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Jonas Krause

PUC PR, PPGIA, Post-Doc

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Professor of Mathematics and Computer Science, specialized in machine learning algorithms and artificial intelligence. I have extensive academic experience in developing and applying advanced techniques in computer vision and time series forecasting. I possess a solid background in optimization, heuristic methods, evolutionary computation, mathematical programming, and other computational analysis approaches. My work combines theoretical teaching with the practical implementation of computational solutions. In addition to classroom activities, I actively participate in research projects focused on developing predictive models and deep learning. My fluency in English and ability to work in a team complement my expertise, allowing me to contribute significantly in academic and research environments.

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Harvard University

School of the Art Institute of Chicago

Duke University

Kansas State University

Armando Marques-Guedes

UNL - New University of Lisbon

Oxford Brookes University

University of Technology Sydney

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RIVERS STATE POLYTECHNIC

Punjab Technical University

Musabe Jean Bosco

Chongqing University of Posts and Telecommunications

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Papers by Jonas Krause

Further analysis of aluminum alloying for the formation of p+ regions in silicon solar cells

Energy Procedia, 2011

We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emi... more We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emitters and back surface fields for n-and p-type silicon solar cells, respectively. We have investigated the structural properties of agglomerated Al-doped p + Si regions which exhibit serious lateral thickness inhomogeneities. We refined an existing simple model based on wetting phenomena for the formation of agglomerated Al-p + regions. According to the model, the thickness inhomogeneities are caused by the contraction of the Al-Si melt during alloying, leading to enhanced and decelerated Si recrystallization in the regions of melt accumulation and depletion, respectively. Furthermore, we demonstrate that the melt contraction can be delayed by carefully choosing adequate alloying conditions. In particular, agglomerations are avoided by increasing the printed Al paste amount or decreasing the peak temperature time or peak temperature, thus enabling a more flexible fabrication of Si solar cells with Al-alloyed rear side.

Highly efficient all-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter

Energy Procedia, 2011

All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter o... more All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter on n-type base material were fabricated and analyzed at Fraunhofer ISE PV-TEC. Three different process sequences are compared to each other. One process flow with shallow phosphorus profiles realizes cells with efficiencies of 19.7%. Using a long thermal oxidation, cells with deep driven-in phosphorus profiles (front and back surface field) were processed where solar cell efficiencies of 20.0% are realized. Both cell efficiencies are independently measured at Fraunhofer ISE CalLab on a designated area of 16.65 cm 2 . The specific contact resistivity of screen-printed and fired silver paste to silicon surfaces with different phosphorus profiles was determined in dependence of several firing conditions. The main developed features of the presented cell structure comprise the firing-stable front and rear passivation layers, the spiking-free passivation layer against aluminum and the deep driven-in phosphorus profile that can be contacted by silver screen-printing paste in a firing step.

$Research paper thumbnail of Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements$

Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements

IEEE Transactions on Electron Devices, Feb 1, 2011

AbstractSurface-passivated and surface-unpassivated aluminum-alloyed p+-layers are characterized... more

Highly efficient all-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter

Energy Procedia, 2011

All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter o... more All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter on n-type base material were fabricated and analyzed at Fraunhofer ISE PV-TEC. Three different process sequences are compared to each other. One process flow with shallow phosphorus profiles realizes cells with efficiencies of 19.7%. Using a long thermal oxidation, cells with deep driven-in phosphorus profiles (front and back surface field) were processed where solar cell efficiencies of 20.0% are realized. Both cell efficiencies are independently measured at Fraunhofer ISE CalLab on a designated area of 16.65 cm 2 . The specific contact resistivity of screen-printed and fired silver paste to silicon surfaces with different phosphorus profiles was determined in dependence of several firing conditions. The main developed features of the presented cell structure comprise the firing-stable front and rear passivation layers, the spiking-free passivation layer against aluminum and the deep driven-in phosphorus profile that can be contacted by silver screen-printing paste in a firing step.

