Marian Popescu

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 In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a(More)
 The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been implemented to automatically detect and recognize pulse train songs of minke whales. While the long term goal of this work(More)
 In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a(More)
This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm-Acoustic Data-mining Accelerator (ADA)-is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together,(More)
Recent progress in patterned microelectrode manufacturing technology and microfluidics has opened the way to a large variety of cellular and molecular biosensor-based applications. In this extremely diverse and rapidly expanding landscape, silicon-based technologies occupy a special position, given their statute of mature, consolidated, and highly(More)
In September and October 2011, a seismic survey took place in Baffin Bay, Western Greenland, in close proximity to a marine protected area (MPA). As part of the mitigation effort, five bottom-mounted marine acoustic recording units (MARUs) collected data that were used for the purpose of measuring temporal and spectral features from each impulsive event,(More)
Accurate forecasting of fine particulate matter concentration in cities is an important problem that can be solved with efficient methods as those provided by computational intelligence, which apply a data driven approach. An example of such method is given by artificial neural networks that are universal approximators, providing very good solutions to time(More)
Recent studies on air pollution emphasized particulate matter impact on human health and climate changes. This impact generated a trend for developing research projects which deal with monitoring and forecasting air quality. This paper fits into this trend and presents an ANFIS (adaptive neuro-fuzzy inference system) modelling approach to predict(More)
The paper addresses the control problem of a binary distillation processes. There are analyzed the quality specifications of separated products and also the distributed and multivariable character of the process. The robustness of the control system lies in its simplicity versus the investment. The paper presents the studies made for development and(More)