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Prof. mateus mendes
Polytechnic Institute of Coimbra

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Research Keywords & Expertise

0 Data Mining
0 Deep Learning
0 Artificial neural networks (ANN)
0 Machine Learning & Artificial Intelligence
0 Computer Vision and Image Processing

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Artificial neural networks (ANN)
Deep Learning
Computer Vision and Image Processing

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Journal article
Published: 30 June 2021 in Applied Sciences
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Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.

ACS Style

Balduíno Mateus; Mateus Mendes; José Farinha; António Cardoso. Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Applied Sciences 2021, 11, 6101 .

AMA Style

Balduíno Mateus, Mateus Mendes, José Farinha, António Cardoso. Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Applied Sciences. 2021; 11 (13):6101.

Chicago/Turabian Style

Balduíno Mateus; Mateus Mendes; José Farinha; António Cardoso. 2021. "Anticipating Future Behavior of an Industrial Press Using LSTM Networks." Applied Sciences 11, no. 13: 6101.

Journal article
Published: 22 May 2021 in Applied Sciences
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Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

ACS Style

Ana Malta; Mateus Mendes; Torres Farinha. Augmented Reality Maintenance Assistant Using YOLOv5. Applied Sciences 2021, 11, 4758 .

AMA Style

Ana Malta, Mateus Mendes, Torres Farinha. Augmented Reality Maintenance Assistant Using YOLOv5. Applied Sciences. 2021; 11 (11):4758.

Chicago/Turabian Style

Ana Malta; Mateus Mendes; Torres Farinha. 2021. "Augmented Reality Maintenance Assistant Using YOLOv5." Applied Sciences 11, no. 11: 4758.

Journal article
Published: 25 February 2021 in Symmetry
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Measuring biometric tree characteristics to estimate the volume of wood in a forest area is a time consuming task. It is usually performed by a team of two or more people, who measure the diameter and height of several trees in sampling plots. The results are then extrapolated for the forest stand. The present paper describes a method which facilitates estimating tree biometric parameters using computational techniques. A camera takes two pictures of each sample tree, with an especially designed target placed close to the tree, to facilitate image processing and camera calibration steps. Taking advantage of the trees’ natural shape and assuming a symmetric stem, the diameter and height of the tree stems are estimated from the images and the volumes of the tree stems are calculated. Experimental trials show promising results, exhibiting errors similar to the traditional methods used currently, in the range of 10%, showing that the method is suitable for forest inventory.

ACS Style

João Coelho; Beatriz Fidalgo; Manuel Crisóstomo; Raúl Salas-González; A. Coimbra; Mateus Mendes. Non-Destructive Fast Estimation of Tree Stem Height and Volume Using Image Processing. Symmetry 2021, 13, 374 .

AMA Style

João Coelho, Beatriz Fidalgo, Manuel Crisóstomo, Raúl Salas-González, A. Coimbra, Mateus Mendes. Non-Destructive Fast Estimation of Tree Stem Height and Volume Using Image Processing. Symmetry. 2021; 13 (3):374.

Chicago/Turabian Style

João Coelho; Beatriz Fidalgo; Manuel Crisóstomo; Raúl Salas-González; A. Coimbra; Mateus Mendes. 2021. "Non-Destructive Fast Estimation of Tree Stem Height and Volume Using Image Processing." Symmetry 13, no. 3: 374.

Journal article
Published: 03 February 2021 in Journal of Imaging
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Quality control of heat sealed bottles is very important to minimize waste and in some cases protect people’s health. The present paper describes a case study where an automated non invasive and non destructive quality control system was designed to assess the quality of the seals of bottles containing pesticide. In this case study, the integrity of the seals is evaluated using an artificial neural network based on images of the seals processed with computer vision techniques. Because the seals are not directly visible from the bottle exterior, the images are infrared pictures obtained using a thermal camera. The method is non invasive, automated, and can be applied to common conveyor belts currently used in industrial plants. The results show that the inspection process is effective in identifying defective seals with a precision of 98.6% and a recall of 100% and because it is automated it can be scaled up to large bottle processing plants.

ACS Style

Samuel Cruz; António Paulino; Joao Duraes; Mateus Mendes. Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network. Journal of Imaging 2021, 7, 24 .

