2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)最新文献

{"title":"A comparative study on feature dependency of the Manipuri language based phonetic engine","authors":"S. K. Dutta, Salam Nandakishor, L. J. Singh","doi":"10.1109/CSCITA.2017.8066533","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066533","url":null,"abstract":"This paper presents a study on how the performance of Phonetic engine(PE) varies with different set of spectral features selected for it. An exclusive study is carried out with a PE developed in the Manipuri language. Here, we built the PE using phonetic transcriptions and modeling of each phonetic unit by Hidden Markov Model (HMM). The symbols of International Phonetic Alphabet (IPA) (revised in 2005) are used in the transcription of the collected data. A 5-state left to right HMM with 32 mixtures in each state is being used to build a model that represents each phonetic unit. Speech feature extraction is a very important stage in the development of such a PE since it is responsible for the overall accuracy of the system. Therefore, selection of a proper feature extraction technique is very crucial in building the PE. In speech and speaker recognition literature, many techniques available for feature extraction, for example, the Linear Predictive Cepstral Coefficients (LPCC), the Mel-frequency Cepstral Coefficients (MFCC), the Perceptual Linear Prediction (PLP) and the Linear Predictive Coding (LPC) etc., to name a few. In our paper, we attempt to analyze the performance of the Manipuri PE for the three widely used spectral features: MFCC, PLP and LPCC for three different modes of collected data: Read, Lecture and Conversation. Here, we are using 13, 26 and 39 coefficient dimensions for each of the above features. After analyzing the accuracy of our system, we found that the PLP and the MFCC are superior to the LPCC under all conditions.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115689446","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}

{"title":"Interactive selection of time-tables generated using evolutionary multi-objective optimization","authors":"A. Bhatt, Lakshmi Kurup","doi":"10.1109/CSCITA.2017.8066525","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066525","url":null,"abstract":"Automatic generation of a time table with multi-level constraints is a challenging exercise. Typically, a timetable problem has many possible solutions in the initial search space, each with a distinct fitness level. In this paper we propose a multi-stage hybrid solution based on an evolutionary algorithm where the initial population is first generated that satisfies all the hard constraints. A user configurable fitness function is used to test the soft constraints. Solutions with fitness value above a certain threshold are then mutated to improve the fitness level of the next population and an acceptable optimal solution can then be selected interactively by the user. This unique interactive approach with configurable fitness functions allows more control to the user over the selection of an optimal solution and helps in multi-objective decision making.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114478745","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}

{"title":"On the design of circularly polarized U-slot cut square microstrip antenna","authors":"A. Deshmukh, P. Kadam, P. Zaveri","doi":"10.1109/CSCITA.2017.8066568","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066568","url":null,"abstract":"Circularly polarized microstrip antenna realized by cutting unequal arm length U-slot is discussed. In 2.3 GHz frequency band, it gives VSWR and axial ratio bandwidth of 9 and 4%, respectively. In the reported work, a detailed explanation about the working of circular polarized antenna in terms patch resonant modes and the equations/design procedure to realize similar antennas at different frequency is not explained. In this paper, an in-depth analysis which explains the effects of unequal lengths U-slot to give circularly polarized response is presented. First the equal lengths of U-slot reduce the resonance frequencies of patch TM01 and TM20 resonant modes. An unequal length in U-slot arms (asymmetrical U-slot) leads to excitation of new resonant mode whose current distribution is similar to TM01 mode of U-slot cut patch. This gives circularly polarized response. Further equations for various patch parameters for U-slot cut antenna are proposed. Using them U-slot cut patch was designed at 1500 MHz. At this frequency antenna yields VSWR and axial ratio bandwidths of 138 (9.6%) and 60 (4.3%) MHz, respectively. Thus proposed equations are useful for designing U-slot cut antenna at any given frequency on thicker air substrate.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132083690","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}

{"title":"Using algebraic signatures to compress built-in self test on a chip","authors":"Jaya Jeswani, J. Rose, Thomas Schwarz","doi":"10.1109/CSCITA.2017.8066531","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066531","url":null,"abstract":"Chip functionality testing can greatly benefit from a Built In Self-Test (BIST). The Self-Test Using MISR and Parallel Shift Register Sequence Generator (STUMPS) architecture uses a compression technique to generate a set of test patterns, to submit them to the circuit undergoing testing, and to compare the output with that of a “gold” (known to be good circuit) by loading and comparing the contents of a Multiple Input Shift Register (MISR). We propose to use algebraic signatures as the comparison signature implemented by the MISR. As we will see, the MISR is still basically a Linear Feedback Shift Register (LFSR), but can now be made to guarantee to discover one or up to k output discrepancies, where k is a very small number that determines the length of the MISR register. The construction of the algebraic signature register is generic and only the comparison value needs to be programmed.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114178329","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}

{"title":"Double threshold based spectrum sensing technique using sample covariance matrix for cognitive radio networks","authors":"Chhagan Charan, R. Pandey","doi":"10.1109/CSCITA.2017.8066542","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066542","url":null,"abstract":"The spectrum sensing is a vital stage in cognitive radio networks. The performance of energy-based spectrum sensing scheme degrades under noise uncertainty, which can be improved by covariance-based sensing methods. In this paper, a double threshold-based spectrum sensing scheme using sample covariance matrix of received signal is presented, that improves the performance of conventional covariance-based spectrum sensing methods. The two thresholds are derived using random matrix theory. The performance of proposed scheme is verified through simulations.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121170032","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}

