{"title":"Thermal aware FPRM based AND-XOR network synthesis of logic circuits","authors":"Apangshu Das, S. Pradhan","doi":"10.1109/ReTIS.2015.7232930","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232930","url":null,"abstract":"With the advent of incorporating increased number of complex logic blocks within a VLSI chip, power-density is increasing. Power-density directly converges into temperature which reduces the yield of the circuit. Adverse affect of power-density reduction is increase in area. So, there is a trade-off between area and power-density. Previous works has been done on the Fixed Polarity Reed-Muller (FPRM) AND-XOR realization for its reduced area or low power realization. In this paper, we present a Genetic Algorithm(GA) based non-exhaustive heuristic to increase the sharing of product terms by selecting the proper polarity of input variables in FPRM expansion and a suitable area and power-density trade-off has been enumerated. Incorporation of the power-density as a fitness constraint in FPRM based optimization for temperature analysis is the first ever effort. The proposed algorithm has been validated with the LGSynth93 benchmark circuit.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708507","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 characteristics and resource aware Hadoop scheduler","authors":"M. Divya, B. Annappa","doi":"10.1109/ReTIS.2015.7232871","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232871","url":null,"abstract":"Hadoop MapReduce is one of the largely used platforms for large scale data processing. Hadoop cluster has machines with different resources, including memory size, CPU capability and disk space. This introduces challenging research issue of improving Hadoop's performance through proper resource provisioning. The work presented in this paper focuses on optimizing job scheduling in Hadoop. Workload Characteristic and Resource Aware (WCRA) Hadoop scheduler is proposed, that classifies the jobs into CPU bound and Disk I/O bound. Based on the performance, nodes in the cluster are classified as CPU busy and Disk I/O busy. The amount of primary memory available in the node is ensured to be more than 25% before scheduling the job. Performance parameters of Map tasks such as the time required for parsing the data, map, sort and merge the result, and of Reduce task, such as the time to merge, parse and reduce is considered to categorize the job as CPU bound or Disk I/O bound. Tasks are assigned the priority based on their minimum Estimated Completion Time. The jobs are scheduled on a compute node in such a way that jobs already running on it will not be affected. Experimental results has given 30 % improvement in performance compared to Hadoop's FIFO, Fair and Capacity scheduler.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127830975","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 transparent tree root identification scheme to support route-optimization and network mobility in PMIPv6 Domain","authors":"Subhajyoti Barman, Ankita Ghosh, S. Biswas","doi":"10.1109/ReTIS.2015.7232936","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232936","url":null,"abstract":"To ensure efficient session continuity for every node in a Mobile Network NEMO Basic Support (NBS) protocol, an extension of MIPv6 has been proposed. The protocol NBS is not directly applicable in PMIPv6 domain as it is a network based mobility management protocol and mobility is transparent from mobile node. It is already proved that to support NEMO BSP in PMIPv6 domain some modifications are required in one of these two protocols or in both. This work is aimed at proposing a new definition of network mobility support in PMIPv6 domain such that they can form a hierarchy of mobile networks. The optimization of the communication path between a Mobile Network Node (MNN) and Corresponding Node (CN) is another objective of this work.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785704","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":"DMBRLE: A lossless compression algorithm for solar irradiance data acquisition","authors":"Soumya Roy, S. C. Panja, S. N. Patra","doi":"10.1109/ReTIS.2015.7232921","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232921","url":null,"abstract":"In this paper a new compression scheme has been formulated based on jointly delta modulation with biasing and Run Length Encoding technique for Direct Normalized Solar Irradiance data. The key features of this Compression Algorithm (Delta Modulation with Biasing and Run Length Encoding techniques viz. DMBRLE) are: it requires less memory as it belongs to direct compression method and its lossless nature. The main Goal of this algorithm has been achieved by compressing successive same sample elements as well as low frequency sample changes region. This is substantiated on jointly First Sample Difference (FSD) and S-Run Length Encoding (S-RLE) techniques. FSD is modified to concentrate at low frequency spectra. Simultaneously S-Run length Encoding is structured to fix the region having successive same sample value. The Compression Algorithm is modified stepwise and its effect on that is a large non-linear in nature. However, the quality of the recovered signal is demonstrated by computing Normalized Root Mean Square Error (NRMES). The result establishes that this developed compression scheme increases the Compression Ratio up to a reasonable value without threw off the Lossless nature.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122173879","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":"Detection of hemorrhages in diabetic retinopathy analysis using color fundus images","authors":"Priya Bharali, J. Medhi, S. Nirmala","doi":"10.1109/ReTIS.2015.7232884","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232884","url":null,"abstract":"Diabetic retinopathy (DR), a complication due to diabetes, is the leading cause of blindness. Diagnosis of DR at its earliest is thus very necessary. In this paper, we present an automated computer aided method for detection of hemorrhages using fundus images. In the first stage, blood vessel (BV) is detected and eliminated. In the following stage hemorrhage candidate is detected. The method is applied on HRF, DIARETDB0, DIARETDB1, MESSIDOR and Local databases which have both normal and pathological images. The proposed method shows an overall sensitivity of 97.3% and specificity of 98.92% in hemorrhage detection.