Further analysis of aluminum alloying for the formation of p+ regions in silicon solar cells

Energy Procedia, 2011

We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emi... more We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emitters and back surface fields for n-and p-type silicon solar cells, respectively. We have investigated the structural properties of agglomerated Al-doped p + Si regions which exhibit serious lateral thickness inhomogeneities. We refined an existing simple model based on wetting phenomena for the formation of agglomerated Al-p + regions. According to the model, the thickness inhomogeneities are caused by the contraction of the Al-Si melt during alloying, leading to enhanced and decelerated Si recrystallization in the regions of melt accumulation and depletion, respectively. Furthermore, we demonstrate that the melt contraction can be delayed by carefully choosing adequate alloying conditions. In particular, agglomerations are avoided by increasing the printed Al paste amount or decreasing the peak temperature time or peak temperature, thus enabling a more flexible fabrication of Si solar cells with Al-alloyed rear side.

$Research paper thumbnail of Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements$

Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements

IEEE Transactions on Electron Devices, 2011

AbstractSurface-passivated and surface-unpassivated aluminum-alloyed p+-layers are characterized... more

19.7% Efficient All-Screen-Printed Back-Contact Back-Junction Silicon Solar Cell With Aluminum-Alloyed Emitter

IEEE Electron Device Letters, 2011

A back-contact back-junction solar cell on n-type silicon with an aluminum-alloyed emitter is int... more A back-contact back-junction solar cell on n-type silicon with an aluminum-alloyed emitter is introduced, where both structuring and metallization are realized by screen-printing. The process sequence for realizing the cell with a pitch of 2 mm is displayed and described. The cell parameters are shown for three different emitter coverages on the rear side (45%, 58%, and 72%). An analysis of the saturation current density J 0 of the different areas was carried out on lifetime samples, and the calculated V oc is compared to the measured one. The potentially critical edge of the Al finger is analyzed in a scanning electron microscopy cross section. No damaging of the aluminum paste to the passivation layer and a homogenous p + -layer over the whole finger width is observed. A conversion efficiency of 19.7% is presented for a cell with an aperture area of 16.65 cm 2 that was exclusively processed in Fraunhofer ISE PV-TEC, which is an industrial-like fabrication environment.

Motional resonances of three-dimensional dual-species Coulomb crystals

Journal of Physics B: Atomic, Molecular and Optical Physics, 2018

We investigate the motional resonances of dual-species Coulomb crystals comprised of 9 Be + and 2... more We investigate the motional resonances of dual-species Coulomb crystals comprised of 9 Be + and 24 Mg + ions held in a 4-rod linear Paul trap. Our experimental data and simulations show that the secular motion of such mixed crystals has rich dynamics. Their secular spectra can differ significantly from those of pure ion crystals. We propose a simple model based on mechanical coupling with Coulomb interactions between the two different ion species that explains many features of the secular spectrum. Our findings contribute to a more reliable identification of the ion species in mixed crystals.

Observation of Josephson Harmonics in Tunnel Junctions

arXiv (Cornell University), Feb 17, 2023

Studying magnetic-field resilience of 3D transmons with thin-film AlOx Josephson junctions

Bulletin of the American Physical Society, Mar 16, 2021

Modelagem e tecnologia no ensino da matemática: uso da transparência a partir de dados da administração pública

RESUMO Este artigo aplica a ideia da modelagem no ensino da Matematica para o EJA e para o ensino... more RESUMO Este artigo aplica a ideia da modelagem no ensino da Matematica para o EJA e para o ensino Fundamental, assim como tambem o uso da tecnologia de apoio e a oportunidade de utilizar o material oferecido pelos orgaos publicos na denominacao de transparencia, no objetivo deste aprendizado. Neste caso, o educando utilizara as operacoes basicas do calculo matematico, a soma ou adicao, a subtracao, a multiplicacao e a divisao, influenciado positivamente pela construcao de tabelas, graficos, quadros, etc. Podera comparar e analisar dados em diferentes regioes do pais e disponibilizar suas conclusoes e criticas sobre os temas estudados no formato de apresentacoes e/ou relatorios. Para isso, e papel do educador a funcao de identificar as diferentes etapas de organizacao e implementacao de um projeto de modelagem matematico, com base na Resolucao de Problemas. Palavras-chave : Ensino aprendizagem. Modelagem matematica. Projetos de modelagem. Ensino fundamental.