AMA Style

Samuel Cruz, António Paulino, Joao Duraes, Mateus Mendes. Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network. Journal of Imaging. 2021; 7 (2):24.

Chicago/Turabian Style

Samuel Cruz; António Paulino; Joao Duraes; Mateus Mendes. 2021. "Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network." Journal of Imaging 7, no. 2: 24.

Journal article
Published: 09 September 2020 in Energies
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The use of clean and renewable energy sources is increasingly important, for economic and environmental reasons. Wind plays a key role among renewable energy sources. Hence, the location, monitoring and maintenance of wind turbines are areas that have received more and more attention in recent years. The paper presents a survey of datasets of wind resources, wind farm installed capacity and wind farm operation, which contain generous amounts of data. Those datasets are important tools, freely available for analysis of wind resources and study of the performance of wind turbines. A short analysis of one of the datasets is also presented, identifying different operational regions, and the ones more likely to aggregate failures. Principal Component Analysis (PCA) is used to study wind turbines’ behavior.

ACS Style

Diogo Menezes; Mateus Mendes; Jorge Alexandre Almeida; Torres Farinha. Wind Farm and Resource Datasets: A Comprehensive Survey and Overview. Energies 2020, 13, 4702 .

AMA Style

Diogo Menezes, Mateus Mendes, Jorge Alexandre Almeida, Torres Farinha. Wind Farm and Resource Datasets: A Comprehensive Survey and Overview. Energies. 2020; 13 (18):4702.

Chicago/Turabian Style

Diogo Menezes; Mateus Mendes; Jorge Alexandre Almeida; Torres Farinha. 2020. "Wind Farm and Resource Datasets: A Comprehensive Survey and Overview." Energies 13, no. 18: 4702.

Journal article
Published: 12 June 2020 in Eksploatacja i Niezawodnosc - Maintenance and Reliability
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ACS Style

João Carlos Antunes Rodrigues; Inês Cost; J. Torres Farinha; Mateus Mendes; Luís Margalho. Predicting motor oil condition using artificial neural networks and principal component analysis. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020, 22, 440 -448.

AMA Style

João Carlos Antunes Rodrigues, Inês Cost, J. Torres Farinha, Mateus Mendes, Luís Margalho. Predicting motor oil condition using artificial neural networks and principal component analysis. Eksploatacja i Niezawodnosc - Maintenance and Reliability. 2020; 22 (3):440-448.

Chicago/Turabian Style

João Carlos Antunes Rodrigues; Inês Cost; J. Torres Farinha; Mateus Mendes; Luís Margalho. 2020. "Predicting motor oil condition using artificial neural networks and principal component analysis." Eksploatacja i Niezawodnosc - Maintenance and Reliability 22, no. 3: 440-448.

Research article
Published: 26 November 2019 in International Journal of Food Sciences and Nutrition
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This study aimed at investigating the influence of some sociodemographic factors on the eating motivations. A longitudinal study was carried conducted with 11960 participants from 16 countries. Data analysis included t-test for independent samples or ANOVA, and neural network models were also created, to relate the input and output variables. Results showed that factors like age, marital status, country, living environment, level of education or professional area significantly influenced all of the studied types of eating motivations. Neural networks modelling indicated variability in the food choices, but identifying some trends, for example the strongest positive factor determining health motivations was age, while for emotional motivations was living environment, and for economic and availability motivations was gender. On the other hand, country revealed a high positive influence for the social and cultural as well as for environmental and political and also for marketing and commercial motivations.

ACS Style

Raquel P. F. Guiné; Ana Cristina Ferrão; Manuela Ferreira; Paula Correia; Mateus Mendes; Elena Bartkiene; Viktória Szűcs; Monica Tarcea; Marijana Matek Sarić; Maša Černelič-Bizjak; Kathy Isoldi; Ayman El-Kenawy; Vanessa Ferreira; Dace Klava; Malgorzata Korzeniowska; Elena Vittadini; Marcela Leal; Lucia Frez-Muñoz; Maria Papageorgiou; Ilija Djekic. Influence of sociodemographic factors on eating motivations – modelling through artificial neural networks (ANN). International Journal of Food Sciences and Nutrition 2019, 71, 614 -627.

