Pub Date : 2018-02-01DOI: 10.1109/SPIN.2018.8474095
Bharat Verma, P. Padhy
The square-of-error functions, such as Integral Square Error (ISE) and Integral Time Square Error (ITSE), are mostly used objective functions for controller design. In this paper, a new Tan Hyperbolic function weighted performance index is proposed for optimal PID controller design. The proposed objective function gives better PID controller design with improved robustness level than the Integral Square Error. The proposed method is validated with the help of two MATLAB Simulation examples with Genetic Algorithm.
{"title":"PID controller design with Hyperbolic Tangent weighted error function using GA","authors":"Bharat Verma, P. Padhy","doi":"10.1109/SPIN.2018.8474095","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474095","url":null,"abstract":"The square-of-error functions, such as Integral Square Error (ISE) and Integral Time Square Error (ITSE), are mostly used objective functions for controller design. In this paper, a new Tan Hyperbolic function weighted performance index is proposed for optimal PID controller design. The proposed objective function gives better PID controller design with improved robustness level than the Integral Square Error. The proposed method is validated with the help of two MATLAB Simulation examples with Genetic Algorithm.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123606806","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474046
M. Kopaczka, Lisa Ernst, Jakob Heckelmann, C. Schorn, R. Tolba, D. Merhof
Laboratory animals used for experiments need to be monitored closely for signs of pain and disstress. A well-established score is the mouse grimace scale (MGS), a method where defined morphological changes of the rodent’s eyes, ears, nose, whiskers and cheeks are assessed by human experts. While proven to be highly reliable, MGS assessment is a time-consuming task requiring manual processing of videos for key frame extraction and subsequent expert grading. While several tools have been presented to support this task for white laboratory rats, no methods are available for the most widely used mouse strain (C56BL6) which is inherently black. In our work, we present a set of methods to aid the expert in the annotation task by automatically processing a video and extracting images of single animals for further assessment. We introduce algorithms for separation of an image potentially containing multiple animals into single subimages displaying exactly one mouse. Additionally, we show how a fully convolutional neural network and a subsequent grading function can be designed in order to select frames that show a profile view of the mouse and therefore allow convenient grading. We evaluate our algorithms and show that the proposed pipeline works reliably and allows fast selection of relevant frames.
{"title":"Automatic Key Frame Extraction From Videos For Efficient Mouse Pain Scoring","authors":"M. Kopaczka, Lisa Ernst, Jakob Heckelmann, C. Schorn, R. Tolba, D. Merhof","doi":"10.1109/SPIN.2018.8474046","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474046","url":null,"abstract":"Laboratory animals used for experiments need to be monitored closely for signs of pain and disstress. A well-established score is the mouse grimace scale (MGS), a method where defined morphological changes of the rodent’s eyes, ears, nose, whiskers and cheeks are assessed by human experts. While proven to be highly reliable, MGS assessment is a time-consuming task requiring manual processing of videos for key frame extraction and subsequent expert grading. While several tools have been presented to support this task for white laboratory rats, no methods are available for the most widely used mouse strain (C56BL6) which is inherently black. In our work, we present a set of methods to aid the expert in the annotation task by automatically processing a video and extracting images of single animals for further assessment. We introduce algorithms for separation of an image potentially containing multiple animals into single subimages displaying exactly one mouse. Additionally, we show how a fully convolutional neural network and a subsequent grading function can be designed in order to select frames that show a profile view of the mouse and therefore allow convenient grading. We evaluate our algorithms and show that the proposed pipeline works reliably and allows fast selection of relevant frames.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116807188","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474110
Anand Jee, S. Hoque, Banani Talukdar, W. Arif
Cognitive radio employs dynamic spectrum access technology to enhance the utilization of spectrum for ad-hoc networks. The unlicensed users or the secondary users make use of cognitive radio technology to bring into function the underutilized part of licensed spectrum pool by means of an opportunistic spectrum access strategy for their continuous transmission. Upon identifying the arrival of the primary users in the same channel, the secondary users are required to perform spectrum handoff in order to maintain the service link. Link establishment and continuous transmission are two prime issues for a superior link maintenance mechanism. In this paper, we present a Markovian model for opportunistic spectrum access to analyze the link maintenance probability of the secondary users in a heterogeneous spectrum environment of licensed and unlicensed spectrum pools. The mathematical expression for the link maintenance probability of the secondary user is derived in terms of their blocking probability and dropping probability in CR ad-hoc networks. The effect of arrival and service rates of primary and secondary users on link maintenance probability is also presented considering the presence of classical users.
