Diego Furtado Silva

Analysis of pigments in polychromes by use of laser induced breakdown spectroscopy and Raman microscopy

Abstract Two laser-based analytical techniques, Laser Induced Breakdown Spectroscopy (LIBS) and Raman microscopy, have been used for the identification of pigments on a polychrome from the Rococo period. Detailed spectral data are presented from analyses performed on a fragment of a gilded altarpiece from the church of Escatron, Zaragoza, Spain. LIBS measurements yielded elemental analytical data which suggest the presence of certain pigments and, in addition, provide information on the stratigraphy of the paint layers. Identification of most pigments and of the materials used in the preparation layer was performed by Raman microscopy.

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for text and a handful of other datatypes. However, surprisingly little progress has been made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for two time series data mining problems, including motif discovery and novelty discovery.

Data stream classification guided by clustering on nonstationary environments and extreme verification latency

Sao Paulo Research Foundation (FAPESP) (grant numbers 2011/17698-5, 2012/50714-7, 2013/26151-5)

Time Series Classification Using Compression Distance of Recurrence Plots

There is a huge increase of interest for time series methods and techniques. Virtually every piece of information collected from human, natural, and biological processes is susceptible to changes over time, and the study of how these changes occur is a central issue in fully understanding such processes. Among all time series mining tasks, classification is likely to be the most prominent one. In time series classification there is a significant body of empirical research that indicates that k-nearest neighbor rule in the time domain is very effective. However, certain time series features are not easily identified in this domain and a change in representation may reveal some significant and unknown features. In this work, we propose the use of recurrence plots as representation domain for time series classification. Our approach measures the similarity between recurrence plots using Campana-Keogh (CK-1) distance, a Kolmogorov complexity-based distance that uses video compression algorithms to estimate image similarity. We show that recurrence plots allied to CK-1 distance lead to significant improvements in accuracy rates compared to Euclidean distance and Dynamic Time Warping in several data sets. Although recurrence plots cannot provide the best accuracy rates for all data sets, we demonstrate that we can predict ahead of time that our method will outperform the time representation with Euclidean and Dynamic Time Warping distances.

Speeding up all-pairwise dynamic time warping matrix calculation

Dynamic Time Warping (DTW) is certainly the most relevant distance for time series analysis. However, its quadratic time complexity may hamper its use, mainly in the analysis of large time series data. All the recent advances in speeding up the exact DTW calculation are confined to similarity search. However, there is a significant number of important algorithms including clustering and classification that require the pairwise distance matrix for all time series objects. The only techniques available to deal with this issue are constraint bands and DTW approximations. In this paper, we propose the first exact approach for speeding up the all-pairwise DTW matrix calculation. Our method is exact and may be applied in conjunction with constraint bands. We demonstrate that our algorithm reduces the runtime in approximately 50% on average and up to one order of magnitude in some datasets.

Laser-induced breakdown spectroscopy and Raman microscopy for analysis of pigments in polychromes

Abstract A polychrome from the Rococo period was analysed by use of two laser-based analytical techniques, laser-induced breakdown spectroscopy (LIBS) and Raman microscopy. The analysis, performed on a fragment of a gilded altarpiece from the church of Escatron, Zaragoza, Spain, provided detailed spectral data that have been used for the identification of pigments. LIBS measurements yielded elemental analytical data that suggest the presence of certain pigments and, in addition, provide information on the stratigraphy of the paint layers. Identification of most pigments and of the materials used in the preparation layer was performed by Raman microscopy.

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Insects have a close relationship with the humanity, in both positive and negative ways. Mosquito borne diseases kill millions of people and insect pests consume and destroy around US

Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

40 billion worth of food each year. In contrast, insects pollinate at least two-thirds of all the food consumed in the world. In order to control populations of disease vectors and agricultural pests, researchers in entomology have developed numerous methods including chemical, biological and mechanical approaches. However, without the knowledge of the exact location of the insects, the use of these techniques becomes costly and inefficient. We are developing a novel sensor as a tool to control disease vectors and agricultural pests. This sensor, which is built from inexpensive commodity electronics, captures insect flight information using laser light and classifies the insects according to their species. The use of machine learning techniques allows the sensor to automatically identify the species without human intervention. Finally, the sensor can provide real-time estimates of insect species with virtually no time gap between the insect identification and the delivery of population estimates. In this paper, we present our solution to the most important challenge to make this sensor practical: the creation of an accurate classification system. We show that, with the correct combination of feature extraction and machine learning techniques, we can achieve an accuracy of almost 90 % in the task of identifying the correct insect species among nine species. Specifically, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito.

LIBS spectra of polychromes with a low cost CCD camera based detector

Throughout the history, insects have had an intimate relationship with humanity, both positive and negative. Insects are vectors of diseases that kill millions of people every year and, at the same time, insects pollinate most of the worlds food production. Consequently, there is a demand for new devices able to control the populations of harmful insects while having a minimal impact on beneficial insects. In this paper, we present an intelligent trap that uses a laser sensor to selectively classify and catch insects. We perform an extensive evaluation of different feature sets from audio analysis and machine learning algorithms to construct accurate classifiers for the insect classification task. Support Vector Machines achieved the best results with a MFCC feature set, which consists of coefficients from frequencies scaled according to the human auditory system. We evaluate our classifiers in multiclass and binary class settings, and show that a binary class classifier that recognizes the mosquito species achieved almost perfect accuracy, assuring the applicability of the proposed intelligent trap.

Extracting Texture Features for Time Series Classification

Abstract Two real samples of polychromes, from the Spanish Baroque period and from the XV century, were analysed by time-integrated laser-induced breakdown spectroscopy (LIBS). The time-integrated spectra showed negligible contribution of continuum background emission. The spectra of the Baroque sample indicated the presence of vermilion; this was confirmed by near-infrared Fourier transform spectroscopy. LIBS spectra of the XV century sample showed Ca, Al, Mg, Na and Pb lines and the molecular emissions CN(B-X) and C 2 (d-a). Relative spectral intensities were measured as a function of the number of laser pulses delivered at the same position of the sample.