{"title":"E-negotiator based on buyer's surfing pattern","authors":"Snehal P. Kulkami, Bidisha Roy, Sridari Iyer","doi":"10.1109/CSCITA.2017.8066523","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066523","url":null,"abstract":"Everyone likes to get the best price, but best price cannot be provided to everyone. This paper proposes an algorithm to provide the best price to the most prospective buyer. E-Negotiation will be based on buyer's activity pattern and surfing behavior. Unlike existing e-negotiation models proposed for B2C, B2B and C2C Ecommerce applications, where the intervention of buyer is present, this paper proposes a system to e-negotiate by just observing buyer's surfing pattern, without depending on his input. Surfing patterns such as sites visited and products surfed will tell the buyer's intention on purchasing the product. The amount of discount for negotiation is generated using a Fuzzy Inference System. Providing such a custom discounted price will motivate the buyers to make the purchase.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"23 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120985137","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}

{"title":"A review on matrix factorization techniques in recommender systems","authors":"Rachana Mehta, Keyur Rana","doi":"10.1109/CSCITA.2017.8066567","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066567","url":null,"abstract":"Growth of the Internet and web applications has led to vast amount of information over web. Information filtering systems such as Recommenders have become potential tools to deal with such plethora of information, help users select and provide relevant information. Collaborative Filtering is the popular approach to recommendation systems. Collaborative Filtering works on the fact that users with similar behavior will have similar interests in future, and using this notion collaborative filtering recommends items to user. However, the sparseness in data and high dimensionality has become a challenge. To resolve such issues, model based, matrix factorization techniques have well emerged. These techniques have evolved from using simple user-item rating information to auxiliary information such as time and trust. In this paper, we present a comprehensive review on such matrix factorization techniques and their usage in recommenders.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114117302","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}

{"title":"Workload prediction model based on supervised learning for energy efficiency in cloud","authors":"Niharika Verma, Anju Sharma","doi":"10.1109/CSCITA.2017.8066526","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066526","url":null,"abstract":"With the rapid emergence of cloud and its magnificent features such as high availability, reliability and security, a large number of clients are moving to the cloud platform. This migration of clients has led to increased burden on the resources such as CPU, network and memory. Hence, the problem of high power consumption, increased carbon footprints and need for higher cooling effects arose. However, resource scheduling and provisioning has become a milestone to handle such diverse issues related to cloud and get them under centralized control system. Workload prediction is an utmost requirement to dynamically predict the incoming workload and schedule the resources according to client needs. Dynamic workload prediction based on historical data can prove useful for pattern matching of current scenario with the past ones and allocate the resources in the most efficient way. This paper aims to propose a prototype model for workload prediction using machine learning models to handle the dynamic nature of the cloud infrastructure. This prediction is then used for resource provisioning. Diverse scheduling scenarios over the cloud are also covered in this paper and alternate remedies have been suggested for low power consumption and temperature control.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115347442","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}

{"title":"Patient knowledge and data privacy in healthcare records system","authors":"Kissi Mireku Kingsford, Zhang Fengli, Gbongli Komlan","doi":"10.1109/CSCITA.2017.8066543","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066543","url":null,"abstract":"The advancement in usage of information and communication technology has changed the operational activities of most organization in recent times. The grown in usage has contributed to the growth in the data that organizations have access to. In the past decade, the growth of these data has further given rise to research and development into computer storage devices and infrastructure. In addition the big data regime has gained the attention of the academic community as evident in scholarly journals shows. The digitalization of health records and medication notes has made individual health data more readily accessible to a wide range of users including patients, doctors, researchers, statisticians, and data scientists. However, increased accessibility of highly sensitive heath records threatens the privacy and confidentiality of patients. The main concern of database privacy in healthcare records system is mostly without the knowledge and contribution of the records owners being the patients. Most research tackles the problems of big data privacy preservation using different frame works and other solutions without involving the record owners. This paper seek the knowledge of some patients with respect to their data recorded in the healthcare system and how their privacy is secured. The paper further researched on identifying the relationship between client knowledge on data privacy and big data protection in healthcare records system. We used field survey to gather and analyze data by adopting a quantitative method. Therefor the respondent in this study were patient and record officers from multiple hospital that includes both private and public institutions. Both Pearson correlation and linear regression analysis is conducted to test the hypothesis stipulated for the purpose of this study. The study makes thrives in areas of combining computer technology and social forces to mitigate the risk propensity associated with healthcare records in healthcare institutions. It provides need direction for data privacy research but shifting the paradigm to integrate social forces and factors as an enabler of healthcare record protection. Sometime that has not been covered extensively in current studies that examine big data, data privacy and healthcare records.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120999013","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}

{"title":"Improving performance of CSS cognitive radio networks under jamming attack","authors":"Rajesh D. Kadu, P. Karde","doi":"10.1109/CSCITA.2017.8066556","DOIUrl":"https://doi.org/10.1109/CSCITA.2017.8066556","url":null,"abstract":"Cognitive radio (CR) sense the spectrum to discover the available white spaces in it for making better utilization of spectrum and alleviates the spectrum shortage problem. While using the available spectrum, it avoids the harmful interference with licensed users or primary users (PUs). Individual spectrum sensing by CR may not be accurate due to fading and shadowing effects. Cooperative spectrum sensing (CSS) ensures more accuracy in sensing the spectrum as it exploits the cooperation among multiple secondary users (SUs) or CRs. However, CSS is more vulnerable to security threats and attacks which degrades the network performance. In this paper, we consider the jamming attack in CSS by CRs and identify the attacker or malicious user (MU) to improve the network performance. In jamming attack, attacker jams the communication channels by continuously sending the bogus signals there by preventing the other SUs from using the channels leading to denial of service attack. The results shows that the network performance does not degrade under jamming attack as the proposed protocol detects and eliminates the attacker.","PeriodicalId":299147,"journal":{"name":"2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125592076","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}