Pub Date : 2019-04-01DOI: 10.1109/ICOEI.2019.8862791
O. P. Mishra, Gaurav Morghare
To move from 4G to 5G technology spectrum deficiency is an important criteria which can be overcome by an efficient technology called Cognitive Radio (CR). This can be achieved by continuous sensing the spectrum band, and detecting the unused frequency bands which would be not used by licensed band and without any unwanted interference to the primary user or licensed user (PU). We Proposed NN-PSO method for network selection and Fast Delivery handover route mechanism in order to increase system efficiency by consider more number of SUs in network and providing to their preferences, and respect the criteria of primary network operators, at the same time. The goal is to provide SUs good network and fast delivery handover route with a high QoS based on the criteria of SU, subject to the interference boundation of each existing network with other channels. The proposed technique Neural Network and Particle swarm optimization would be use to solve the Network selection and optimization problem. Finally, experimental results and numerical parameters represent the effectiveness of the proposed NN-PSO methods to finding a near-optimal solution for network selection.
{"title":"An Efficient approach Network Selection and Fast Delivery Handover Route 5G LTE Network","authors":"O. P. Mishra, Gaurav Morghare","doi":"10.1109/ICOEI.2019.8862791","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862791","url":null,"abstract":"To move from 4G to 5G technology spectrum deficiency is an important criteria which can be overcome by an efficient technology called Cognitive Radio (CR). This can be achieved by continuous sensing the spectrum band, and detecting the unused frequency bands which would be not used by licensed band and without any unwanted interference to the primary user or licensed user (PU). We Proposed NN-PSO method for network selection and Fast Delivery handover route mechanism in order to increase system efficiency by consider more number of SUs in network and providing to their preferences, and respect the criteria of primary network operators, at the same time. The goal is to provide SUs good network and fast delivery handover route with a high QoS based on the criteria of SU, subject to the interference boundation of each existing network with other channels. The proposed technique Neural Network and Particle swarm optimization would be use to solve the Network selection and optimization problem. Finally, experimental results and numerical parameters represent the effectiveness of the proposed NN-PSO methods to finding a near-optimal solution for network selection.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128676105","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862715
Meghanan Bhairu Mutgekar, P. Bhaskar
In today's scenario, there is huge involvement of transformations. These scenarios include digital signal processing and image processing. It is very essential in all sectors. Transformation techniques are useful as it makes analysis easier, reliable and relevant. Discrete cosine transform is the most important step in any signal frequency based analysis or multimedia compression and decompression methods. In this era where storage requirements are vast compression of data is urgent need of time. The DCT implementation on FPGA and its performance evaluation is focused in this paper. The fast DCT algorithm using FFT based approach is considered for improvement in performance of DCT and its FPGA implementation is verified using Nexys 4 DDR board having Artix 7 series Xilinx processor. The results obtained are analysis based on utilization overhead, power allocation scenarios and time consumption analysis which are found to be satisfactory.
{"title":"Analysis of DCT and FAST DCT using soft core processor","authors":"Meghanan Bhairu Mutgekar, P. Bhaskar","doi":"10.1109/ICOEI.2019.8862715","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862715","url":null,"abstract":"In today's scenario, there is huge involvement of transformations. These scenarios include digital signal processing and image processing. It is very essential in all sectors. Transformation techniques are useful as it makes analysis easier, reliable and relevant. Discrete cosine transform is the most important step in any signal frequency based analysis or multimedia compression and decompression methods. In this era where storage requirements are vast compression of data is urgent need of time. The DCT implementation on FPGA and its performance evaluation is focused in this paper. The fast DCT algorithm using FFT based approach is considered for improvement in performance of DCT and its FPGA implementation is verified using Nexys 4 DDR board having Artix 7 series Xilinx processor. The results obtained are analysis based on utilization overhead, power allocation scenarios and time consumption analysis which are found to be satisfactory.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129062304","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862751
Ch. Narasimha Kumar, A. Madhumitha, N. S. Preetam, P. Gupta, J. P. Anita
As the complexity of the digital circuits increases there should be a check on its functionality in a more exhaustive way. So here comes the need for test pattern generation technique to detect the presence of the faults and to obtain the test patterns. The switching activity in digital circuits may overheat the circuit due to which unwanted responses may occur. This may lead to a high power consumption, so it is necessary to reduce the power. The proposed paper includes generation of test patterns and a technique for test power reduction in VLSI. The results have been validated using ISCAS’85 and ISCAS’89 benchmark circuits.