Efficacy and Safety of [225Ac]Ac-PSMA-617 Augmented [177Lu]Lu-PSMA-617 Radioligand Therapy in Patients with Highly Advanced mCRPC with Poor Prognosis

Pharmaceutics, 2021

The use of 225Ac in prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT),... more The use of 225Ac in prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT), either as monotherapy or in combination with 177Lu, is a promising therapy approach in patients with metastatic castration-resistant prostate carcinoma (mCRPC). In this study, we report the efficacy and safety of [225Ac]Ac-PSMA-617 augmented [177Lu]Lu-PSMA-617 RLT in 177Lu-naive mCRPC patients (n = 15) with poor prognosis (presence of visceral metastases, high total tumor burden with diffuse bone metastases or a short PSA doubling time of <2 months). Biochemical (by PSA serum value) and molecular imaging response (by [68Ga]Ga-PSMA-11 PET/CT) was assessed after two cycles of [177Lu]Lu-PSMA-617 RLT, with at least one [225Ac]Ac-PSMA-617 augmentation. In addition, PSA-based progression-free survival (PSA-PFS), overall survival (OS) and toxicity (according to CTCAE) were analyzed. We observed a biochemical- and molecular imaging-based partial remission in 53.3% (8/15) and 66.7% (10/15) of...

Language-focused Deepfake Detection Using Phonemes, Mouth Movements, and Video Features

The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in g... more The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in generating highly realistic deepfake videos have raised concerns regarding the reliability of our human senses. In response to this challenge, we propose a deepfake detection system based on phonemes, the transcribed text, associated mouth movements, and videoextracted features. As a proof-of-concept, we develop a deepfake detection system specifically designed for the Portuguese language, employing three presidential candidates from the 2022 Brazilian elections. Additionally, we introduce a unique dataset comprising real and fake videos involving these three individuals and deliberately blending their identities. The extracted features consolidate relevant attributes, which we utilized to train multiple classification algorithms. Notably, our computational models demonstrate satisfactory performance when authenticating or detecting fake videos containing at least one of the trained phonemes from the Portuguese language. Hence, we conclude that deepfake detection is feasible, primarily due to the absence of natural expressions, particularly in non-English language deepfake videos. Furthermore, developing individual-guided deepfake detection systems may facilitate the authentication of videos featuring celebrities or politicians during future online events.

Language-focused Deepfake Detection Using Phonemes, Mouth Movements, and Video Features

9th IEEE Latin American Conference on Computational Intelligence (IEEE LA-CCI 2023), 2023

The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in g... more The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in generating highly realistic deepfake videos have raised concerns regarding the reliability of our human senses. In response to this challenge, we propose a deepfake detection system based on phonemes, the transcribed text, associated mouth movements, and videoextracted features. As a proof-of-concept, we develop a deepfake detection system specifically designed for the Portuguese language, employing three presidential candidates from the 2022 Brazilian elections. Additionally, we introduce a unique dataset comprising real and fake videos involving these three individuals and deliberately blending their identities. The extracted features consolidate relevant attributes, which we utilized to train multiple classification algorithms. Notably, our computational models demonstrate satisfactory performance when authenticating or detecting fake videos containing at least one of the trained phonemes from the Portuguese language. Hence, we conclude that deepfake detection is feasible, primarily due to the absence of natural expressions, particularly in non-English language deepfake videos. Furthermore, developing individual-guided deepfake detection systems may facilitate the authentication of videos featuring celebrities or politicians during future online events.

