Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Bionic models for identification of biological systems

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described

Bionic models for identification of biological systems

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described

Comparison of Non-Coherent Linear Detection Algorithms Applied to a 2-D Numerical Breast Model

A comparative analysis of an imaging method based on a multifrequency Multiple Signal Classification (MUSIC) approach against two common linear detection algorithms based on noncoherent migration is made. The different techniques are tested using synthetic data generated through CST Microwave Studio and a phantom developed from MRI scans of a mostly fat breast. The multifrequency MUSIC approach shows an overall superior performance compared to the noncoherent techniques. This letter reports that this highly performing algorithm does not require any antenna calibration or phase response estimation and allows the use of efficient and complex antenna geometries without difficult algorithm redefinitions

The problems and challenges of managing crowd sourced audio-visual evidence

A number of recent incidents, such as the Stanley Cup Riots, the uprisings in the Middle East and the London riots have demonstrated the value of crowd sourced audio-visual evidence wherein citizens submit audio-visual footage captured on mobile phones and other devices to aid governmental institutions, responder agencies and law enforcement authorities to confirm the authenticity of incidents and, in the case of criminal activity, to identify perpetrators. The use of such evidence can present a significant logistical challenge to investigators, particularly because of the potential size of data gathered through such mechanisms and the added problems of time-lining disparate sources of evidence and, subsequently, investigating the incident(s). In this paper we explore this problem and, in particular, outline the pressure points for an investigator. We identify and explore a number of particular problems related to the secure receipt of the evidence, imaging, tagging and then time-lining the evidence, and the problem of identifying duplicate and near duplicate items of audio-visual evidence

The Many Manifestations of Downsizing: Hierarchical Galaxy Formation Models confront Observations

[abridged] It has been widely claimed that several lines of observational evidence point towards a "downsizing" (DS) of the process of galaxy formation over cosmic time. This behavior is sometimes termed "anti-hierarchical", and contrasted with the "bottom-up" assembly of the dark matter structures in Cold Dark Matter models. In this paper we address three different kinds of observational evidence that have been described as DS: the stellar mass assembly, star formation rate and the ages of the stellar populations in local galaxies. We compare a broad compilation of available data-sets with the predictions of three different semi-analytic models of galaxy formation within the Lambda-CDM framework. In the data, we see only weak evidence at best of DS in stellar mass and in star formation rate. We find that, when observational errors on stellar mass and SFR are taken into account, the models acceptably reproduce the evolution of massive galaxies, over the entire redshift range that we consider. However, lower mass galaxies are formed too early in the models and are too passive at late times. Thus, the models do not correctly reproduce the DS trend in stellar mass or the archaeological DS, while they qualitatively reproduce the mass-dependent evolution of the SFR. We demonstrate that these discrepancies are not solely due to a poor treatment of satellite galaxies but are mainly connected to the excessively efficient formation of central galaxies in high-redshift haloes with circular velocities ~100-200 km/s. [abridged]Comment: MNRAS accepted, 16 pages, 10 figure

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included