{"title":"Analysis of Link Maintenance Probability for Cognitive Radio Ad Hoc Networks","authors":"Anand Jee, S. Hoque, Banani Talukdar, W. Arif","doi":"10.1109/SPIN.2018.8474110","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474110","url":null,"abstract":"Cognitive radio employs dynamic spectrum access technology to enhance the utilization of spectrum for ad-hoc networks. The unlicensed users or the secondary users make use of cognitive radio technology to bring into function the underutilized part of licensed spectrum pool by means of an opportunistic spectrum access strategy for their continuous transmission. Upon identifying the arrival of the primary users in the same channel, the secondary users are required to perform spectrum handoff in order to maintain the service link. Link establishment and continuous transmission are two prime issues for a superior link maintenance mechanism. In this paper, we present a Markovian model for opportunistic spectrum access to analyze the link maintenance probability of the secondary users in a heterogeneous spectrum environment of licensed and unlicensed spectrum pools. The mathematical expression for the link maintenance probability of the secondary user is derived in terms of their blocking probability and dropping probability in CR ad-hoc networks. The effect of arrival and service rates of primary and secondary users on link maintenance probability is also presented considering the presence of classical users.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117237786","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474237
Kavita Goyal, M. Uddin
Research on cerebrospinal fluid (CSF) circulation in human brain has significant role to diagnose the several brain diseases. There are many internal and external factors which cause the unbalanced condition of CSF circulation in the human brain. Due to this unbalanced condition, the hydrocephalus condition may occur in the human brain. In this research paper, we develop a MATLAB based fuzzy model to establish the relationship between intracranial pressure (ICP) and unbalanced condition of cerebrospinal fluid in the brain and track the how intracranial pressure changed with unbalanced condition of CSF. This unbalanced condition of CSF is created by changing the CSF absorption rate of venous system.
{"title":"Fuzzy Estimation of Changes in Intracranial Pressure under hydrocephalus condition of Brain","authors":"Kavita Goyal, M. Uddin","doi":"10.1109/SPIN.2018.8474237","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474237","url":null,"abstract":"Research on cerebrospinal fluid (CSF) circulation in human brain has significant role to diagnose the several brain diseases. There are many internal and external factors which cause the unbalanced condition of CSF circulation in the human brain. Due to this unbalanced condition, the hydrocephalus condition may occur in the human brain. In this research paper, we develop a MATLAB based fuzzy model to establish the relationship between intracranial pressure (ICP) and unbalanced condition of cerebrospinal fluid in the brain and track the how intracranial pressure changed with unbalanced condition of CSF. This unbalanced condition of CSF is created by changing the CSF absorption rate of venous system.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123999899","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474186
Neha Samant, Poonam Sonar
Recently, breast cancer is major reason of cancer deaths among women. When cells in the breast tissue grow rapidly and gets divided without control breast cancer takes place which leads to formation of a mass or lump called as tumour. Here, the proposed method details comprehensive study and incorporation of image processing techniques to detect and classify tumours in terms of their accuracy. The proposed pre-processing technique removes all the unwanted labels present in an image to find region of interest (ROI). The various transformation methods such as Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Radon Transform are applied to the ROI. Later, Gray-Level Co-Occurrence Matrix (GLCM) features are obtained. Lastly, the classification accuracy of detected abnormality is being found out using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. The recommended method is verified using Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) publicly available dataset. From the implementation, it has been inferred that out of three proposed methods a highest of 93.89% accuracy is achieved using combination of DFT and SVM classifier for DDSM database whereas for MIAS it returns 80% accuracy.
