, the samples of different wines with their attributes required for quality assurance is collected and different data mining classification algorithms- Naive Bayes, Simple Logistic, KStar, JRip, J48 are applied on it. The wine will be classified into three main categories and the accuracy of the algorithms are compared.
{"title":"Wine Quality Prediction Using Data Mining","authors":"P. Shruthi","doi":"10.1109/ICATIECE45860.2019.9063846","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063846","url":null,"abstract":"Certifying the quality of food product is the major concern of the country. The citizens of the country are recommended to use only quality assured products. The same thing need to be applied for the wine industry also. The quality of wine need to be assessed and it should be classified into different category based on the quality assessment. Data mining is the right approach to achieve this as it extracts the useful information by analyzing the data set. In this paper, the samples of different wines with their attributes required for quality assurance is collected and different data mining classification algorithms- Naive Bayes, Simple Logistic, KStar, JRip, J48 are applied on it. The wine will be classified into three main categories and the accuracy of the algorithms are compared.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125857080","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063773
K. S, Neela R R, S. M., Madhuchandra, H. K
A system of interrelated computing devices, mechanical, and digital machines that are provided with the ability to transfer data over a network without requiring human interaction constitutes Internet of Things. This brings out automation of things. It is achieved through sensor and actuator devices. This paper brings out a survey on various sensors and actuator which is used in the implementation of Smart Cradle.
{"title":"Analysis on IoT Based Smart Cradle System with an Android Application for Baby Monitoring","authors":"K. S, Neela R R, S. M., Madhuchandra, H. K","doi":"10.1109/ICATIECE45860.2019.9063773","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063773","url":null,"abstract":"A system of interrelated computing devices, mechanical, and digital machines that are provided with the ability to transfer data over a network without requiring human interaction constitutes Internet of Things. This brings out automation of things. It is achieved through sensor and actuator devices. This paper brings out a survey on various sensors and actuator which is used in the implementation of Smart Cradle.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116094406","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063836
S. Jahnavi, C. Nandini
Privacy and security are two pivotal rights in day-to-day life. At present, keys, passwords and PIN’s are used to secure the confidential data. However the above mentioned methods can be compromised and thus propose threats to security. This paper provides an advanced method to enhance the security system using face detection and recognition algorithms integrated with raspberry pi that is used to control the access to the door. Since face is indubitably related to an individual, it cannot be duplicated. This paper consists of three subsystems-Face detection, Feature extraction and Face recognition for door access. Initially the system is trained with authorized persons features, stored in the database. Firstly, the process is started by capturing the image of an object using raspberry pi camera followed by face detection done using Viola Jones algorithm as it provides a greater accuracy in real-time object detection. Next the feature extraction and face detection is done using Local Binary Pattern (LBP) algorithm that can extract local neighboring texture information of grey scale image and can efficiently differentiate between object and background. The extracted features are dimensionally reduced using Principal Component Analysis (PCA) algorithm .The detected face is compared against the stored features and if there is a match the access is provided to the authorized person. If not, the access to the door is denied and an alarm is raised alerting the admin.