AMA Style

Raquel P. F. Guiné, Ana Cristina Ferrão, Manuela Ferreira, Paula Correia, Mateus Mendes, Elena Bartkiene, Viktória Szűcs, Monica Tarcea, Marijana Matek Sarić, Maša Černelič-Bizjak, Kathy Isoldi, Ayman El-Kenawy, Vanessa Ferreira, Dace Klava, Malgorzata Korzeniowska, Elena Vittadini, Marcela Leal, Lucia Frez-Muñoz, Maria Papageorgiou, Ilija Djekic. Influence of sociodemographic factors on eating motivations – modelling through artificial neural networks (ANN). International Journal of Food Sciences and Nutrition. 2019; 71 (5):614-627.

Chicago/Turabian Style

Raquel P. F. Guiné; Ana Cristina Ferrão; Manuela Ferreira; Paula Correia; Mateus Mendes; Elena Bartkiene; Viktória Szűcs; Monica Tarcea; Marijana Matek Sarić; Maša Černelič-Bizjak; Kathy Isoldi; Ayman El-Kenawy; Vanessa Ferreira; Dace Klava; Malgorzata Korzeniowska; Elena Vittadini; Marcela Leal; Lucia Frez-Muñoz; Maria Papageorgiou; Ilija Djekic. 2019. "Influence of sociodemographic factors on eating motivations – modelling through artificial neural networks (ANN)." International Journal of Food Sciences and Nutrition 71, no. 5: 614-627.

Journal article
Published: 12 July 2019 in Algorithms
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Augmented Reality is increasingly used for enhancing user experiences in different tasks. The present paper describes a model combining augmented reality and artificial intelligence algorithms in a 3D model of an area of the city of Coimbra, based on information extracted from OpenStreetMap. The augmented reality effect is achieved using a video projection over a 3D printed map. Users can interact with the model using a smart phone or similar device and simulate itineraries which are optimized using a genetic algorithm and A*. Among other applications, the model can be used for tourists or travelers to simulate travels with realism, as well as virtual reconstructions of historical places or remote areas.

ACS Style

Mateus Mendes; Jorge Almeida; Hajji Mohamed; Rudi Giot. Projected Augmented Reality Intelligent Model of a City Area with Path Optimization. Algorithms 2019, 12, 140 .

AMA Style

Mateus Mendes, Jorge Almeida, Hajji Mohamed, Rudi Giot. Projected Augmented Reality Intelligent Model of a City Area with Path Optimization. Algorithms. 2019; 12 (7):140.

Chicago/Turabian Style

Mateus Mendes; Jorge Almeida; Hajji Mohamed; Rudi Giot. 2019. "Projected Augmented Reality Intelligent Model of a City Area with Path Optimization." Algorithms 12, no. 7: 140.

Conference paper
Published: 18 August 2018 in Transactions on Engineering Technologies
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ASSIS is a service robot which uses a camera, sonars and infra-red sensors for navigation. It uses images stored into a Sparse Distributed Memory, implemented in parallel in a Graphics Processing Unit, as a method for robot localization and navigation. It is controlled from a web-based interface. Algorithms for following previously learnt paths using visual and odometric information are described. A stack-based method for avoiding random obstacles, using visual information, is proposed. The results show the algorithms are adequate for indoors robot localization and navigation.

ACS Style

Mateus Mendes; A. Paulo Coimbra; Manuel M. Crisóstomo; Manuel Cruz. Vision-Based Collision Avoidance for Service Robot. Transactions on Engineering Technologies 2018, 233 -248.

AMA Style

Mateus Mendes, A. Paulo Coimbra, Manuel M. Crisóstomo, Manuel Cruz. Vision-Based Collision Avoidance for Service Robot. Transactions on Engineering Technologies. 2018; ():233-248.

Chicago/Turabian Style

Mateus Mendes; A. Paulo Coimbra; Manuel M. Crisóstomo; Manuel Cruz. 2018. "Vision-Based Collision Avoidance for Service Robot." Transactions on Engineering Technologies , no. : 233-248.

