Showing items 1 - 4 of 4

Your selections:

Business context in big data analytics

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
  • Date: 2015
  • Type: Text , Conference proceedings
  • Relation: 10th International Conference on Information, Communications and Signal Processing, ICICS 2015; Singapore; 2nd-4th December 2015
  • Full Text: false
  • Reviewed:
  • Description: Big data are generated from a variety of sources having different representation forms and formats, it raises a research question as how important data relevant to a business context can be captured and analyzed more accurately to represent deep and relevant business insight. There is a number of existing big data analytic methods available in the literature that consider contextual information such as the context of a query and its users, the context of a query-driven recommendation system, etc. However, these methods still have many challenges and none of them has considered the context of a business in either data collection or analysis process. To address this research gap, we introduce a big data analytic technique which embeds a business context in terms of the significance level of a query into the bedrock of its data collection and analysis process. We implemented our proposed model under the framework of Hadoop considering the context of a grocery shop. The results exhibit that our method substantially increases the amount of data collection and their deep insight with an increase of the significance level value. © 2015 IEEE.
  • Description: 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

Machine fault severity estimation based on adaptive wavelet nodes selection and SVM

- Yaqub, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Yaqub, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: IEEE International Conference on Mechatronics and Automation (ICMA),Beijing 7 August 2011 to 10 August 2011) p. 1951-1956
  • Full Text: false
  • Reviewed:
  • Description: The study is focused on estimating the severity level of the bearing faults which helps in determining the residual life of the equipment and planned maintenance. A novel technique, adaptive severity estimation model (ASEM) is proposed based on adaptive selection of wavelet decomposition nodes and support vector machines. Vibration data from multiple severity levels are used to build the fault estimation model. An adaptive criterion for wavelet decomposition node selection is developed which helps ASEM to achieve robustness in estimating fault severity under varying signal to noise ratio (SNR), a key demand in industrial environment. The simulated data with known severity level is used to parameterize the estimation model. The fault severity estimation performance of ASEM is also validated for the real vibration data and its robustness is gauged under varying SNR conditions.

Heraclitus: A LFSR-based stream cipher with key dependent structure

- Colbert, Bernard, Dekker, Anthony, Batten, Lynn

  • Authors: Colbert, Bernard , Dekker, Anthony , Batten, Lynn
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: We describe Heraclitus as an example of a stream cipher that uses a 128 bit index string to specify the structure of each instance in real time: each instance of Heraclitus will be a stream cipher based on mutually clocked shift registers. © 2011 IEEE.

Time-frequency filter bank: A simple approach for audio and music separation

- Yang, Ning, Usman, Muhammad, He, Xiangjian, Jan, Mian, Zhang, Liming

  • Authors: Yang, Ning , Usman, Muhammad , He, Xiangjian , Jan, Mian , Zhang, Liming
  • Date: 2017
  • Type: Text , Journal article
  • Relation: IEEE access Vol. 5, no. (2017), p. 27114-27125
  • Full Text: false
  • Reviewed:
  • Description: Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.
  • 1