{"title":"Smart Anti-Theft Door locking System","authors":"S. Jahnavi, C. Nandini","doi":"10.1109/ICATIECE45860.2019.9063836","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063836","url":null,"abstract":"Privacy and security are two pivotal rights in day-to-day life. At present, keys, passwords and PIN’s are used to secure the confidential data. However the above mentioned methods can be compromised and thus propose threats to security. This paper provides an advanced method to enhance the security system using face detection and recognition algorithms integrated with raspberry pi that is used to control the access to the door. Since face is indubitably related to an individual, it cannot be duplicated. This paper consists of three subsystems-Face detection, Feature extraction and Face recognition for door access. Initially the system is trained with authorized persons features, stored in the database. Firstly, the process is started by capturing the image of an object using raspberry pi camera followed by face detection done using Viola Jones algorithm as it provides a greater accuracy in real-time object detection. Next the feature extraction and face detection is done using Local Binary Pattern (LBP) algorithm that can extract local neighboring texture information of grey scale image and can efficiently differentiate between object and background. The extracted features are dimensionally reduced using Principal Component Analysis (PCA) algorithm .The detected face is compared against the stored features and if there is a match the access is provided to the authorized person. If not, the access to the door is denied and an alarm is raised alerting the admin.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"28 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131377525","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063816
B. Roopa, R. Manjunatha Prasad
Autism Spectrum Disorder (ASD) is highly complicated neurodevelopment disorder whose increasing prevalence is 1 in 68 individuals (survey of Centers for Disease Control and Preventions). There are various influential’s for ASD. The root cause is not known predominantly even today. But the state of the art of autism in research is, due to autism risk genes showcasing structural & functional brain differences and behavioral features of ASD. Some of the key measuring tools which are multifaceted indicators help to diagnose autism are like: 1.Physiological Detection (emotion assessment from autistic individual), which uses 4 Physiological signals namely electrocardiogram (ECG), skin conductance (SC), respiration and skin temperature. Outcomes were addressed by rating on three scales: arousal, valance and dominance. This approach is non invasive and economical. 2. Exploring the network connectivity in brain, the magnetic resonance imaging (MRI) and functional magnetic resonance imaging (f-MRI) fetches a non invasive approach to map the ordinal patterns of interaction in brain regions to better understand the pathology. 3. Most common machine learning classifier applied to diagnose ASD is Support vector machine (SVM) algorithm. The further implication of Robust SVM (variant of the single SVM) in research progress has improved the accuracy of diagnosing ASD from control group (CG). 4. Last but not the least Deep learning models helps in building model of profound classification accuracy. Early and accurate diagnosis of ASD intensity level leading to selection of correct treatment procedures and thus helps the autistic individual to undergo worth therapies or other relevant treatments.
{"title":"Concatenating framework in ASD analysis towards research progress","authors":"B. Roopa, R. Manjunatha Prasad","doi":"10.1109/ICATIECE45860.2019.9063816","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063816","url":null,"abstract":"Autism Spectrum Disorder (ASD) is highly complicated neurodevelopment disorder whose increasing prevalence is 1 in 68 individuals (survey of Centers for Disease Control and Preventions). There are various influential’s for ASD. The root cause is not known predominantly even today. But the state of the art of autism in research is, due to autism risk genes showcasing structural & functional brain differences and behavioral features of ASD. Some of the key measuring tools which are multifaceted indicators help to diagnose autism are like: 1.Physiological Detection (emotion assessment from autistic individual), which uses 4 Physiological signals namely electrocardiogram (ECG), skin conductance (SC), respiration and skin temperature. Outcomes were addressed by rating on three scales: arousal, valance and dominance. This approach is non invasive and economical. 2. Exploring the network connectivity in brain, the magnetic resonance imaging (MRI) and functional magnetic resonance imaging (f-MRI) fetches a non invasive approach to map the ordinal patterns of interaction in brain regions to better understand the pathology. 3. Most common machine learning classifier applied to diagnose ASD is Support vector machine (SVM) algorithm. The further implication of Robust SVM (variant of the single SVM) in research progress has improved the accuracy of diagnosing ASD from control group (CG). 4. Last but not the least Deep learning models helps in building model of profound classification accuracy. Early and accurate diagnosis of ASD intensity level leading to selection of correct treatment procedures and thus helps the autistic individual to undergo worth therapies or other relevant treatments.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131589349","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063615
K. J. Ghanashyam, Vatsala G A, A. Chaturvedi
In this mechanical life, each work is associated with the more than one goal which leads to the different multi decision model. In this rational world garbage is predominant problem which is faced by all country especially developing nations like India. The primary goal of this paper is to give a complete optimal solution for the wet garbage recycling plants such as bio methanation plant and compost plant. In this study, we designed a progressive Goal Programming model for fiscal management of wet garbage recycling plant at the apartment level. We discus about wet garbage compost plant and the optimal management of production of compost with the minimum usage of resources. Next, we took a wet garbage biogas plant for the study which produces the methane gas which can be used for lightings of apartment utility area which can save the electricity and to use the gas for cooking purpose also optimal management of production of the biogas. we have given this goal programming model for the fiscal management which can reduce the cost of maintenance in the apartments by minimizing the budget allocation to the maintain the compost production plant and biogas production plant.