{"title":"Mammogram Classification in Transform Domain","authors":"Neha Samant, Poonam Sonar","doi":"10.1109/SPIN.2018.8474186","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474186","url":null,"abstract":"Recently, breast cancer is major reason of cancer deaths among women. When cells in the breast tissue grow rapidly and gets divided without control breast cancer takes place which leads to formation of a mass or lump called as tumour. Here, the proposed method details comprehensive study and incorporation of image processing techniques to detect and classify tumours in terms of their accuracy. The proposed pre-processing technique removes all the unwanted labels present in an image to find region of interest (ROI). The various transformation methods such as Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), and Radon Transform are applied to the ROI. Later, Gray-Level Co-Occurrence Matrix (GLCM) features are obtained. Lastly, the classification accuracy of detected abnormality is being found out using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. The recommended method is verified using Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) publicly available dataset. From the implementation, it has been inferred that out of three proposed methods a highest of 93.89% accuracy is achieved using combination of DFT and SVM classifier for DDSM database whereas for MIAS it returns 80% accuracy.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125647540","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474140
G. Asha, Gowrishankar
Wireless Sensor Network (WSN) gives the accessibility of tiny and low-cost Sensor Nodes (SNs) with ability of observing, detecting and monitoring the environment. These SNs are deployed randomly inside the network. Due to the limited energy resources of SN, energy consumption is the major key in design of the cluster and route in WSN. In this paper, clusters are generated based on the K-means with Particle Swarm Optimization (PSO) algorithm and the routing is established by Glowworm Swarm Optimization (GSO), it is named as a PSO-GSO-WSN. This method improves the overall performance of system in terms of energy consumption of the node, Network life time, routing convergence and path optimization. The Performance of PSO-GSO-WSN is compared with the LEACH technique and the results were promising.
{"title":"An Energy aware Routing Mechanism in WSNs using PSO and GSO Algorithm","authors":"G. Asha, Gowrishankar","doi":"10.1109/SPIN.2018.8474140","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474140","url":null,"abstract":"Wireless Sensor Network (WSN) gives the accessibility of tiny and low-cost Sensor Nodes (SNs) with ability of observing, detecting and monitoring the environment. These SNs are deployed randomly inside the network. Due to the limited energy resources of SN, energy consumption is the major key in design of the cluster and route in WSN. In this paper, clusters are generated based on the K-means with Particle Swarm Optimization (PSO) algorithm and the routing is established by Glowworm Swarm Optimization (GSO), it is named as a PSO-GSO-WSN. This method improves the overall performance of system in terms of energy consumption of the node, Network life time, routing convergence and path optimization. The Performance of PSO-GSO-WSN is compared with the LEACH technique and the results were promising.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130402377","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474153
Vysakh S. Mohan, V. Sowmya, K. Soman
Detection of vehicles from aerial images have several real world implications in surveillance, military applications, traffic lot management, border patrol and traffic monitoring. The system proposed in this paper intends to automate the process of detecting vehicles from aerial images, rather than relying on a human operator. Here, we identify an optimum classification strategy for the proposed detection system, which is the initial stage of designing a vehicle detection pipeline. This research focuses on the feature extraction capabilities of standard neural network models like, Alexnet [6] and VGG-16 [7], which are compared against classic feature extraction techniques, like Histogram of Oriented Gradients and Singular Value Decomposition. The extracted features are benchmarked across standard machine learning algorithms such as Support Vector Machine and random forest. It is observed that the neural net extracted features gives an overall classification accuracy of 99% on the VEDAI dataset. The classification was treated as a binary class problem with vehicles as one class and rest everything as non-vehicles.