Journal article
Published: 01 April 2018
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The present study aimed at investigating the influence of several production factors, conservation conditions, and extraction procedures on the phenolic compounds and antioxidant activity of blueberries from different cultivars. The experimental data was used to train artificial neural networks, using a feed-forward model, which gave information about the variables affecting the antioxidant activity and the concentration of phenolic compounds in blueberries. The ANN input variables were location, cultivar, the age of the bushes, the altitude of the farm, production mode, state, storage time, type of extract and order of extract, while the output variables were total phenolic compounds, tannins as well as ABTS and DPPH antioxidant activity. The ANN model was fairly good, with values of the correlation factor for the whole dataset varying from 0.948 to 0.979, while the values of mean squared error were ranging from 0.846 to 0.018, for DPPH antioxidant acidity and anthocyanins, respectively. The results obtained showed that the methanol extracts contained higher amounts of total phenols and anthocyanins as compared to acetone: water extracts, while presenting similar quantities of tannins in both extracts. The blueberries from organic farming were richer in phenolic compounds and possessed higher antioxidant activity than those from conventional agriculture. Even though the effect of storage was not established with high certainty, a trend was observed for an increase in the phenolic compounds and antioxidant activity along storage, either when under refrigeration or under freezing, for the storage periods evaluated.

ACS Style

Raquel Guiné; Christophe Gonçalves; Susana Matos; Fernando Gonçalves; Daniela V.T.A Costa; Mateus Mendes. Modeling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries. 2018, 37, 193 -212.

AMA Style

Raquel Guiné, Christophe Gonçalves, Susana Matos, Fernando Gonçalves, Daniela V.T.A Costa, Mateus Mendes. Modeling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries. . 2018; 37 (2):193-212.

Chicago/Turabian Style

Raquel Guiné; Christophe Gonçalves; Susana Matos; Fernando Gonçalves; Daniela V.T.A Costa; Mateus Mendes. 2018. "Modeling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries." 37, no. 2: 193-212.

Conference paper
Published: 28 March 2018 in Organizing Smart Buildings and Cities
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We propose a customer-focused approach to design product traceability for Industry 4.0. Our design-science research includes a review of traceability technologies and participative enterprise modeling in the ceramic industry. We find benefits in combining Business Process Modeling Notation and Goal-oriented Requirements Language representations to (1) promote reflection by experts with different backgrounds, (2) reach consensus with a solution that addresses the goals of multiple stakeholders, and (3) ensure that customers’ needs are a priority in traceability design. The resulting model combines technologies in different stages of the product lifecycle and is implemented in a cloud-based MES (Manufacturing Execution System) prototype. Depending on each stage and strategic intention, the identification code can be embedded in the product, transport, or package. Our contribution can assist managers in the creation of cloud-based MES to support traceability integration at (1) technological, (2) vertical, and (3) horizontal levels that are required in the fourth industrial revolution.

ACS Style

João Barata; Paulo Rupino Da Cunha; Anand Subhashchandra Gonnagar; Mateus Mendes. Product Traceability in Ceramic Industry 4.0: A Design Approach and Cloud-Based MES Prototype. Organizing Smart Buildings and Cities 2018, 187 -204.

AMA Style

João Barata, Paulo Rupino Da Cunha, Anand Subhashchandra Gonnagar, Mateus Mendes. Product Traceability in Ceramic Industry 4.0: A Design Approach and Cloud-Based MES Prototype. Organizing Smart Buildings and Cities. 2018; ():187-204.

Chicago/Turabian Style

João Barata; Paulo Rupino Da Cunha; Anand Subhashchandra Gonnagar; Mateus Mendes. 2018. "Product Traceability in Ceramic Industry 4.0: A Design Approach and Cloud-Based MES Prototype." Organizing Smart Buildings and Cities , no. : 187-204.

Journal article
Published: 18 December 2017 in International Journal of Fruit Science
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ACS Style

Raquel Guiné; Susana Matos; Fernando J. Gonçalves; Daniela Costa; Mateus Mendes. Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks. International Journal of Fruit Science 2017, 18, 199 -214.

AMA Style

Raquel Guiné, Susana Matos, Fernando J. Gonçalves, Daniela Costa, Mateus Mendes. Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks. International Journal of Fruit Science. 2017; 18 (2):199-214.

