2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献_第5页

A 1.8 V 11.02 μW single-ended inverter-based OTA with 113.62 dB gain 基于1.8 V 11.02 μW单端逆变器的OTA，增益113.62 dB

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806229

Yajunath Kaliyath, T. Laxminidhi

引用次数: 2

Spectrum sensing in the presence of Cauchy noise through differential entropy 基于微分熵的柯西噪声频谱传感

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806266

Sanjeev Gurugopinath, R. Muralishankar, H. N. Shankar

引用次数: 7

Experimental analysis of RSSI-based distance estimation for wireless sensor networks 基于rssi的无线传感器网络距离估计实验分析

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806221

R. Mahapatra, N. Shet

引用次数: 17

FPGA prototyping and parameterised based resource evaluation of Network on Chip architecture 片上网络架构的FPGA原型与参数化资源评估

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806267

A. Swain, Kunda Rajesh Babu, Sourav Narayan Satpathy, K. Mahapatra

引用次数: 2

Energy aware SLA and green cloud federations 能源意识SLA和绿色云联盟

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806217

Nimisha Joy, K. Chandrasekaran, A. Binu

{"title":"Energy aware SLA and green cloud federations","authors":"Nimisha Joy, K. Chandrasekaran, A. Binu","doi":"10.1109/DISCOVER.2016.7806217","DOIUrl":"https://doi.org/10.1109/DISCOVER.2016.7806217","url":null,"abstract":"Cloud computing has been an emerging research topic since 2007. The online services are delivered as pay-as-you-use in Cloud computing. The customers need not be in a long term contract with Cloud Service Providers (CSPs). Service level agreements (SLAs) are agreements signed between a cloud service provider and customers. Energy aware SLAs extend the existing SLA agreements in order to include energy and carbon aware parameters. The efficiency and availability of the system are not disturbed when certain jobs are relaxed in order to obtain high energy consumption. In order to increase the energy consumption further we incorporated the concept of cloud federations in which a group of CSPs, mutually agree to make use of their resources to execute the VMs of other CSPs. An algorithm based on the amount of energy consumed by different CSPs is being readily implemented. This algorithm grant a group of CSPs to jointly set the federations in a manner to distribute their work equally so as to reduce the energy consumed. The results from the above approach show that it can be considered as a hopeful solution to the problem of reducing the energy cost even though there are a few challenges during implementation.","PeriodicalId":383554,"journal":{"name":"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115836295","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}

引用次数: 2

Algorithms for minimizing the receiver interference in a wireless sensor network 无线传感器网络中接收机干扰最小化的算法

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806251

D. Shetty, M. Lakshmi

引用次数: 8

Analysis and verification of hard tie-off signals of SoC SoC硬接断信号的分析与验证

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806264

P. Ghosh

{"title":"Analysis and verification of hard tie-off signals of SoC","authors":"P. Ghosh","doi":"10.1109/DISCOVER.2016.7806264","DOIUrl":"https://doi.org/10.1109/DISCOVER.2016.7806264","url":null,"abstract":"Nowadays MP-SoCs or embedded SoCs have several hundred thousands of hard tie-off signals. Mostly, hard tie-offs signals are verified or reviewed by SoC verification team along with SoC architecture team manually. It is very cumbersome and error prone process. Often, it becomes infeasible due to large number of hard tie-offs signal in SoC. It is one of the most challenging tasks in SoC verification. In this paper, we are proposing a verification methodology which automatically verifies hard tie-off signal values based on previous taped out SoC database(s), IP delivery database(s) and SoC Architecture Specification(for tie-off signals). For new IPs, we are proposing mutation based methodology for qualifying each tied signal. At the end, we are also proposing to generate one UVM sequence per IP basis for the verified hard tie signals. The proposed methodology was applied on SoC1/SoC4 [9] on final RTL design, we have found many critical logic bugs. We have seen this methodology improved productivity of hard tied verification by 10X or more in our UVM based SoC verification environments for three different SoCs. It improves the quality of verification.","PeriodicalId":383554,"journal":{"name":"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133535442","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}

引用次数: 0

Signal processing computation based seismic energy estimation of blast induced ground vibration waves 基于信号处理计算的爆炸诱发地震动波地震能量估计

