Pub Date : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723826
S. Monira, Zaman M. Faisal, Hideo Hirose
Rainfall forecasting has been one of the most scientifically and technologically challenging task in the climate dynamics and climate prediction theory around the world in the last century. This is due to the great effect of forecasting on human activities and also for the significant computational advances that are utilized in this research field. In this paper our main objective is to forecast over a very short-term and specified local area weather using local data which is not always available by forecast center but will be available in the future by social network or some other methods. For this purpose in this paper we have applied three different algorithms belonging to the paradigm of artificial intelligence in short-term forecast of rainfalls (24 hours) using a regional rainfall data of Bihar (India) as a case study. This forecast is about predicting the categorical rainfall (some pre-defined category based on the amount of total daily rainfall) amount for the next day. We have used two classifier ensemble methods and a single classifier model for this purpose. The ensemble methods used in this paper are LogitBoosting (LB), and Random Forest (RF). The single classifier model is a Least Square Support Vector Machine (LS-SVM). We have optimized each of the models on validation sets and then forecast with the optimum model on the out of sample (or test) dataset. We have also verified our forecast results with some of the latest verification tools available. The experimental and verification results suggest that these methods are capable of efficiently forecasting the categorical rainfall amount in short term.
{"title":"Comparison of artificially intelligent methods in short term rainfall forecast","authors":"S. Monira, Zaman M. Faisal, Hideo Hirose","doi":"10.1109/ICCITECHN.2010.5723826","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723826","url":null,"abstract":"Rainfall forecasting has been one of the most scientifically and technologically challenging task in the climate dynamics and climate prediction theory around the world in the last century. This is due to the great effect of forecasting on human activities and also for the significant computational advances that are utilized in this research field. In this paper our main objective is to forecast over a very short-term and specified local area weather using local data which is not always available by forecast center but will be available in the future by social network or some other methods. For this purpose in this paper we have applied three different algorithms belonging to the paradigm of artificial intelligence in short-term forecast of rainfalls (24 hours) using a regional rainfall data of Bihar (India) as a case study. This forecast is about predicting the categorical rainfall (some pre-defined category based on the amount of total daily rainfall) amount for the next day. We have used two classifier ensemble methods and a single classifier model for this purpose. The ensemble methods used in this paper are LogitBoosting (LB), and Random Forest (RF). The single classifier model is a Least Square Support Vector Machine (LS-SVM). We have optimized each of the models on validation sets and then forecast with the optimum model on the out of sample (or test) dataset. We have also verified our forecast results with some of the latest verification tools available. The experimental and verification results suggest that these methods are capable of efficiently forecasting the categorical rainfall amount in short term.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133333512","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723896
K. Mamun, Manoj Banik, M. Mace, Mark E. Lutmen, R. Vaidyanathan, Shouyan Wang
Tongue movement ear pressure (TMEP) signals have been used to generate controlling commands in assistive human machine interfaces aimed at people with disabilities. The objective of this study is to classify the controlled movement related signals of an intended action from internally occurring physiological signals which can interfere with the inter-movement classification. TMEP signals were collected, corresponding to six types of controlled movements and activity relating to the potentially interfering environment including when a subject spoke, coughed or drank. The signal processing algorithm involved TMEP signal detection, segmentation, feature extraction and selection, and classification. The features of the segmented TMEP signals were extracted using the wavelet packet transform (WPT). A multi-layer neural network was then designed and tested based on statistical properties of the WPT coefficients. The average classification performance for discriminating interference and controlled movement related TMEP signal achieved 97.05%. The classification of TMEP signals based on the WPT is robust and the interferences to the controlling commands of TMEP signals in assistive human machine interface can be significantly reduced using the multi-layer neural network when considered in this challenging environment.
