Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

Short-term wind power forecast based on cluster analysis and artificial neural networks

[EN]In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids...

Use of inert gas-enriched atmospheres and post-packaging irradiation in shelf-life extension of watercress

Watercress (Nasturtium officinale R. Br.) is a semi-aquatic plant of the Brassicaceae family highly appreciated in the Mediterranean cuisine. It features sharp, peppery and slightly tangy taste and contains health-promoting phytochemicals. Its consumption as a fresh-cut product has increased in recent years, as well as the global market of minimally processed vegetables. This demand is driven by the growing interest in the role of food in promoting the human health and wellbeing and to meet consumer needs for fresh-like and more convenient foods. Due to the reduced shelf-life of this plant, the suitability of inert gas-enriched atmospheres and ionizing irradiation for preserving visual, nutritional and functional quality attributes during cold storage was studied. Watercress samples were gathered in the Northeast region of Portugal, rinsed in tap water and a portion was immediately analyzed (non-stored control). The remaining fresh material was packaged in polyethylene bags under N2- and Ar-enriched atmospheres, conventional atmosphere (air) and vacuum (no atmosphere). Samples under conventional atmosphere were irradiated at 1, 2 and 5 kGy of gamma-rays (predicted doses) in a 60Co experimental chamber. A non-irradiated control followed all the experiment. Then, all packaged samples were stored at 4 ºC for 7 days. The studied quality parameters included the colour that was measured with a Konica Minolta colorimeter, and total soluble solids and pH determined in squeezed juice. The proximate composition (moisture, proteins, fat, ash, carbohydrates and energy) was evaluated using the AOA C procedures. Organic acids, free sugars, fatty acids and tocopherols were analyzed by chromatographic techniques. Samples were also evaluated for its DPPH• scavenging activity, reducing power, and lipid peroxidation inhibition capacity trough the inhibition of the β-carotene bleaching and thiobarbituric acid reactive substances (TBAR S) formation. Differences among treatments were analyzed using the one-way analysis of variance (ANO VA) and a linear discriminant analysis (LDA ) was used to evaluate the effects on the overall postharvest quality. After evaluating the effect on the individual quality parameters, the LDA revealed that the Ar-enriched atmosphere and the irradiation at 2 kGy were suitable processing choices for preserving the integrity of the non-stored control samples during cold storage. Thus, these non-thermal treatments were highlighted for shelf-life extension of fresh-cut watercress.

Nitrogen vs argon: what is the most effective atmosphere for shelf-life extension of buckler sorrel leaves?

Buckler sorrel (Rumex induratus Boiss. & Reut.) is an underutilized leafy vegetable with peculiar sensory properties and potential as a gourmet food. In the food industry, different packaging methods have been used for shelf-life extension, but it is important to know how the quality of minimally processed vegetable is affected by these treatments. Recently, nitrogen and argon have been used for food packaging. Nitrogen is low soluble in water and other food constituents and does not support the growth of aerobic microbes. In turn, argon is biochemically active and appears to interfere with enzymatic oxygen receptor sites. In this study, modified atmospheres enriched with nitrogen and argon were evaluated for shelf-life extension of buckler sorrel leaves. Wild samples were gathered in Bragança, Portugal, considering local consumers’ sites and criteria. Healthy and undamaged leaves were selected, rinsed in tap water, and a portion was immediately analyzed (non-stored control). The remaining fresh material was packaged in polyethylene bags under nitrogen- and argon-enriched atmospheres and a conventional control atmosphere (air). All packaged samples were stored at 4 ºC for 12 days and then analyzed. The headspace gas composition was monitored during storage. Different quality attributes were evaluated, including visual (colour), nutritional (macronutrients, individual sugars and fatty acids) and bioactive (hydrophilic and lipophilic molecules and antioxidant properties) parameters. Different statistical tools were used; the one-way analysis of variance (ANO VA) was applied for analyse the differences among treatments and a linear discriminant analysis (LDA ) was used to evaluate the effects on the overall postharvest quality. The argon-enriched atmosphere better prevent the samples yellowing. The proximate composition did not change significantly during storage. Samples in control atmosphere revealed higher protein and ash contents and lower levels of lipids. The non-stored control samples presented the higher amounts of fructose, glucose and trehalose. The storage time increased the palmitic acid levels and decreased the content in α-linolenic and linoleic acids. The γ- e δ-tocopherols were higher after the 12 days of cold storage. Probably, the synthesis of these lipophilic compounds was a plant strategy to fight against the abiotic stress induced by storage. Higher levels of total phenolics and flavonoids and increased reducing power and β-carotene bleaching inhibition capacity were also found in the stored control samples. Once again, this result may be attributed to the intrinsic plant-protection mechanisms. Overall, the argon atmosphere was more suitable for quality preservation and shelf-life extension of buckler sorrel.

Effects of gamma and electron-beam irradiation on the individual phenolics of Viola tricolor L. edible flowers.

