Pub Date : 2009-03-14DOI: 10.1109/MSPCT.2009.5164175
D. Hussain, S. Ather
How often have you been able to implement an algorithm as it is described in a paper? And when you did, were you confident that you had exactly the same parameter values and results as the authors of the paper? All too often, articles do not describe all the details of an algorithm and thus prohibit an implementation by someone else. In this paper, we describe reproducible research, a paradigm to allow other people to reproduce with minimal effort the results that have been obtained. We discuss both the reproducibility of data and algorithms, and give examples for each of them. The effort required to make research reproducible is compensated by a higher visibility and impact of the results.
{"title":"Notice of Violation of IEEE Publication PrinciplesReproducible research in various facets of signal processing","authors":"D. Hussain, S. Ather","doi":"10.1109/MSPCT.2009.5164175","DOIUrl":"https://doi.org/10.1109/MSPCT.2009.5164175","url":null,"abstract":"How often have you been able to implement an algorithm as it is described in a paper? And when you did, were you confident that you had exactly the same parameter values and results as the authors of the paper? All too often, articles do not describe all the details of an algorithm and thus prohibit an implementation by someone else. In this paper, we describe reproducible research, a paradigm to allow other people to reproduce with minimal effort the results that have been obtained. We discuss both the reproducibility of data and algorithms, and give examples for each of them. The effort required to make research reproducible is compensated by a higher visibility and impact of the results.","PeriodicalId":179541,"journal":{"name":"2009 International Multimedia, Signal Processing and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839690","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 : 2009-03-14DOI: 10.1109/MSPCT.2009.5164166
B. Anami, V. Pagi
Vehicles of a given type, in different working conditions, generate dissimilar sound patterns. Each sound pattern is viewed as acoustic signature. Sounds of moving vehicles provide clues of their traits such as makes, possible faults, performances of sub systems and the like. Different work conditions mean vehicles running at different speeds, under different road conditions, different accelerations and the like. In such situations tracking of faults manually becomes difficult and automatic acoustic surveillance enables easy monitoring of certain conditions of the vehicles and future consequences. These could be accidents, over speeding of the vehicles, compliance with traffic rules and regulations etc. In this paper, we have proposed an acoustic signature based neural network model for recognizing different types of two-wheelers. We have used simple time-domain features such as Average Zero Crossing rate(ZCR), Root Mean Square(RMS), and Short Time Energy(STE), and frequency-domain features such as Mean and Standard Deviation of Spectrum Centroid (CMEAN and CSD). Two-wheelers of three major Indian makes, namely Hero Honda, Bajaj and TVS, are considered in the work. The vehicles are classified into Bikes and Scooters. It is observed from the results that classification accuracy depends on different factors such as their usage, maintenance, environmental and road conditions. We have considered age of the vehicle as a factor in choosing the samples. The recognition results show 73.33% accuracy.
{"title":"An acoustic signature based neural network model for type recognition of two-wheelers","authors":"B. Anami, V. Pagi","doi":"10.1109/MSPCT.2009.5164166","DOIUrl":"https://doi.org/10.1109/MSPCT.2009.5164166","url":null,"abstract":"Vehicles of a given type, in different working conditions, generate dissimilar sound patterns. Each sound pattern is viewed as acoustic signature. Sounds of moving vehicles provide clues of their traits such as makes, possible faults, performances of sub systems and the like. Different work conditions mean vehicles running at different speeds, under different road conditions, different accelerations and the like. In such situations tracking of faults manually becomes difficult and automatic acoustic surveillance enables easy monitoring of certain conditions of the vehicles and future consequences. These could be accidents, over speeding of the vehicles, compliance with traffic rules and regulations etc. In this paper, we have proposed an acoustic signature based neural network model for recognizing different types of two-wheelers. We have used simple time-domain features such as Average Zero Crossing rate(ZCR), Root Mean Square(RMS), and Short Time Energy(STE), and frequency-domain features such as Mean and Standard Deviation of Spectrum Centroid (CMEAN and CSD). Two-wheelers of three major Indian makes, namely Hero Honda, Bajaj and TVS, are considered in the work. The vehicles are classified into Bikes and Scooters. It is observed from the results that classification accuracy depends on different factors such as their usage, maintenance, environmental and road conditions. We have considered age of the vehicle as a factor in choosing the samples. The recognition results show 73.33% accuracy.","PeriodicalId":179541,"journal":{"name":"2009 International Multimedia, Signal Processing and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129031412","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}
Existing Set-Top Boxes experiences very large jitter in terrestrial environment due to multi path reflections. Existing algorithms for achieving clock recovery for DVB-T relies on very complex calculations which are not suitable for real time data processing. In digital Set-top box most of the CPU bandwidth is consumed in audio & video data decoding so clock synchronization algorithms get very less CPU time to execute. This paper presents Low cost weighing filter approach to solve the color loss problem in Digital Set-top Box. This algorithm has been tested in practical satellite and terrestrial environment. Its performance has been compared with Linear Regression based algorithm and shows distinct advantages.
{"title":"Performance evaluation of enhanced FIR filter based module for clock synchronization in MPEG2 transport stream","authors":"Monika Jain, A. Jain, P. Jain, Sharad Jain","doi":"10.1145/1523103.1523167","DOIUrl":"https://doi.org/10.1145/1523103.1523167","url":null,"abstract":"Existing Set-Top Boxes experiences very large jitter in terrestrial environment due to multi path reflections. Existing algorithms for achieving clock recovery for DVB-T relies on very complex calculations which are not suitable for real time data processing. In digital Set-top box most of the CPU bandwidth is consumed in audio & video data decoding so clock synchronization algorithms get very less CPU time to execute. This paper presents Low cost weighing filter approach to solve the color loss problem in Digital Set-top Box. This algorithm has been tested in practical satellite and terrestrial environment. Its performance has been compared with Linear Regression based algorithm and shows distinct advantages.","PeriodicalId":179541,"journal":{"name":"2009 International Multimedia, Signal Processing and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831151","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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