{"title":"Multi-layer neural network classification of tongue movement ear pressure signal for human machine interface","authors":"K. Mamun, Manoj Banik, M. Mace, Mark E. Lutmen, R. Vaidyanathan, Shouyan Wang","doi":"10.1109/ICCITECHN.2010.5723896","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723896","url":null,"abstract":"Tongue movement ear pressure (TMEP) signals have been used to generate controlling commands in assistive human machine interfaces aimed at people with disabilities. The objective of this study is to classify the controlled movement related signals of an intended action from internally occurring physiological signals which can interfere with the inter-movement classification. TMEP signals were collected, corresponding to six types of controlled movements and activity relating to the potentially interfering environment including when a subject spoke, coughed or drank. The signal processing algorithm involved TMEP signal detection, segmentation, feature extraction and selection, and classification. The features of the segmented TMEP signals were extracted using the wavelet packet transform (WPT). A multi-layer neural network was then designed and tested based on statistical properties of the WPT coefficients. The average classification performance for discriminating interference and controlled movement related TMEP signal achieved 97.05%. The classification of TMEP signals based on the WPT is robust and the interferences to the controlling commands of TMEP signals in assistive human machine interface can be significantly reduced using the multi-layer neural network when considered in this challenging environment.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133199662","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723844
S. M. Kamruzzaman, M. S. Alam
In this paper, we propose a dynamic TDMA slot reservation (DTSR) protocol for cognitive radio ad hoc networks. Quality of Service (QoS) guarantee plays a critically important role in such networks. We consider the problem of providing QoS guarantee to users as well as to maintain the most efficient use of scarce bandwidth resources. A dynamic frame length expansion and shrinking scheme that controls the excessive increase of unassigned slots has been proposed. This method efficiently utilizes the channel bandwidth by assigning unused slots to new neighboring nodes and increasing the frame length when the number of slots in the frame is insufficient to support the neighboring nodes. It also shrinks the frame length in an effective way. Our proposed scheme, which provides both QoS guarantee and efficient resource utilization, be employed to optimize the channel spatial reuse and maximize the system throughput. Extensive simulation results show that the proposed mechanism achieves significant performance improvement in multichannel cognitive radio ad hoc networks.
{"title":"Dynamic TDMA slot reservation protocol for cognitive radio ad hoc networks","authors":"S. M. Kamruzzaman, M. S. Alam","doi":"10.1109/ICCITECHN.2010.5723844","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723844","url":null,"abstract":"In this paper, we propose a dynamic TDMA slot reservation (DTSR) protocol for cognitive radio ad hoc networks. Quality of Service (QoS) guarantee plays a critically important role in such networks. We consider the problem of providing QoS guarantee to users as well as to maintain the most efficient use of scarce bandwidth resources. A dynamic frame length expansion and shrinking scheme that controls the excessive increase of unassigned slots has been proposed. This method efficiently utilizes the channel bandwidth by assigning unused slots to new neighboring nodes and increasing the frame length when the number of slots in the frame is insufficient to support the neighboring nodes. It also shrinks the frame length in an effective way. Our proposed scheme, which provides both QoS guarantee and efficient resource utilization, be employed to optimize the channel spatial reuse and maximize the system throughput. Extensive simulation results show that the proposed mechanism achieves significant performance improvement in multichannel cognitive radio ad hoc networks.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133528843","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723895
S. U. Ahmed, M. Shahjahan, K. Murase
In this paper, a faster supervised algorithm (BPfast) for the neural network training is proposed that maximizes the derivative of sigmoid activation function during back-propagation (BP) training. BP adjusts the weights of neural network with minimizing an error function. Due to the presence of derivative information in the weight update rule, BP goes to ‘premature saturation’ that slows down the training convergence. In the saturation region, the derivative information tends to zero. To overcome the problem, BPfast maximizes the derivative of activation function together with minimizing the error function. BPfast is tested on five real world benchmark problems such as breast cancer, diabetes, heart disease, Australian credit card, and horse. BPfast exhibits faster convergence and good generalization ability over standard BP algorithm.
{"title":"Maximization of the gradient function for efficient neural network training","authors":"S. U. Ahmed, M. Shahjahan, K. Murase","doi":"10.1109/ICCITECHN.2010.5723895","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723895","url":null,"abstract":"In this paper, a faster supervised algorithm (BPfast) for the neural network training is proposed that maximizes the derivative of sigmoid activation function during back-propagation (BP) training. BP adjusts the weights of neural network with minimizing an error function. Due to the presence of derivative information in the weight update rule, BP goes to ‘premature saturation’ that slows down the training convergence. In the saturation region, the derivative information tends to zero. To overcome the problem, BPfast maximizes the derivative of activation function together with minimizing the error function. BPfast is tested on five real world benchmark problems such as breast cancer, diabetes, heart disease, Australian credit card, and horse. BPfast exhibits faster convergence and good generalization ability over standard BP algorithm.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123429006","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723832
S. Banik, M. Anwer, A. Khan
Soft computing forecasting tools play an important role to forecast many complicated systems. In this paper, an effort has been made to use soft computing approaches to predict Dhaka daily temperatures for the period of 28 February 1945 to 27 August 2006. We have selected the fuzzy neuro model, the neuro genetic algorithm model as soft computing techniques. To compare results, a popular time series statistical technique, namely autoregressive moving average model is selected and based on error analysis, a suitable model to predict temperature for Dhaka city is proposed. The performance comparisons of different models due to root mean square error, correlation coefficient and coefficient of determination between observed and predicted temperatures indicate that the neuro genetic algorithm model predicts temperatures with maximum accuracy, followed by the fuzzy neuro model. Our believe findings of this paper will be useful for those who are interested about Bangladeshi important atmospheric parameter, namely temperature.