{"title":"Deep Neural Networks as Feature Extractors for Classification of Vehicles in Aerial Imagery","authors":"Vysakh S. Mohan, V. Sowmya, K. Soman","doi":"10.1109/SPIN.2018.8474153","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474153","url":null,"abstract":"Detection of vehicles from aerial images have several real world implications in surveillance, military applications, traffic lot management, border patrol and traffic monitoring. The system proposed in this paper intends to automate the process of detecting vehicles from aerial images, rather than relying on a human operator. Here, we identify an optimum classification strategy for the proposed detection system, which is the initial stage of designing a vehicle detection pipeline. This research focuses on the feature extraction capabilities of standard neural network models like, Alexnet [6] and VGG-16 [7], which are compared against classic feature extraction techniques, like Histogram of Oriented Gradients and Singular Value Decomposition. The extracted features are benchmarked across standard machine learning algorithms such as Support Vector Machine and random forest. It is observed that the neural net extracted features gives an overall classification accuracy of 99% on the VEDAI dataset. The classification was treated as a binary class problem with vehicles as one class and rest everything as non-vehicles.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130468850","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474233
V. S. Srinivas, A. Srikrishna, B. Eswara Reddy
Clustering is important and widely used in variety of machine learning applications. High dimensionality is a curse to clustering, that declines the algorithm performance in knowledge discovery and increases the algorithm complexity. The high dimensionality risk can be reduced with proper selection of good subset of features by avoiding irrelevant features. Selection of good clusters with proper subset of features is posed as a problem of optimization, can be solved with powerful Meta heuristic methods. Till date, a number of evolutionary based solutions are available for both problems automatic clustering and feature selection. From a decade, Automatic Clustering using Differential evolution is found to be one of the successful methods in automatic clustering considering all features. There is no algorithm that finds optimal clusters with simultaneous feature sub set selection. The paper proposes a novel Automatic Clustering with simultaneous Feature Subset Selection using Differential Evolution (ACFSDE) algorithm. ACFSDE is an enhanced variant to ACDE, defines a new chromosome structure for selection of optimal features and/for optimal clusters. Experiments are conducted in two fold; one is, using numeric UCI benchmark datasets and synthetic data sets. Second is to study the performance of ACFSDE for texture image segmentation by applying on images. The results on numeric data are evaluated using six clustering validity measures and are compared with five other existing clustering algorithms. The ACFSDE results are very prominent with more than 80% of average accuracy.
{"title":"Automatic Clustering simultaneous Feature Subset Selection using Differential Evolution","authors":"V. S. Srinivas, A. Srikrishna, B. Eswara Reddy","doi":"10.1109/SPIN.2018.8474233","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474233","url":null,"abstract":"Clustering is important and widely used in variety of machine learning applications. High dimensionality is a curse to clustering, that declines the algorithm performance in knowledge discovery and increases the algorithm complexity. The high dimensionality risk can be reduced with proper selection of good subset of features by avoiding irrelevant features. Selection of good clusters with proper subset of features is posed as a problem of optimization, can be solved with powerful Meta heuristic methods. Till date, a number of evolutionary based solutions are available for both problems automatic clustering and feature selection. From a decade, Automatic Clustering using Differential evolution is found to be one of the successful methods in automatic clustering considering all features. There is no algorithm that finds optimal clusters with simultaneous feature sub set selection. The paper proposes a novel Automatic Clustering with simultaneous Feature Subset Selection using Differential Evolution (ACFSDE) algorithm. ACFSDE is an enhanced variant to ACDE, defines a new chromosome structure for selection of optimal features and/for optimal clusters. Experiments are conducted in two fold; one is, using numeric UCI benchmark datasets and synthetic data sets. Second is to study the performance of ACFSDE for texture image segmentation by applying on images. The results on numeric data are evaluated using six clustering validity measures and are compared with five other existing clustering algorithms. The ACFSDE results are very prominent with more than 80% of average accuracy.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130502463","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474260
J. B. Alonso-Hernández, María L. Barragán-Pulido, José P. González-Torres, C. Travieso-González, M. A. Ferrer-Ballester, J. De León y De Juan, M. Dutta, Garima Vyas
The objective of this report is to design a mechanism of classification that, through electroglottography, helps distinguishing between healthy and pathological subjects, as well as maximizing the efficiency of electroglottography through an optimal configuration of the classification parameters of SVM (Support Vector Machine). The proposed system consists in parameterizing electroglottography signals obtained in the open database, Saarbruecken Voice DataBase, and to draw the more relevant characteristics in temporary, frequency and cepstral domain. Afterwards, the samples are classified with a SVM. The study carried out contains different combinations of parameters and characteristics in order to assess the appropriate configuration considering: the recorded vowel, the type of windowing, the configured SVM percentages of training and the different values of the SVM parameters. The results obtained are compared to the real data, in this way, it is obtained the performance values of the system (precision, sensitivity and specificity) for each features configuration contemplated. The best results come from vowel I, 30 ms windowing with 50% overlapping, percentages of training around 80–90% (PES higher than PEP) and γ and σ2 values of 100 and 0.1 respectively. This study expects to provide a greater knowledge to the classification methods based on electroglottography as an aid in diagnosing laryngeal diseases.