{"title":"Fault Diagnosis Using Automatic Test Pattern Generation and Test Power Reduction Technique for VLSI Circuits","authors":"Ch. Narasimha Kumar, A. Madhumitha, N. S. Preetam, P. Gupta, J. P. Anita","doi":"10.1109/ICOEI.2019.8862751","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862751","url":null,"abstract":"As the complexity of the digital circuits increases there should be a check on its functionality in a more exhaustive way. So here comes the need for test pattern generation technique to detect the presence of the faults and to obtain the test patterns. The switching activity in digital circuits may overheat the circuit due to which unwanted responses may occur. This may lead to a high power consumption, so it is necessary to reduce the power. The proposed paper includes generation of test patterns and a technique for test power reduction in VLSI. The results have been validated using ISCAS’85 and ISCAS’89 benchmark circuits.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129450738","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862631
Tarun Kumar Dugar, S. Gowtham, U. K. Chakraborty
Authorship Identification as a task has been long studied and explored. Historically, authorship claims were ratified for copyright issues after the death of the author for unpublished work through style matching. The immense growth in the reach of internet technologies has once again brought to the fore the importance of authorship identification. An application opening up in areas like Intellectual Property Right settlement, Copyrights, Plagiarism, Cyber Crime and Forensics, authorship identification is now an area of active research. The current work presents a Deep Neural Network based approach to authorship identification from a large corpus. The experiments carried out bring out the applicability of Deep Neural Networks for the task and also highlights the importance of hyperparameter tuning for the purpose. Results show that a proper choice and balance in the hyperparameter setting can improve already established outcomes.
{"title":"Hyperparameter Tuning for Enhanced Authorship Identification Using Deep Neural Networks","authors":"Tarun Kumar Dugar, S. Gowtham, U. K. Chakraborty","doi":"10.1109/ICOEI.2019.8862631","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862631","url":null,"abstract":"Authorship Identification as a task has been long studied and explored. Historically, authorship claims were ratified for copyright issues after the death of the author for unpublished work through style matching. The immense growth in the reach of internet technologies has once again brought to the fore the importance of authorship identification. An application opening up in areas like Intellectual Property Right settlement, Copyrights, Plagiarism, Cyber Crime and Forensics, authorship identification is now an area of active research. The current work presents a Deep Neural Network based approach to authorship identification from a large corpus. The experiments carried out bring out the applicability of Deep Neural Networks for the task and also highlights the importance of hyperparameter tuning for the purpose. Results show that a proper choice and balance in the hyperparameter setting can improve already established outcomes.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485194","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862556
S. Shanmugam, J. Devi
The idea in this work is combining the GLOH algorithm and PCA to detect and classify the color features of vegetables (or) fruits. The detection and classification of color features of vegetables (or) fruits is an identified phase of research in agriculture. In technical way, image processing defines the processing of signals, gets input as an image, photo or video frame; and the output of this process can be an image or image related parameters. The aim of the paper is detecting and classifying the vegetables / fruits according to its color. It contains three parts, first two parts uses algorithms such as GLOH and SIFT-PCA, used for feature extraction and its reduction and key point extraction and dimensionality reduction. The third part of this work uses PCA to classifies the vegetables. The final result of the work is achieved by integrating the above three parts.
{"title":"A Neural Network Based Approach For Recognition And Classification Of Color Features For Vegetables","authors":"S. Shanmugam, J. Devi","doi":"10.1109/ICOEI.2019.8862556","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862556","url":null,"abstract":"The idea in this work is combining the GLOH algorithm and PCA to detect and classify the color features of vegetables (or) fruits. The detection and classification of color features of vegetables (or) fruits is an identified phase of research in agriculture. In technical way, image processing defines the processing of signals, gets input as an image, photo or video frame; and the output of this process can be an image or image related parameters. The aim of the paper is detecting and classifying the vegetables / fruits according to its color. It contains three parts, first two parts uses algorithms such as GLOH and SIFT-PCA, used for feature extraction and its reduction and key point extraction and dimensionality reduction. The third part of this work uses PCA to classifies the vegetables. The final result of the work is achieved by integrating the above three parts.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121192651","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862772
Pooja Rajput, U. Mittal, P. Mishra, A. T. Nimal, J. Rawat
This paper presents the effect of various tuning combinations on SAW device performance for chemical sensing application along with simulations and experimental results. SAW device with suitable tuning arrangement provides clean resonant frequency, specified Bandwidth and better stability for SAW based oscillator required for chemical sensing application.