Edible flowers are being used in culinary preparations to improve the sensorial and nutritional qualities of food, besides improving human health due to the profusion in bioactive compounds [1]. Nevertheless, edible flowers are highly perishable and must be free of insects, which is difficult because they are usually cultivated without using pesticides [2]. Food irradiation is an economically viable technology to extend shelf life of foods, improving their hygiene and quality, while disinfesting insects [3]. The efficiency and safety of radiation processing (using Co-60 or electronaccelerators) have been approved by legal authorities (FDA, USDA, WHO, FAO), as also by the scientific community, based on extensive research [4]. Viola tricolor L. (heartseases), from Violaceae family, is one of the most popular edible flowers. Apart from being used as food, it has also been applied for its medicinal properties, mainly due to their biological activity and phenolic composition [5]. Herein, the phenolic compounds were analyzed by HPLC-DAD-ESI/MS and linear discriminant analysis (LDA) was performed to compare the results from flowers submitted to different irradiation doses and technologies (Co-60 and electron-beam). Quercetin-3-O-(6-O-rhamnosylglucoside)-7-O-rhamnoside (Figure 1) was the most abundant compound, followed by quercetin-3-O-rutinoside and acetyl-quercetin-3-O (6-O-rhamnosylglucoside)-7-O-rhamnoside. In general, irradiated samples (mostly with 1 kGy) showed the highest phenolic compounds content. The LDA outcomes indicated that differences among phenolic compounds effectively discriminate the assayed doses and technologies, defining which variables contributed mostly to that separation. This information might be useful to define which dose and/or technology optimizes the content in a specific phenolic compound. Overall, irradiation did not negatively affect the levels of phenolic compounds, providing the possibility of its application to expand the shelf life of V. tricolor and highlighting new commercial solutions for this functional food.

Cottage cheeses functionalized with fennel and chamomile extracts: comparative performance between free and microencapsulated forms

Globally, there is a trend for healthy food products, preferably incorporating natural bioactive ingredients, replacing synthetic additives. From previous screening studies, extracts of Foeniculum vulgare Mill. (fennel) and Matricaria recutita L. (chamomile) maintained nutritional properties and improved the antioxidant activity of cottage cheese. Nevertheless, this effect was limited to 7 days. Accordingly, aqueous extracts of these plants were microencapsulated in alginate and incorporated into cottage cheese to achieve an extended bioactivity. Plain cottage cheese, and cheese functionalized by direct addition of free decoctions, were prepared and compared. Independently of plant species, "functionalization type" factor did not show a significant effect on the nutritional parameters, as also confirmed in the linear discriminant analysis, where these parameters were not selected as discriminating variables. Furthermore, samples functionalized with microencapsulated extracts showed higher antioxidant activity after the 7th day, thereby demonstrating that the main purpose of this experimental work was achieved.

Basil as functional and preserving ingredient in “Serra da Estrela” cheese

Antitumor, antimicrobial and antioxidant activities of basil were studied, along with its characterization in phenolic compounds, organic acids and soluble sugars. The results placed basil as a valuable candidate for functionalization and conservation of food products, maintaining their nutritional properties, while increasing their shelf life and potential health effects. The basil leaves were then incorporated in "Serra da Estrela Cheese", either in its dehydrated form or as a decoction. The cheeses were then subject to a nutritional evaluation, being characterized for their fatty acids, minerals and CIE color parameters. To assess the combined effects of plant incorporation and storage time, a 2-way ANOVA was used to process the results, further analysed through a linear discriminant analysis. Overall, basil leaves provided antioxidant activity to the cheeses, reduced the moisture, and preserved the unsaturated fatty acids and proteins. Comparing both incorporation types, the decoctions had a higher functionalizing and conservative effect.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.

Elemental Analysis and Forensic Comparison of Soils by Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)

The elemental analysis of soil is useful in forensic and environmental sciences. Methods were developed and optimized for two laser-based multi-element analysis techniques: laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS). This work represents the first use of a 266 nm laser for forensic soil analysis by LIBS. Sample preparation methods were developed and optimized for a variety of sample types, including pellets for large bulk soil specimens (470 mg) and sediment-laden filters (47 mg), and tape-mounting for small transfer evidence specimens (10 mg). Analytical performance for sediment filter pellets and tape-mounted soils was similar to that achieved with bulk pellets. An inter-laboratory comparison exercise was designed to evaluate the performance of the LA-ICP-MS and LIBS methods, as well as for micro X-ray fluorescence (μXRF), across multiple laboratories. Limits of detection (LODs) were 0.01-23 ppm for LA-ICP-MS, 0.25-574 ppm for LIBS, 16-4400 ppm for µXRF, and well below the levels normally seen in soils. Good intra-laboratory precision (≤ 6 % relative standard deviation (RSD) for LA-ICP-MS; ≤ 8 % for µXRF; ≤ 17 % for LIBS) and inter-laboratory precision (≤ 19 % for LA-ICP-MS; ≤ 25 % for µXRF) were achieved for most elements, which is encouraging for a first inter-laboratory exercise. While LIBS generally has higher LODs and RSDs than LA-ICP-MS, both were capable of generating good quality multi-element data sufficient for discrimination purposes. Multivariate methods using principal components analysis (PCA) and linear discriminant analysis (LDA) were developed for discriminations of soils from different sources. Specimens from different sites that were indistinguishable by color alone were discriminated by elemental analysis. Correct classification rates of 94.5 % or better were achieved in a simulated forensic discrimination of three similar sites for both LIBS and LA-ICP-MS. Results for tape-mounted specimens were nearly identical to those achieved with pellets. Methods were tested on soils from USA, Canada and Tanzania. Within-site heterogeneity was site-specific. Elemental differences were greatest for specimens separated by large distances, even within the same lithology. Elemental profiles can be used to discriminate soils from different locations and narrow down locations even when mineralogy is similar.

