The Crime rate and criminal activities have increased enormously in the past few decades. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited and on the other hand, the number of criminals are increasing drastically. Hence to support the law keepers, data about the criminals, criminal history together with criminal attitudes will be very much benefitted. This paper aims towards the construction of new methodologies based on Data mining concepts and serves as a decision support system.
Uploaded by tijcsajournaltijcsa on 06/29/2012
Digital publication details: 10 pages.
http://www.aiaa-journal.org The trimming scheme with a prefixed cutoff portion is known as a method of improving the robustness of statistical models such as multivariate Gaussian mixture models (MG-MMs) in small scale tests by alleviating the impacts of outliers. However, when this method is applied to real-world data, such as noisy speech processing, it is hard to know the optimal cut-off portion to remove the outliers and sometimes removes useful data samples as well.
Uploaded by sep2011--now on 02/19/2013
Digital publication details: 13 pages.
Image noise is unwanted information in an image and can occur at any moment of time such as during image capture, transmission, or processing and it may or may not depend on image content. In order to remove the noise from the noisy image, prior knowledge about the nature of noise must be known otherwise noise removal causes the image blurring. Identifying nature of noise is a challenging problem. Many researchers have proposed their ideas on image noise identification and each of the work has its assumptions, advantages and limitations. In this paper, we proposed a new methodology based on neural network for identifying the different types of noise such as Non Gaussian, Gaussian white, Salt and Pepper and Speckle noise.
Uploaded by ideseditor on 01/16/2013
Digital publication details: 4 pages.
The popular press and a recent spate of remarkable books have pointed critically to the contribution of financial innovation and quantitative models to the financial crisis. These critiques have cited particular statistical approaches, such as the Gaussian copula, for massively underestimating systemic risk. The broader critiques doubt the risk management capabilities of firms and regulators to understand and evaluate complex financial instruments, such as synthetic securities. These critiques cut at the core of the Basel II international regulations which permitted banks to create their own models to value illiquid and risky assets. They also have major implications for the design of a regulatory system and regulations regarding whether regulation is at all possible, who is best able to do it, and ultimately if complex financial innovation should be sharply curtailed.
Uploaded by bernsteincenter on 11/01/2010
Digital publication details: 24 pages.
An algorithm to solve security constrained unit commitment problem (UCP) with both operational and power flow constraints (PFC) have been proposed to plan a secure and economical hourly generation schedule. This proposed algorithm introduces an efficient unit commitment (UC) approach with PFC that obtains the minimum system operating cost satisfying both unit and network constraints when contingencies are included. In the proposed model repeated optimal power flow for the satisfactory unit combinations for every line removal under given study period has been carried out to obtain UC solutions with both unit and network constraints. The system load demand patterns have been obtained for the test case systems taking into account of the hourly load variations at the load buses by adding Gaussian random noises.
Uploaded by ideseditor on 01/18/2013
Digital publication details: 9 pages.
Due to aggressive scaling and process imperfection in sub-45 nm technology node Vt (threshold voltage) shift is more pronounced causing large variations in circuit response. Therefore, this paper presents the analyses of various popular 1-bit digital summing circuits in light of PVT (process, voltage and temperature) variations to verify their functionality and robustness. The investigation is carried with ±3ó process parameters and ±10% VDD (supply voltage) variation by applying Gaussian distribution and Monte Carlo analysis at 22 nm technology node on HSPICE environment. Design guidelines are derived to select the most suitable topology for the design features required. Transmission Gate (TG)-based digital summing circuit is found to be the most robust against PVT variations.
Uploaded by ideseditor on 01/14/2013
Digital publication details: 9 pages.
This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a “primary” input containing the corrupted Signal and a “reference” input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. Computer simulations with uncorrelated Gaussian noise and signals confirm the results of the analysis and demonstrate the effectiveness of the least mean squares (LMS) algorithms.This Adaptive Noise Canceller is then useful for enhancing the S/N ratio of data collected from sensors (or sensor arrays) working in noisy environment, or dealing with potentially weak signals.
Uploaded by iserp on 04/21/2011
Digital publication details: 5 pages.
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Uploaded by ajkawa on 07/15/2012
Digital publication details: 264 pages.
EM units summary for Gauss and SI units
Uploaded by ucaptd3 on 11/04/2012
Digital publication details: 12 pages.
Robot y articulacioes
Uploaded by joseluisola on 04/18/2013
Digital publication details: 321 pages.