{"title":"New Feature Extraction from Electroglottographic Signals Applied to Automatic Detection of Laryngeal Pathologies","authors":"J. B. Alonso-Hernández, María L. Barragán-Pulido, José P. González-Torres, C. Travieso-González, M. A. Ferrer-Ballester, J. De León y De Juan, M. Dutta, Garima Vyas","doi":"10.1109/SPIN.2018.8474260","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474260","url":null,"abstract":"The objective of this report is to design a mechanism of classification that, through electroglottography, helps distinguishing between healthy and pathological subjects, as well as maximizing the efficiency of electroglottography through an optimal configuration of the classification parameters of SVM (Support Vector Machine). The proposed system consists in parameterizing electroglottography signals obtained in the open database, Saarbruecken Voice DataBase, and to draw the more relevant characteristics in temporary, frequency and cepstral domain. Afterwards, the samples are classified with a SVM. The study carried out contains different combinations of parameters and characteristics in order to assess the appropriate configuration considering: the recorded vowel, the type of windowing, the configured SVM percentages of training and the different values of the SVM parameters. The results obtained are compared to the real data, in this way, it is obtained the performance values of the system (precision, sensitivity and specificity) for each features configuration contemplated. The best results come from vowel I, 30 ms windowing with 50% overlapping, percentages of training around 80–90% (PES higher than PEP) and γ and σ2 values of 100 and 0.1 respectively. This study expects to provide a greater knowledge to the classification methods based on electroglottography as an aid in diagnosing laryngeal diseases.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125404191","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 : 2018-02-01DOI: 10.1109/SPIN.2018.8474116
Devashish Arora, M. Rawat
This paper presents the analysis of side lobe suppression using Genetic Algorithm. Results of window techniques for large number of antenna elements are used as a motivation for the Genetic Algorithm based side lobe suppression. Analysis of results in connection with suppression of side lobes using Genetic Algorithm are observed for number of metrics. The aforementioned implementation of Genetic Algorithm is used with LTE signal whose extension can be used for practical implementation for upcoming 5G technology in millimeter wavebands.
{"title":"Beam Shaping using Genetic Algorithm for Large Array Beamforming","authors":"Devashish Arora, M. Rawat","doi":"10.1109/SPIN.2018.8474116","DOIUrl":"https://doi.org/10.1109/SPIN.2018.8474116","url":null,"abstract":"This paper presents the analysis of side lobe suppression using Genetic Algorithm. Results of window techniques for large number of antenna elements are used as a motivation for the Genetic Algorithm based side lobe suppression. Analysis of results in connection with suppression of side lobes using Genetic Algorithm are observed for number of metrics. The aforementioned implementation of Genetic Algorithm is used with LTE signal whose extension can be used for practical implementation for upcoming 5G technology in millimeter wavebands.","PeriodicalId":184596,"journal":{"name":"2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126607277","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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