{"title":"A Complete Optimal Solution for the Wet Garbage Recycling Plant in Apartments Cluster by Radical Multi-Objective Decision Model","authors":"K. J. Ghanashyam, Vatsala G A, A. Chaturvedi","doi":"10.1109/ICATIECE45860.2019.9063615","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063615","url":null,"abstract":"In this mechanical life, each work is associated with the more than one goal which leads to the different multi decision model. In this rational world garbage is predominant problem which is faced by all country especially developing nations like India. The primary goal of this paper is to give a complete optimal solution for the wet garbage recycling plants such as bio methanation plant and compost plant. In this study, we designed a progressive Goal Programming model for fiscal management of wet garbage recycling plant at the apartment level. We discus about wet garbage compost plant and the optimal management of production of compost with the minimum usage of resources. Next, we took a wet garbage biogas plant for the study which produces the methane gas which can be used for lightings of apartment utility area which can save the electricity and to use the gas for cooking purpose also optimal management of production of the biogas. we have given this goal programming model for the fiscal management which can reduce the cost of maintenance in the apartments by minimizing the budget allocation to the maintain the compost production plant and biogas production plant.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132577075","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}
Pub Date : 2019-03-01DOI: 10.1109/ICATIECE45860.2019.9063821
Sreenivasa Setty, M.S Kavana, Aman Ulla
Having seen this Wonderful creation of god, it amazes everyone for the beauty around us is mesmerizing, the nature and the habitats on this planet. Every day we happen to encounter beautiful sunrise and starting the day with full potential and ending day peacefully and calmness like a sunset at evening. But What about those people who aren’t blessed sight, wonder how they spend their entire life with only one color and no sight. So, the aim is to help the blind people sense the surrounding so that they can at least feel their surroundings and don’t miss the God’s creation. We for the first-time combining Internet of Things (IoT) and Machine Leaning (ML) together and creating a real-time product which can help the blind to hear and know things in their surroundings. We will be using a Raspberry Pi 3 microcontroller and a Raspberry pi cam to feed the video recording and then apply the Object Detection Algorithm on the video and detects objects for real.
{"title":"Goggle, GPS Tracker and Water Purity Detector","authors":"Sreenivasa Setty, M.S Kavana, Aman Ulla","doi":"10.1109/ICATIECE45860.2019.9063821","DOIUrl":"https://doi.org/10.1109/ICATIECE45860.2019.9063821","url":null,"abstract":"Having seen this Wonderful creation of god, it amazes everyone for the beauty around us is mesmerizing, the nature and the habitats on this planet. Every day we happen to encounter beautiful sunrise and starting the day with full potential and ending day peacefully and calmness like a sunset at evening. But What about those people who aren’t blessed sight, wonder how they spend their entire life with only one color and no sight. So, the aim is to help the blind people sense the surrounding so that they can at least feel their surroundings and don’t miss the God’s creation. We for the first-time combining Internet of Things (IoT) and Machine Leaning (ML) together and creating a real-time product which can help the blind to hear and know things in their surroundings. We will be using a Raspberry Pi 3 microcontroller and a Raspberry pi cam to feed the video recording and then apply the Object Detection Algorithm on the video and detects objects for real.","PeriodicalId":106496,"journal":{"name":"2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)","volume":"51 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115978635","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：