{"title":"Effect of Tuning on SAW Device Characteristics","authors":"Pooja Rajput, U. Mittal, P. Mishra, A. T. Nimal, J. Rawat","doi":"10.1109/ICOEI.2019.8862772","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862772","url":null,"abstract":"This paper presents the effect of various tuning combinations on SAW device performance for chemical sensing application along with simulations and experimental results. SAW device with suitable tuning arrangement provides clean resonant frequency, specified Bandwidth and better stability for SAW based oscillator required for chemical sensing application.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396122","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862732
Krushil M. Bhadani, Bijal Talati
Online advertising is a huge, rapidly growing advertising market in today's world. The common form of online advertising is using image ads. A decision is made (often in real time) every time when a user sees an ad, and the advertiser is eager to determine the best ad to display. Consequently, many algorithms have been developed in order to calculate the optimal ad in order to show that the current user is available at the present time. Typically, these algorithms focus on variations of the ad, optimizing among different properties such as background color, image size, or set of images but none of them define the property of objects. Our study looks at new qualities of ads that can be determined before an ad is shown (rather than online optimization) and defines which ad image's objects are most likely to be successful. We present a set of algorithms that utilize machine learning to investigate online advertising and to construct object detection models which can foresee objects that are likely to be in successive ad image. The focus of results is to get high success rate in ad image with objects appear in it. In this paper we discuss two approaches, using cascading trainer and R-CNN network. We have compare this two approaches using HOG and CNN features. R-CNN gives better result than cascading but require more time to train.
{"title":"Exploring online ad images using a clustering approach","authors":"Krushil M. Bhadani, Bijal Talati","doi":"10.1109/ICOEI.2019.8862732","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862732","url":null,"abstract":"Online advertising is a huge, rapidly growing advertising market in today's world. The common form of online advertising is using image ads. A decision is made (often in real time) every time when a user sees an ad, and the advertiser is eager to determine the best ad to display. Consequently, many algorithms have been developed in order to calculate the optimal ad in order to show that the current user is available at the present time. Typically, these algorithms focus on variations of the ad, optimizing among different properties such as background color, image size, or set of images but none of them define the property of objects. Our study looks at new qualities of ads that can be determined before an ad is shown (rather than online optimization) and defines which ad image's objects are most likely to be successful. We present a set of algorithms that utilize machine learning to investigate online advertising and to construct object detection models which can foresee objects that are likely to be in successive ad image. The focus of results is to get high success rate in ad image with objects appear in it. In this paper we discuss two approaches, using cascading trainer and R-CNN network. We have compare this two approaches using HOG and CNN features. R-CNN gives better result than cascading but require more time to train.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127611651","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862653
Feba Joseph, V. Anoop
Due to simplicity and non invasive nature, ECG is used as a reference for detecting heart diseases although it measures the electrical activity of the heart. The electrical signal of each heart beat depicts any of the abnormalities present in the heart. QRS complexes, R, P and T waves are the major characteristic in ECG signal analysis. Reliable detection of these fiducial waves is used for analysing the performance of system. There are various difficulties while detecting these waves mainly due to baseline oscillations, morphology of waveforms and the frequency overlapping. For the accurate categorization of arrhythmia, complex difference in ECG morphology seems to be a great challenge. Thus the proper detection of P wave, QRS complex, R wave and T wave are important for the accurate and reliable detection.
{"title":"Detection of Waves in ECG for Arrhythmia Classification","authors":"Feba Joseph, V. Anoop","doi":"10.1109/ICOEI.2019.8862653","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862653","url":null,"abstract":"Due to simplicity and non invasive nature, ECG is used as a reference for detecting heart diseases although it measures the electrical activity of the heart. The electrical signal of each heart beat depicts any of the abnormalities present in the heart. QRS complexes, R, P and T waves are the major characteristic in ECG signal analysis. Reliable detection of these fiducial waves is used for analysing the performance of system. There are various difficulties while detecting these waves mainly due to baseline oscillations, morphology of waveforms and the frequency overlapping. For the accurate categorization of arrhythmia, complex difference in ECG morphology seems to be a great challenge. Thus the proper detection of P wave, QRS complex, R wave and T wave are important for the accurate and reliable detection.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121979040","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862588
P. Aswin, J. Chandana, Seethal Reghunath, Maya Menon
This paper proposes a method for detecting and recognizing the object using Stereo Vision, Scale-Invariant Feature Transform (SIFT) and Fast library for approximate Nearest Neighbors (FLANN) concept with its implementation on an embedded system. Using stereo vision on the microprocessor Raspberry Pi, the implemented system takes the two images produced as input, calculates the disparity map which provides the relative depth information. Using this map and the Scale-Invariant Feature Transform (SIFT), features are obtained and matched with a database having large collection of images. This implementation uses Fast Library for Approximate Nearest Neighbors (FLANN), which unlike the Brute-Force matching algorithm can support large databases. This system gives a voice output when the object is recognized by text to speech conversion.