Insights into population structure of Diplodus vulgaris along the SW Portuguese coast from otolith elemental signatures

The common two-banded sea bream (Diplodus vulgaris) is an important fish in the marine ecosystems of the NW Atlantic and Mediterranean. In southern Portugal it is a major fishery resource being targeted mainly by the artisanal fleets. Although there is some knowledge of the age, growth and reproductive biology of the species, information about its population structure is scarce and somewhat limited to the Mediterranean Sea. In this study the otolith elemental signatures of 90 specimens of D. vulgaris of the same age group (2+ years) and cohort collected from the important fishery regions of SW Portugal (Sesimbra, Sagres and Faro) have been analysed by inductively coupled plasma mass spectrometry (ICP-MS). Two different methodologies have been applied: solution based analysis of the whole otoliths; representative of the entire life-history prior to capture, and laser ablation analysis of otolith cores; representative of the larval and early post-settlement phase. Whole otolith comparisons utilised Sr/Ca, Ba/Ca, Mn/Ca, Li/Ca and Ni/Ca to demonstrate regional population structure. Classification accuracy rates from linear discriminant function analyses (LDFA) of whole otolith chemistry data were high for each region; Faro - 93%, Sagres - 90% and Sesimbra - 80%. Comparison of the otolith core chemistry utilised Sr/Ca, Ba/Ca, Mn/Ca and Mg/Ca and Zn/Ca. LDFA for the otolith core chemistry also achieved accurate classification for samples from Sesimbra (73%), but there was high overlap of otolith chemistry between samples from Faro and Sagres (47 and 43% classification accuracy respectively). The whole otolith results suggest that D. vulgaris are resident in the regional fishing areas during the juvenile phase. Both the core and whole otolith chemistry data supported separation of the Sesimbra fishery region from the more southern and closely associated Faro and Sagres regions for management purposes. However, while the whole otolith data indicated that the populations at Faro and Sagres likely remained separated in the juvenile stage, the otolith core chemistry data was inconclusive as to whether recruitment to these two areas was derived, or not, from different spawning areas.

Data-driven Signal Processing per Jamming Detection in Sistemi Satellitari

In questo elaborato vengono analizzate differenti tecniche per la detection di jammer attivi e costanti in una comunicazione satellitare in uplink. Osservando un numero limitato di campioni ricevuti si vuole identificare la presenza di un jammer. A tal fine sono stati implementati i seguenti classificatori binari: support vector machine (SVM), multilayer perceptron (MLP), spectrum guarding e autoencoder. Questi algoritmi di apprendimento automatico dipendono dalle features che ricevono in ingresso, per questo motivo è stata posta particolare attenzione alla loro scelta. A tal fine, sono state confrontate le accuratezze ottenute dai detector addestrati utilizzando differenti tipologie di informazione come: i segnali grezzi nel tempo, le statistical features, le trasformate wavelet e lo spettro ciclico. I pattern prodotti dall’estrazione di queste features dai segnali satellitari possono avere dimensioni elevate, quindi, prima della detection, vengono utilizzati i seguenti algoritmi per la riduzione della dimensionalità: principal component analysis (PCA) e linear discriminant analysis (LDA). Lo scopo di tale processo non è quello di eliminare le features meno rilevanti, ma combinarle in modo da preservare al massimo l’informazione, evitando problemi di overfitting e underfitting. Le simulazioni numeriche effettuate hanno evidenziato come lo spettro ciclico sia in grado di fornire le features migliori per la detection producendo però pattern di dimensioni elevate, per questo motivo è stato necessario l’utilizzo di algoritmi di riduzione della dimensionalità. In particolare, l'algoritmo PCA è stato in grado di estrarre delle informazioni migliori rispetto a LDA, le cui accuratezze risentivano troppo del tipo di jammer utilizzato nella fase di addestramento. Infine, l’algoritmo che ha fornito le prestazioni migliori è stato il Multilayer Perceptron che ha richiesto tempi di addestramento contenuti e dei valori di accuratezza elevati.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Adaptive linear classifier by linear programming,

Mode of access: Internet.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.