{"title":"Soft computing models to predict daily temperature of Dhaka","authors":"S. Banik, M. Anwer, A. Khan","doi":"10.1109/ICCITECHN.2010.5723832","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723832","url":null,"abstract":"Soft computing forecasting tools play an important role to forecast many complicated systems. In this paper, an effort has been made to use soft computing approaches to predict Dhaka daily temperatures for the period of 28 February 1945 to 27 August 2006. We have selected the fuzzy neuro model, the neuro genetic algorithm model as soft computing techniques. To compare results, a popular time series statistical technique, namely autoregressive moving average model is selected and based on error analysis, a suitable model to predict temperature for Dhaka city is proposed. The performance comparisons of different models due to root mean square error, correlation coefficient and coefficient of determination between observed and predicted temperatures indicate that the neuro genetic algorithm model predicts temperatures with maximum accuracy, followed by the fuzzy neuro model. Our believe findings of this paper will be useful for those who are interested about Bangladeshi important atmospheric parameter, namely temperature.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123538890","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723901
Javed Hossain, M. Ashraful Amin
This paper presents a simple and computationally efficient method for plant species recognition using leaf image. This method works only for the plants with broad flat leaves which are more or less two dimensional in nature. The method consists of five major parts. First, images of leaf are acquired with digital camera or scanners. Then the user selects the base point of the leaf and a few reference points on the leaf blades. Based on these points the leaf shape is extracted from the background and a binary image is produced. After that the leaf is aligned horizontally with its base point on the left of the image. Then several morphological features, such as eccentricity, area, perimeter, major axis, minor axis, equivalent diameter, convex area and extent, are extracted. A unique set of features are extracted from the leaves by slicing across the major axis and parallel to the minor axis. Then the feature pointes are normalized by taking the ratio of the slice lengths and leaf lengths (major axis). These features are used as inputs to the probabilistic neural network. The network was trained with 1200 simple leaves from 30 different plant species. The proposed method has been tested using ten-fold cross-validation technique and the system shows 91.41% average recognition accuracy.
{"title":"Leaf shape identification based plant biometrics","authors":"Javed Hossain, M. Ashraful Amin","doi":"10.1109/ICCITECHN.2010.5723901","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723901","url":null,"abstract":"This paper presents a simple and computationally efficient method for plant species recognition using leaf image. This method works only for the plants with broad flat leaves which are more or less two dimensional in nature. The method consists of five major parts. First, images of leaf are acquired with digital camera or scanners. Then the user selects the base point of the leaf and a few reference points on the leaf blades. Based on these points the leaf shape is extracted from the background and a binary image is produced. After that the leaf is aligned horizontally with its base point on the left of the image. Then several morphological features, such as eccentricity, area, perimeter, major axis, minor axis, equivalent diameter, convex area and extent, are extracted. A unique set of features are extracted from the leaves by slicing across the major axis and parallel to the minor axis. Then the feature pointes are normalized by taking the ratio of the slice lengths and leaf lengths (major axis). These features are used as inputs to the probabilistic neural network. The network was trained with 1200 simple leaves from 30 different plant species. The proposed method has been tested using ten-fold cross-validation technique and the system shows 91.41% average recognition accuracy.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284671","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723878
M. A. Wahid, M. Kaykobad, M. Hasan
Given a set of n points in the plane, the problem of computing the circular ordering of the points about a viewpoint v and efficiently maintaining this ordering information as v moves is well defined in computer graphics and animation. Each of the unique circular ordering in respect to v is called as view. In this paper, our task is to generalize this idea for 3D point set and to propose a kinetic data structure named Kinetic Neighborhood Graph to maintain the view dynamically with efficiency O(mλs(n2)), locality O(1) and responsiveness O(m).