本文提出了一种利用立体视觉、尺度不变特征变换(SIFT)和快速近似近邻库(FLANN)概念进行目标检测和识别的方法，并在嵌入式系统上实现。实现的系统利用树莓派微处理器上的立体视觉，将生成的两幅图像作为输入，计算视差图，从而提供相对深度信息。利用该映射和比例不变特征变换(SIFT)，获得特征并与具有大量图像集的数据库进行匹配。该实现采用了FLANN (Fast Library for Approximate Nearest Neighbors)算法，与蛮力匹配算法不同，FLANN可以支持大型数据库。该系统通过文本到语音的转换来识别对象时，给出语音输出。
{"title":"Stereo-Vision Based System For Object Detection And Recognition","authors":"P. Aswin, J. Chandana, Seethal Reghunath, Maya Menon","doi":"10.1109/ICOEI.2019.8862588","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862588","url":null,"abstract":"This paper proposes a method for detecting and recognizing the object using Stereo Vision, Scale-Invariant Feature Transform (SIFT) and Fast library for approximate Nearest Neighbors (FLANN) concept with its implementation on an embedded system. Using stereo vision on the microprocessor Raspberry Pi, the implemented system takes the two images produced as input, calculates the disparity map which provides the relative depth information. Using this map and the Scale-Invariant Feature Transform (SIFT), features are obtained and matched with a database having large collection of images. This implementation uses Fast Library for Approximate Nearest Neighbors (FLANN), which unlike the Brute-Force matching algorithm can support large databases. This system gives a voice output when the object is recognized by text to speech conversion.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115946759","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 : 2019-04-01DOI: 10.1109/ICOEI.2019.8862710
Selvam S Arul, Fomra R Yashank, K. Aswin, P. Aswin, Sundaram G. A. Shanmugha
Vehicular communication systems are networks in which vehicles and roadside units constitute the communicating nodes, providing each other with critical operational information such as safety warnings and traffic situations. They can prove effective in avoiding accidents and traffic congestion. An intelligent transport system will process the data from vehicle-to-vehicle (V2 $V$) and vehicle-to-infrastructure (V2I) communication networks in order to improve traffic management. This shall allow vehicles to also communicate with roadside infrastructure such as sensors, passengers, traffic databases, signaling systems, and among themselves. The tasks included in the study presented here consider a lot of real time data analysis, automated trade-off negotiations, algorithms for which are deployed on embedded computing platforms to perform tasks identified as part of efficient traffic reorganization and flow. The outcomes are destined to make a significant impact in the form of financial engineering by means of high frequency trading of the stake holders' stock.
{"title":"Influencing High Frequency Trading Patterns in Highway Operator Stock using Efficient V2V & V2I C4 Solutions for Vehicular Networks","authors":"Selvam S Arul, Fomra R Yashank, K. Aswin, P. Aswin, Sundaram G. A. Shanmugha","doi":"10.1109/ICOEI.2019.8862710","DOIUrl":"https://doi.org/10.1109/ICOEI.2019.8862710","url":null,"abstract":"Vehicular communication systems are networks in which vehicles and roadside units constitute the communicating nodes, providing each other with critical operational information such as safety warnings and traffic situations. They can prove effective in avoiding accidents and traffic congestion. An intelligent transport system will process the data from vehicle-to-vehicle (V2 $V$) and vehicle-to-infrastructure (V2I) communication networks in order to improve traffic management. This shall allow vehicles to also communicate with roadside infrastructure such as sensors, passengers, traffic databases, signaling systems, and among themselves. The tasks included in the study presented here consider a lot of real time data analysis, automated trade-off negotiations, algorithms for which are deployed on embedded computing platforms to perform tasks identified as part of efficient traffic reorganization and flow. The outcomes are destined to make a significant impact in the form of financial engineering by means of high frequency trading of the stake holders' stock.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134037617","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}
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