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806225

V. Sastry, G. R. Chandra

{"title":"Signal processing computation based seismic energy estimation of blast induced ground vibration waves","authors":"V. Sastry, G. R. Chandra","doi":"10.1109/DISCOVER.2016.7806225","DOIUrl":"https://doi.org/10.1109/DISCOVER.2016.7806225","url":null,"abstract":"Study of ground vibrations resulted from blasting operations in mines and quarries is significant ecological aspect. In general, very lesser amount of explosive energy will be utilized in blasting process for breakage and creation of fragmentation, however the remaining will be squandered in the form of shock waves. Shock waves resulted from blasting operations cannot be entirely abolished, nonetheless can be lessened to the extent possible using an appropriate blasting methodology. Substantial work has been performed to detect ground vibrations for assessing the blast performance using the intensity of ground vibrations. Nevertheless, not much research has carried in the estimation of seismic energy and utilizing this energy for assessing the performance of blast rounds. In this paper, a Signal Processing based technique for the estimation of seismic energy dissipated at various distances is proposed. In total, 116 blast vibration events from Limestone Mines, 96 blast vibration events from Underground Coal Mine and 43 blast vibration events from Sandstone Mines were collected and respective signal processing analysis was carried out using Advanced Blastware and DADiSP software. Each vibration event in one direction carries about 2500 particle motion samples.","PeriodicalId":383554,"journal":{"name":"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133656693","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}

引用次数: 3

Sound event detection in urban soundscape using two-level classification 基于二级分类的城市声景声事件检测

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806268

Bibek Luitel, Y. V. S. Murthy, S. Koolagudi

{"title":"Sound event detection in urban soundscape using two-level classification","authors":"Bibek Luitel, Y. V. S. Murthy, S. Koolagudi","doi":"10.1109/DISCOVER.2016.7806268","DOIUrl":"https://doi.org/10.1109/DISCOVER.2016.7806268","url":null,"abstract":"A huge increase in automobile field h as lead t o the creation of different sounds in large volume, especially in urban cities. An analysis of the increased quantity of automobiles will give information related to traffic and vehicles. It also provides a scope to understand the scenario of particular location using sound scape information. In this paper, a two level classification is proposed to classify urban sound events such as bus engine (BE), bus horn (BH), car horn (CH) and whistle (W) sounds. The above sounds are taken as they place a major role in traffic scenario. A real-time data is collected from the live recordings at major locations of the urban city. Prior to the detection of events, the class of events is identified u sing signal processing techniques. Further, features such as Mel-frequency cepstral coefficients (MFCCs) a re extracted based on the analysis of a spectrum of the above-mentioned events and they are prominent to classify even in the complex scenario. Classifiers such as artificial neural networks (ANN), naive-Bayesian (NB), decision tree (J48), random forest (RF) are used at two levels. The proposed approach outperforms the existing approaches that usually does direct feature extraction without signal level analysis.","PeriodicalId":383554,"journal":{"name":"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134585654","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}

引用次数: 11

An efficient framework of data mining and its analytics on massive streams of big data repositories 一个有效的数据挖掘框架及其对大数据存储库的海量流的分析

2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2016-08-01 DOI: 10.1109/DISCOVER.2016.7806259

D. Disha, B. J. Sowmya, Chetan, S. Seema

{"title":"An efficient framework of data mining and its analytics on massive streams of big data repositories","authors":"D. Disha, B. J. Sowmya, Chetan, S. Seema","doi":"10.1109/DISCOVER.2016.7806259","DOIUrl":"https://doi.org/10.1109/DISCOVER.2016.7806259","url":null,"abstract":"Big Data consists of huge volume of complex growing data sets from several independent sources. With the rapid development of data collection and storage capacity, big data are expanding in all science and engineering domains. The most fundamental challenge for big data applications is to scrutinize the large amount of data and extract required information or knowledge for future usage which is beyond the limit of relational databases with respect to storage and processing of massive quantity of data. Intent of this paper is by considering the big data repository as Twitter, dynamically mine the recent tweets related to Kapoor and Sons movie and perform the data mining operation and analytics on it by overcoming the challenges categorized with respect to the HACE theorem. To handle the massive amount of tweets we have used Hadoop Map Reduce framework to perform data mining analytic operations such as data cleansing, data classification and data clustering. Prediction model for the movie review is built by using Naive Bayes Classifier and accuracy of the prediction is calculated with the help of binomial test as it conforms to the Bernoulli distribution. Clustering of Tweets are obtained on the basis of Location and Hash Tags. Additionally privacy for the user tweets are preserved by using Data mining anomaly Technique, results are displayed with the help of intelligent graphs. As a Performance Evaluation of Map Reduce, the predictive analysis is done by using Map Reduce as well as without using Map Reduce, based on the execution time comparison performance graph is obtained to prove Map Reduce is an Efficient framework for huge volume of data.","PeriodicalId":383554,"journal":{"name":"2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132797263","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}

引用次数: 3