Chicago/Turabian Style

Raquel Guiné; Susana Matos; Fernando J. Gonçalves; Daniela Costa; Mateus Mendes. 2017. "Evaluation of phenolic compounds and antioxidant activity of blueberries and modelization by artificial neural networks." International Journal of Fruit Science 18, no. 2: 199-214.

Original paper
Published: 03 June 2017 in Journal of Food Measurement and Characterization
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The effect of various pre-drying treatments on the quality of dried carrots was evaluated by assessing the values of moisture, ash, protein, fibre, sugars and colour. The pre-drying treatments under investigation were dipping, either in ascorbic acid or sodium metabisulphite at different concentrations and pre-treatment times, as well as blanching. The experimental data was analysed using neural networks, so that relevant patterns in the data were found and conclusions drawn about each variable. The results showed that the type of pre-drying treatment (chemical or physical) had variable impact on the nutritional composition of the dried carrots but not on the colour parameters, which were found to be mostly unaffected by the pre-treatment procedure. Pre-treatment with chemical agents such as ascorbic acid or metabisulphite seem to have the least impact on the parameters studied. The results of the analysis by artificial neural networks confirmed these findings.

ACS Style

Maria João Barroca; Raquel P. F. Guiné; Ana Rita P. Calado; Paula Correia; Mateus Mendes. Artificial neural network modelling of the chemical composition of carrots submitted to different pre-drying treatments. Journal of Food Measurement and Characterization 2017, 11, 1815 -1826.

AMA Style

Maria João Barroca, Raquel P. F. Guiné, Ana Rita P. Calado, Paula Correia, Mateus Mendes. Artificial neural network modelling of the chemical composition of carrots submitted to different pre-drying treatments. Journal of Food Measurement and Characterization. 2017; 11 (4):1815-1826.

Chicago/Turabian Style

Maria João Barroca; Raquel P. F. Guiné; Ana Rita P. Calado; Paula Correia; Mateus Mendes. 2017. "Artificial neural network modelling of the chemical composition of carrots submitted to different pre-drying treatments." Journal of Food Measurement and Characterization 11, no. 4: 1815-1826.

Journal article
Published: 04 February 2015 in Food and Bioprocess Technology
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The present work assessed the influence of different factors on some physical and chemical properties of nuts. The factors evaluated were the presence or absence of the inner skin, geographical origin, storage conditions (ambient temperature, in a stove at 30 and 50 °C, in a chamber at 30 and 50 °C and 90 % RH, refrigerated and freezing) and type of package (none, low density polyethylene and low density polyethylene). The fruits studied were almonds, hazelnuts and walnuts from different countries. The properties measured were moisture content, water activity, colour coordinates (L*, a* and b*) and texture parameters (hardness and friability). Experimental data were modelled using neural networks. The results showed that the almonds from Spain and Romania had aw greater than 0.6, and therefore, its stability was not guaranteed, contrarily to the other samples that presented values of aw lower than 0.6. The colour coordinate lightness varied from 40.60 to 49.30 in the fresh samples but decreased during storage, indicating darkening. In general, an increase in hardness and friability was observed with the different storage conditions. Neuron weight analysis has shown that the origin was a good predictor for moisture content and texture; whereas, the storage condition was a good predictor for aw and colour. In conclusion, it was possible to verify that the properties of nuts are very different depending on origin; they are better preserved at lower temperatures and the type of package used did not impact the properties studied.

ACS Style

Raquel P. F. Guiné; Cátia F. F. Almeida; Paula Correia; Mateus Mendes. Modelling the Influence of Origin, Packing and Storage on Water Activity, Colour and Texture of Almonds, Hazelnuts and Walnuts Using Artificial Neural Networks. Food and Bioprocess Technology 2015, 8, 1113 -1125.

AMA Style

Raquel P. F. Guiné, Cátia F. F. Almeida, Paula Correia, Mateus Mendes. Modelling the Influence of Origin, Packing and Storage on Water Activity, Colour and Texture of Almonds, Hazelnuts and Walnuts Using Artificial Neural Networks. Food and Bioprocess Technology. 2015; 8 (5):1113-1125.

Chicago/Turabian Style

Raquel P. F. Guiné; Cátia F. F. Almeida; Paula Correia; Mateus Mendes. 2015. "Modelling the Influence of Origin, Packing and Storage on Water Activity, Colour and Texture of Almonds, Hazelnuts and Walnuts Using Artificial Neural Networks." Food and Bioprocess Technology 8, no. 5: 1113-1125.