{"title":"Kinetisation of view of 3D point set","authors":"M. A. Wahid, M. Kaykobad, M. Hasan","doi":"10.1109/ICCITECHN.2010.5723878","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723878","url":null,"abstract":"Given a set of n points in the plane, the problem of computing the circular ordering of the points about a viewpoint v and efficiently maintaining this ordering information as v moves is well defined in computer graphics and animation. Each of the unique circular ordering in respect to v is called as view. In this paper, our task is to generalize this idea for 3D point set and to propose a kinetic data structure named Kinetic Neighborhood Graph to maintain the view dynamically with efficiency O(mλs(n2)), locality O(1) and responsiveness O(m).","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130018838","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723877
M. Shoyaib, M. Abdullah-Al-Wadud, Jo Moo Youl, Muhammad Mahbub Alam, O. Chae
We introduce a facial expression recognition method, which incorporates a weight to the Local Binary Pattern (LBP), and generates solid expression features. Furthermore, we use Adaboost to select a small set of prominent features, which is used by the Support Vector Machine (SVM) to classify facial expressions efficiently. Experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both accuracy and complexities.
{"title":"Facial expression recognition based on a weighted Local Binary Pattern","authors":"M. Shoyaib, M. Abdullah-Al-Wadud, Jo Moo Youl, Muhammad Mahbub Alam, O. Chae","doi":"10.1109/ICCITECHN.2010.5723877","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723877","url":null,"abstract":"We introduce a facial expression recognition method, which incorporates a weight to the Local Binary Pattern (LBP), and generates solid expression features. Furthermore, we use Adaboost to select a small set of prominent features, which is used by the Support Vector Machine (SVM) to classify facial expressions efficiently. Experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both accuracy and complexities.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125179035","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723839
M. M. Rahaman, Anindya Das, M. Z. Nayen, Md. Saidur Rahman
This paper describes several feature extraction techniques, which will facilitate Automatic Speech Recognition (ASR) for Bangla speech. These techniques are applied on different sound-packets, which are essentially segments of Bangla speech. The key temporal regions in a sound-packet that contain vital information about the speech signal are identified. Some novel feature extraction methods are developed using the information contained within these key regions. It has been observed that a single feature cannot provide enough information to achieve successful automatic speech recognition; rather a combination of the features can be used effectively to increase the accuracy.
{"title":"Special feature extraction techniques for Bangla speech","authors":"M. M. Rahaman, Anindya Das, M. Z. Nayen, Md. Saidur Rahman","doi":"10.1109/ICCITECHN.2010.5723839","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723839","url":null,"abstract":"This paper describes several feature extraction techniques, which will facilitate Automatic Speech Recognition (ASR) for Bangla speech. These techniques are applied on different sound-packets, which are essentially segments of Bangla speech. The key temporal regions in a sound-packet that contain vital information about the speech signal are identified. Some novel feature extraction methods are developed using the information contained within these key regions. It has been observed that a single feature cannot provide enough information to achieve successful automatic speech recognition; rather a combination of the features can be used effectively to increase the accuracy.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128477986","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 : 2010-12-01DOI: 10.1109/ICCITECHN.2010.5723919
T. Chakraborty, A. Mukhopadhyay, I. Saha Misra, S. Sanyal
The performance of wireless LANs is greatly affected by path loss, RF interference and other sources of signal attenuation in addition to network congestion. The primary factors involved in effective real-time communication, namely delay and loss, must be within certain controlled limits in such a scenario. In this paper, we analyze the various factors driving IEEE 802.11b access points through extensive simulations and thereafter develop an optimization technique to configure the parameters of the Access Point. We simulate our test bed scenario and apply the developed algorithm. Finally, we implement the configured parameters in our testbed to provide optimum Voice over IP (VoIP) performance. Simulation and measured results have been included.
{"title":"Optimization technique for configuring IEEE 802.11b access point parameters to improve VoIP performance","authors":"T. Chakraborty, A. Mukhopadhyay, I. Saha Misra, S. Sanyal","doi":"10.1109/ICCITECHN.2010.5723919","DOIUrl":"https://doi.org/10.1109/ICCITECHN.2010.5723919","url":null,"abstract":"The performance of wireless LANs is greatly affected by path loss, RF interference and other sources of signal attenuation in addition to network congestion. The primary factors involved in effective real-time communication, namely delay and loss, must be within certain controlled limits in such a scenario. In this paper, we analyze the various factors driving IEEE 802.11b access points through extensive simulations and thereafter develop an optimization technique to configure the parameters of the Access Point. We simulate our test bed scenario and apply the developed algorithm. Finally, we implement the configured parameters in our testbed to provide optimum Voice over IP (VoIP) performance. Simulation and measured results have been included.","PeriodicalId":149135,"journal":{"name":"2010 13th International Conference on Computer and Information Technology (ICCIT)","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114301409","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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