2019 IEEE Conference on Information and Communication Technology最新文献_第5页

Energy Conservation Schemes of Wireless Sensor Networks for IoT Applications: A Survey 面向物联网应用的无线传感器网络节能方案综述

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066228

Gagandeep Kaur, M. Bhattacharya, P. Chanak

引用次数: 0

Neural Machine Translation: English to Hindi 神经机器翻译:英语到印地语

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066238

Sahinur Rahman Laskar, Abinash Dutta, Partha Pakray, Sivaji Bandyopadhyay

引用次数: 3

An Enhanced Criterion for Induced $H_{infty}$ Stability of Discrete-time Systems with Time-varying Delay and External Disturbance 具有时变时滞和外部干扰的离散系统诱导$H_{infty}$稳定性的一个增强判据

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066234

Kalpana Singh, Dinesh Chaurasia, V. Kandanvli

引用次数: 0

An Observer based FLL to Estimate the Grid Parameters of Three Phase Systems 一种基于观测器的三相系统栅格参数估计方法

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066135

R. Arthi, K. Arun, K. Selvajyothi

引用次数: 0

Automated Helmet Detection for Multiple Motorcycle Riders using CNN 使用CNN的多个摩托车骑手自动头盔检测

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066191

Madhuchhanda Dasgupta, O. Bandyopadhyay, Sanjay Chatterji

{"title":"Automated Helmet Detection for Multiple Motorcycle Riders using CNN","authors":"Madhuchhanda Dasgupta, O. Bandyopadhyay, Sanjay Chatterji","doi":"10.1109/CICT48419.2019.9066191","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066191","url":null,"abstract":"Automated detection of traffic rule violators is an essential component of any smart traffic system. In a country like India with high density of population in all big cities, motorcycle is one of the main modes of transport. It is observed that most of the motorcyclists avoid the use of helmet within the city or even in highways. Use of helmet can reduce the risk of head and severe brain injury of the motorcyclists in most of the motorcycle accident cases. Today violation of most of the traffic and safety rules are detected by analysing the traffic videos captured by surveillance camera. This paper proposes a framework for detection of single or multiple riders travel on a motorcycle without wearing helmets. In the proposed approach, at first stage, motorcycle riders are detected using YOLOv3 model which is an incremental version of YOLO model, the state-of-the-art method for object detection. In the second stage, a Convolutional Neural Network (CNN) based architecture has been proposed for helmet detection of motorcycle riders. The proposed model is evaluated on traffic videos and the obtained results are promising in comparison with other CNN based approaches.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115963573","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: 39

Improved Coupled Autoencoder based Zero Shot Recognition using Active Learning 基于主动学习的改进耦合自编码器零镜头识别

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066127

Upendra Pratap Singh, Kaustubh Rakesh, Rishabh, Vipul Kumar, Krishna Pratap Singh

{"title":"Improved Coupled Autoencoder based Zero Shot Recognition using Active Learning","authors":"Upendra Pratap Singh, Kaustubh Rakesh, Rishabh, Vipul Kumar, Krishna Pratap Singh","doi":"10.1109/CICT48419.2019.9066127","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066127","url":null,"abstract":"Zero shot learning seeks to learn useful patterns in the source domain and identify novel concepts in the target domain. This transfer learning paradigm has recently gained immense popularity given the inherent limitations in data acquisition and subsequent annotation for a task (or domain). While typical zero shot learning methods utilize all the classes (and their instances) in the source domain in a passive way, we, in our work, actively use only a handful of relevant classes for learning in the source domain. With this intelligent data subset, we jointly learn the source and target domain parameters using coupled semantic autoencoders. This joint learning reduces the projection domain shift problem. We further extend the above model for word embedding based semantic space as well. For classes with no word embedding, we have solved prototype sparsity problem by training a neural network with all classes that has one. This neural network seeks to learn a mapping from attribute space to word embedding space. Experiments on AWA2 and CUB-UCSD datasets confirm the superiority of our hybrid approach over state of art methods by up to 16% and 8% in attribute and word embedding space respectively.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133729132","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

Supply and Demand Planning of Electricity Power: A Comprehensive Solution 电力供需规划:一个综合解决方案

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066184

S. Perera, S.J. Dissanayake, Dinithi Fernando, Sehan De Silva, W. Rankothge

{"title":"Supply and Demand Planning of Electricity Power: A Comprehensive Solution","authors":"S. Perera, S.J. Dissanayake, Dinithi Fernando, Sehan De Silva, W. Rankothge","doi":"10.1109/CICT48419.2019.9066184","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066184","url":null,"abstract":"Electrical energy is one of the fastest growing energy demands in the world. Uncertainty in supplying the demand can threaten the social economic aspects of a country. The biggest driver of electrical demand is weather. Climatic changes not only affect the demand but also renewable energy supply. Wind and Solar are two alternative energy sources with less pollution. We have proposed a platform which helps energy providers, energy traders with services related to electricity supply and demand planning, with following modules. (1) Forecasting electricity consumption patterns (2) Forecasting wind power generation (3) Optimizing Load Shedding. Our platform has been implemented using statistical and machine learning techniques: Multi-Linear Regression for consumption prediction, Random forest regression for wind power forecast, and genetic algorithm to optimize load shedding. Our results show that, using our proposed module, we can minimize the imbalance between the supply and demand of electricity by predicting the consumption patterns of consumers, predicting the wind power generation and by selecting the best feeder to be selected for load shedding under given constraints.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130962292","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: 5

LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture LEDCOM:一种新型高效的基于LED的精准农业通信技术

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066177

K. V. Sai Vineeth, Y. R. Vara Prasad, Shivendra Dubey, H. Venkataraman

{"title":"LEDCOM: A Novel and Efficient LED Based Communication for Precision Agriculture","authors":"K. V. Sai Vineeth, Y. R. Vara Prasad, Shivendra Dubey, H. Venkataraman","doi":"10.1109/CICT48419.2019.9066177","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066177","url":null,"abstract":"Wireless Sensor Networks and Satellite Remote Sensing are some of the existing techniques that are used to collect, analyze and interpret data from the agricultural crop sites. However, there are certain limitations common to both of these techniques that are concerned with the latency and the resolution of the data collected. UAVs (Unmanned Aerial Vehicles) are becoming another alternative that has become integral nowadays due to its affordable and scalable nature while offering user friendly requirements and customizations. This proposes a novel and cost-effective technique (LEDCOM) that harnesses the capabilities of ground sensors and unmanned UAV while using computer vision methods to produce a qualitative data analysis system that describes the crop site under supervision. An UAV is assumed to collect the ground based sensor node data in the form of binary patterns on LED Arrays that is encoded in the image taken by a camera of a drone. Image processing techniques are used to identify and decode the LED sequences from the arrays. The performance of the proposed system is evaluated under different features and image resolutions within the same lighting conditions. A promising performance is observed for LED pattern identification from the challenging images taken from a height.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116336741","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

REHR: Residual Energy based Hybrid Routing Protocol for Wireless Sensor Networks 基于剩余能量的无线传感器网络混合路由协议

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066144

Akhilesh Panchal, R. Singh

{"title":"REHR: Residual Energy based Hybrid Routing Protocol for Wireless Sensor Networks","authors":"Akhilesh Panchal, R. Singh","doi":"10.1109/CICT48419.2019.9066144","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066144","url":null,"abstract":"Wireless Sensor Network (WSN) is being used in the Internet of Thing (IoT) applications, where sensing of information is the prime object. WSN consists of a large number of nodes which are used for collecting valuable information from the target area, and also transmit it to respective destination. Therefore, the energy of node should be utilized efficiently, it is the most fundamental challenge of WSNs, and primarily depends on the packet routing strategy. In this paper, we are proposing a Residual Energy based Hybrid Routing (REHR) protocol, in-which direct nodes are selected by the optimum value of nodes, thereafter direct nodes and clustering-nodes are hybridly used for efficient packet transmission. Here, the packet routing strategy uses single-hop and multi-hop communication, which depends upon relative euclidean distances and residual energy of the alive nodes. This technique gives the optimal usage of the node's energy to save its energy as well as for reducing the load of the CHs. We have shown that the results of our proposed work are better in terms of the lifetime and residual energy of the network.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116242343","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: 3

Optimizing Driver Assistance Systems for Real-Time performance on Resource Constrained GPUs 在资源受限的gpu上优化驾驶员辅助系统的实时性能

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066239

O. Ramwala, C. Paunwala, M. Paunwala

{"title":"Optimizing Driver Assistance Systems for Real-Time performance on Resource Constrained GPUs","authors":"O. Ramwala, C. Paunwala, M. Paunwala","doi":"10.1109/CICT48419.2019.9066239","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066239","url":null,"abstract":"The importance of Advanced Driver Assistance Systems has increased tremendously due to their ability to reduce road fatalities by facilitating drivers for appropriate action selection in circumstances involving high probability of collisions. One of the major factors contributing to accidents on road is driver distraction and drowsiness. A variety of algorithms including several Forward Collision Warning algorithms have been proposed to alleviate the issue to road accidents. These algorithms are promising approaches to mitigate this problem. However, most of these proposals are computationally complex algorithms and require powerful GPUs to perform in real-time. Such GPUs are not only expensive but also have high power consumption. Thus, it is necessary to yield real time performance on resource constrained GPUs like NVIDIA's Jetson TX2 which is not only one of the most eminent GPU-enabled platforms for autonomous systems but also cost effective and power efficient [1]. This paper proposes utilization of pruning of Neural Networks and TensorFlow TensorRT to optimize computationally complex algorithms utilized for Driver Assistance Systems to obtain real-time functionality on TX2 without compromising the accuracy of the system.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124774404","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