A PHONEME-BASED DEEP FAKE DETECTION SYSTEM

International Journal of Advances in Science, Engineering and Technology (IJASEAT), 2023

What happens if we can no longer trust our senses? There is a general concern that Artificial Int... more What happens if we can no longer trust our senses? There is a general concern that Artificial Intelligence (AI) and Machine Learning (ML) algorithms could soon create extraordinarily realistic fake videos that trick our eyes and ears. Thinking on a counter-response, we propose a new deepfake detection system based on phonemes, their transcribed text, the associated mouth movements, and video-extracted features. As a proof-of-concept, we create a Brazilian Portuguese deepfake detection system using three presidential candidates of the 2022 elections and one of the authors. We also present a novel dataset of authentic and fake videos from these four individuals mixing their identities, which we used to extract features and feed our classification methods. Our classification methods achieved satisfactory results when authenticating (or not) testing videos that contain at least one of the trained phonemes from the Brazilian Portuguese language. In conclusion, we support the hypothesis that deepfake detection is possible due to the lack of expression in the target's mouth, especially in non-English language fake videos. And developing a deepfake detection system with individual-guided classification models may help authenticate videos of celebrities or politicians in future and online events.

CNN-Based Plant Species Categorization Using Natural Images

Automatic identification of plants from natural images is a challenging problem that is relevant ... more Automatic identification of plants from natural images is a challenging problem that is relevant to both the disciplines of Botany and Computer Science. The classification of plant images at the species level is a computer vision task called fine-grained categorization. This categorization problem is particularly complicated due to a large number of plant species, the inter-species similarity, the large-scale variation in appearance, and the lack of annotated data. Despite the availability of dozens of plant identification mobile applications, categorizing plant species from natural images remains an unsolved problem-e.g., most of the existing applications do not address the multiscale nature of this type of image. Furthermore, an automated system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in preserving ecosystem biodiversity and public education but also in numerous agricultural activities such as detecting abnormalities in plants and analyzing food crops. In this dissertation, I present a new approach to the problem of automatically categorizing plant species using photos taken in nature. Essentially, this approach assembles a collection of Convolutional Neural Networks (CNN-based) to create a plant categorization system that I named WTPlant (What's That Plant?). One of the novelties of this system is a preprocessing method that extracts multi-scale samples from natural images, making the classification models more robust to variations in the scale of the plant. A comprehensive experimental evaluation of this new preprocessing method compares its performance with frequently used data augmentation techniques over different classification models of the system. WTPlant also enables the categorization of multiple plant components simultaneously by employing distinct classification pipelines for plants (leaves, branches, bushes, and trees) and flowers. The combination of these multi-organ analyses ensures a broader categorization process. It can be further extended by adding pipelines for fruits, barks, roots, etc., depending on the availability of annotated images. In summary, this new approach locates multiple plant organs in a natural image and guides the extraction of representative samples at various scales used to train and test state-of-the-art CNN classification models. To apply the WTPlant system in a real-world environment, I implement a scale-up process that adapts the classification models. In this process, models have their top classification layers replaced to accommodate a more significant number of plant species. But due to a lack of training data, these models have to be pre-trained to achieve satisfactory performance. As a result, I also implement the integration of domain-specific knowledge to create plant and flower expert classification models. Initially focusing on the University of Hawai'i Mānoa campus plants, this research aims to produce the most accurate system for classifying Hawaiian plants and make it available to botanists, tourists, and the entire community to use. As a case study, I create a mobile version of the WTPlant system to categorize plant species from the Harold L. Lyon Arboretum, a University of Hawai'i Research Unit located at the upper end of the Mānoa Valley.

Expanding CNN-Based Plant Phenotyping Systems to Larger Environments

Plant phenotyping systems strive to maintain high categorization accuracy when expanding their sc... more Plant phenotyping systems strive to maintain high categorization accuracy when expanding their scopes to larger environments. In this paper, we discuss problems associated with expanding the plant categorization scope. These problems are particularly complicated due to the increase in the number of species and the inter-species similarity. In our approach, we modify previously trained Convolutional Neural Networks (CNNs) and integrate domain-specific knowledge in the fine-tuning process of these models to maintain high accuracy while expanding the scope. This process is the key idea behind our CNN-based expanding approach resulting in plant-expert models. Experiments described in this paper compare the accuracy of an expanded phenotyping system using different plant-related datasets during the training of the CNN categorization models. Although it takes much longer to train these models, our approach achieves better performance compared to models trained without the integration of d...