Journal article
Published: 01 February 2015 in Food Chemistry
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Bananas (cv. Musa nana and Musa cavendishii) fresh and dried by hot air at 50 and 70 °C and lyophilisation were analysed for phenolic contents and antioxidant activity. All samples were subject to six extractions (three with methanol followed by three with acetone/water solution). The experimental data served to train a neural network adequate to describe the experimental observations for both output variables studied: total phenols and antioxidant activity. The results show that both bananas are similar and air drying decreased total phenols and antioxidant activity for both temperatures, whereas lyophilisation decreased the phenolic content in a lesser extent. Neural network experiments showed that antioxidant activity and phenolic compounds can be predicted accurately from the input variables: banana variety, dryness state and type and order of extract. Drying state and extract order were found to have larger impact in the values of antioxidant activity and phenolic compounds

ACS Style

Raquel P.F. Guiné; Maria João Barroca; Fernando J. Gonçalves; Mariana Alves; Solange Oliveira; Mateus Mendes. Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 2015, 168, 454 -459.

AMA Style

Raquel P.F. Guiné, Maria João Barroca, Fernando J. Gonçalves, Mariana Alves, Solange Oliveira, Mateus Mendes. Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry. 2015; 168 ():454-459.

Chicago/Turabian Style

Raquel P.F. Guiné; Maria João Barroca; Fernando J. Gonçalves; Mariana Alves; Solange Oliveira; Mateus Mendes. 2015. "Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments." Food Chemistry 168, no. : 454-459.

Journal article
Published: 17 May 2014 in International Journal of Food Engineering
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In the present work, the effect of drying was evaluated on some chemical and physical properties of apples, and the functions were modelled using feed-forward artificial neural networks. The drying kinetics and the mass transfer properties were also studied. The results indicated that acidity and sugars were significantly reduced by drying. Regarding colour lightness decreases, whereas redness and yellowness increased. As for texture, the dried samples were softer and less cohesive as compared to the fresh ones. Mass diffusivity increased with temperature, from 4.4×10−10 m2/s at 30°C to 1.4×10−9 m2/s at 60°C, and so did the mass transfer coefficient, increasing from 3.7×10−10 m/s at 30°C to 7.4×10−9 m/s at 60°C. As to the activation energy, it was found to be 34 kJ/mol. Neural network modelling showed that all properties can be correctly predicted by feed-forward neural networks. The analysis of the networks’ behaviours input layer weight values also shows which properties are more affected by dehydration or more dependent on variety.

ACS Style

Raquel P. F. Guiné; Ana C. Cruz; M. Mendes. Convective Drying of Apples: Kinetic Study, Evaluation of Mass Transfer Properties and Data Analysis using Artificial Neural Networks. International Journal of Food Engineering 2014, 10, 281 -299.

AMA Style

Raquel P. F. Guiné, Ana C. Cruz, M. Mendes. Convective Drying of Apples: Kinetic Study, Evaluation of Mass Transfer Properties and Data Analysis using Artificial Neural Networks. International Journal of Food Engineering. 2014; 10 (2):281-299.

Chicago/Turabian Style

Raquel P. F. Guiné; Ana C. Cruz; M. Mendes. 2014. "Convective Drying of Apples: Kinetic Study, Evaluation of Mass Transfer Properties and Data Analysis using Artificial Neural Networks." International Journal of Food Engineering 10, no. 2: 281-299.

Book chapter
Published: 27 April 2014 in Transactions on Engineering Technologies
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The Sparse Distributed Memory (SDM) has been studied for decades as a theoretical model of an associative memory in many aspects similar to the human brain. It has been tested for different purposes. The present work describes its use as a quick text classifier, based on pattern similarity only. The results found with different datasets were superior to the performance of the dumb classifier or purely random choice, even without text preprocessing. Experiments were performed with a popular Reuters newsgroups dataset and also for real time web ad serving.

ACS Style

Mateus Mendes; A. Paulo Coimbra; Manuel M. Crisóstomo; Jorge Rodrigues. Experiments with a Sparse Distributed Memory for Text Classification. Transactions on Engineering Technologies 2014, 555 -568.