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132119721","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 memory based approach to word sense disambiguation in Bengali using k-NN method","authors":"Rajat Pandit, S. Naskar","doi":"10.1109/ReTIS.2015.7232909","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232909","url":null,"abstract":"Word Sense Disambiguation (WSD) is an important and challenging task in the area of Natural Language Processing (NLP) where the task is to find the correct sense of an ambiguous word given its context. There have been very few attempts on WSD in Bengali or in Indian languages. The k-Nearest-Neighbor (k-NN) algorithm is a very well known and popular method for text classification. The k-NN algorithm determines the classification of a new sample from its k nearest neighbors. In this paper, we present how k-NN algorithm can be effectively applied to the task of WSD in Bengali. The k-NN algorithm achieved an accuracy of over 71% in a WSD task in Bengali reported in this paper.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130167641","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":"Signal processing technique based fault location of a distribution line","authors":"P. Ray, D. Mishra","doi":"10.1109/ReTIS.2015.7232919","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232919","url":null,"abstract":"Hybrid signal processing technique is discussed in this paper to sense the fault in a 11 kV, 30 km distribution line with R-L load placed at the receiving end. The proposed method uses 1 cycle post fault voltage and current signal wave form sending end of the system under study. Further preprocessing of the collected signal is done by wavelet packet transform and discrete wavelet transform which includes decomposition of the signal and feature extraction. Thereafter from the total feature set, redundant features are removed and best features are selected by genetic algorithm based feature selection method to get better accuracy and reduce computational difficulty. To simulate the model accurately, sampling frequency taken is 30 kHz. Train and test data set are generated by considering various operating conditions which are made entirely different in order to make the suggested method insensitive to parameter variations. Artificial neural network /Support vector machine is used for prediction purpose. Then optimal features are fed to artificial neural network/ support vector machine to detect the fault location. It was observed from the simulation outcomes that the suggested method is quite accurate and fast as compared to schemes investigated by other researchers.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116619911","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":"Condition monitoring and diagnostics for automotive applications","authors":"A. Ukil","doi":"10.1109/ReTIS.2015.7232896","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232896","url":null,"abstract":"Classification of signals acquired by condition monitoring systems for automotive application is becoming increasingly important. The work presented in this paper is motivated by a real-life classification challenge organized by Ford. Data samples from an automotive subsystem were collected. A classifier is designed to robustly isolate the different types of problems, by analyzing the acquired signals. In this paper, the wavelet transform is used as data reduction and feature selection tool. The proper input feature is then classified by neural network as a nonlinear classifier tool. Results show significant accuracy with reduced amount of false positives.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392813","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":"Human ringworm detection using wavelet energy signature","authors":"M. Saha, M. K. Naskar, B. N. Chatterji","doi":"10.1109/ReTIS.2015.7232874","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232874","url":null,"abstract":"We propose an application based experimental work to identify the ringworm images from a set of human skin images. Our approach deals with the 3-level decomposition of the skin images by the Daubechies (DB), Coiflet (CF), Biorthogonal (BO) and Discrete Meyer (DM) wavelets and extraction of the corresponding energy signatures. The discriminatory energy signatures from the different wavelet decomposed approximation and detail subbands at each level of resolution are used to tabulate the training and testing databases. The binary classifier, Support Vector Machine (SVM) is then deployed to detect the ringworm images successfully.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116972514","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 novel palmprint authetication system by XWT based feature extraction and BFOA based feature selection and optimization","authors":"Nandita Sanyal, A. Chatterjee, S. Munshi","doi":"10.1109/ReTIS.2015.7232922","DOIUrl":"https://doi.org/10.1109/ReTIS.2015.7232922","url":null,"abstract":"This paper is about a new palm-print based biometric authentication system. It is low-cost as it employs a scanner coupled to a PC, for acquiring the image of the human palm. The histogram of the palm-image is cross wavelet transformed(XWT) with respect to that of a reference palm-image. Several features are extracted from the resulting spectrum, and a trained ANN is used to identify the subject, based on these features. Bacterial Foraging Optimization Algorithm(BFOA) is used to select the combination of features that results in the best performance. An Adaptive Bacterial Foraging Optimization Algorithm (ABFOA) is also introduced to further augment the performance of the system. Extensive experimental investigations reveal that an authentication accuracy of more than 97% can be obtained with the system.","PeriodicalId":161306,"journal":{"name":"2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130777348","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}