AMA Style

Mateus Mendes, A. Paulo Coimbra, Manuel M. Crisóstomo, Jorge Rodrigues. Experiments with a Sparse Distributed Memory for Text Classification. Transactions on Engineering Technologies. 2014; ():555-568.

Chicago/Turabian Style

Mateus Mendes; A. Paulo Coimbra; Manuel M. Crisóstomo; Jorge Rodrigues. 2014. "Experiments with a Sparse Distributed Memory for Text Classification." Transactions on Engineering Technologies , no. : 555-568.

Journal article
Published: 26 July 2011 in Robotica
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SUMMARYRobot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.

ACS Style

Mateus Mendes; António Paulo Coimbra; Manuel M. Crisóstomo. Robot navigation based on view sequences stored in a sparse distributed memory. Robotica 2011, 30, 571 -581.

AMA Style

Mateus Mendes, António Paulo Coimbra, Manuel M. Crisóstomo. Robot navigation based on view sequences stored in a sparse distributed memory. Robotica. 2011; 30 (4):571-581.

Chicago/Turabian Style

Mateus Mendes; António Paulo Coimbra; Manuel M. Crisóstomo. 2011. "Robot navigation based on view sequences stored in a sparse distributed memory." Robotica 30, no. 4: 571-581.

Book chapter
Published: 07 June 2011 in Lecture Notes in Electrical Engineering
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Navigation based on visual memories is very common among humans. However, planning long trips requires a more sophisticated representation of the environment, such as a topological map, where connections between paths are easily noted. The present approach is a system that learns paths by storing sequences of images and image information in a sparse distributed memory (SDM). Connections between paths are detected by exploring similarities in the images, using the same SDM, and a topological representation of the paths is created. The robot is then able to plan paths and switch from one path to another at the connection points. The system was tested under reconstitutions of country and urban environments, and it was able to successfully map, plan paths and navigate autonomously.

ACS Style

Mateus Mendes; António Paulo Coimbra; Manuel M. Crisóstomo. Topological Mapping Using Vision and a Sparse Distributed Memory. Lecture Notes in Electrical Engineering 2011, 273 -284.

AMA Style

Mateus Mendes, António Paulo Coimbra, Manuel M. Crisóstomo. Topological Mapping Using Vision and a Sparse Distributed Memory. Lecture Notes in Electrical Engineering. 2011; ():273-284.

Chicago/Turabian Style

Mateus Mendes; António Paulo Coimbra; Manuel M. Crisóstomo. 2011. "Topological Mapping Using Vision and a Sparse Distributed Memory." Lecture Notes in Electrical Engineering , no. : 273-284.

Book chapter
Published: 24 February 2010 in Lecture Notes in Electrical Engineering
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A Sparse Distributed Memory (SDM) is a kind of associative memory suitable to work with high-dimensional vectors of random data. This memory model exhibits the characteristics of a large boolean space, which are to a great extent those of the human long-term memory. Hence, this model is attractive for Robotics and Artificial Intelligence, since it can possibly grant artificial machines those same characteristics. However, the original SDM model is appropriate to work with random data. Sensorial data is not always random: most of the times it is based on the Natural Binary Code and tends to cluster around some specific points. This means that the SDM performs poorer than expected. As part of an ongoing project, in which the goal is to navigate a robot using a SDM to store and retrieve sequences of images and associated path information, different methods of encoding the data were tested. Some methods perform better than others, and one method is presented that can offer the best performance and still maintain the characteristics of the original model.

ACS Style

Mateus Mendes; Manuel M. Crisóstomo; António Paulo Coimbra. Encoding Data to Use with a Sparse Distributed Memory. Lecture Notes in Electrical Engineering 2010, 285 -295.

AMA Style

Mateus Mendes, Manuel M. Crisóstomo, António Paulo Coimbra. Encoding Data to Use with a Sparse Distributed Memory. Lecture Notes in Electrical Engineering. 2010; ():285-295.

Chicago/Turabian Style

Mateus Mendes; Manuel M. Crisóstomo; António Paulo Coimbra. 2010. "Encoding Data to Use with a Sparse Distributed Memory." Lecture Notes in Electrical Engineering , no. : 285-295.