What's That Plant? WTPlant is a Deep Learning System to Identify Plants in Natural Images

Automatic identification of plant species from natural images is a challenging problem with many ... more Automatic identification of plant species from natural images is a challenging problem with many practical applications in multiple disciplines. An accurate and automated system for plant species identification has important implications in addressing botanical taxonomy gaps, identifying new species, controlling the balance of ecosystems, and estimating yield and resource requirements in agriculture. However, identifying plants from uncontrolled natural images is a challenging problem due to the complexity of natural images, a large number of plant species, inter-species similarity, and the large-scale variance in appearance. In this work, we present a system called WTPlant, specifically designed for identifying plants in natural images. By assembling a collection of Convolutional Neural Networks with stacked/residual blocks and a preprocessing stage for multiscale analysis, WTPlant presents itself as a highly discriminative deep learning approach for this image classification problem.

Programação matemática e evolução diferencial para a otimização de redes de dutos

A otimizacao de uma rede de transporte de derivados de petroleo e um problema complexo e abordado... more A otimizacao de uma rede de transporte de derivados de petroleo e um problema complexo e abordado na literatura atual. A modelagem matematica deste problema proposta neste trabalho cria um problema de otimizacao combinatorial. Metodos de resolucao deste problema atraves da programacao linear inteira mista e de algoritmos heuristicos de evolucao diferencial (Evolucao Diferencial Binaria e Evolucao Diferencial Discretizada) sao propostos utilizando variaveis binarias. Os resultados encontrados com a programacao linear apresentam valores otimos para os benchmarks com pequenos espacos de busca e valores sub-otimos para grandes. Resultados utilizando a evolucao diferencial tambem sao apresentados como uma alternativa de baixo esforco computacional. A aplicacao destes metodos proporciona alternativas para o transporte de diferentes produtos em um horizonte de tempo definido e compara os metodos heuristicos com codificacoes binarias e continuas. Tais resultados incentivam a utilizacao de a...

WTPlant (What's That Plant?)

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018

Despite the availability of dozens of plant identification mobile applications, identifying plant... more Despite the availability of dozens of plant identification mobile applications, identifying plants from a natural image remains a challenging problem - most of the existing applications do not address the complexity of natural images, the large number of plant species, and the multi-scale nature of natural images. In this technical demonstration, we present the WTPlant system for identifying plants in natural images. WTPlant is based on deep learning approaches. Specifically, it uses stacked Convolutional Neural Networks for image segmentation, a novel preprocessing stage for multi-scale analyses, and deep convolutional networks to extract the most discriminative features. WTPlant employs different classification architectures for plants and flowers, thus enabling plant identification throughout all the seasons. The user interface also shows, in an interactive way, the most representative areas in the image that are used to predict each plant species. The first version of WTPlant is trained to classify 100 different plant species present in the campus of the University of Hawai'i at Manoa. First experiments support the hypothesis that an initial segmentation process helps guide the extraction of representative samples and, consequently, enables Convolutional Neural Networks to better recognize objects of different scales in natural images. Future versions aim to extend the recognizable species to cover the land-based flora of the Hawaiian Islands.

Further analysis of aluminum alloying for the formation of p+ regions in silicon solar cells

Energy Procedia, 2011

We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emi... more We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emitters and back surface fields for n-and p-type silicon solar cells, respectively. We have investigated the structural properties of agglomerated Al-doped p + Si regions which exhibit serious lateral thickness inhomogeneities. We refined an existing simple model based on wetting phenomena for the formation of agglomerated Al-p + regions. According to the model, the thickness inhomogeneities are caused by the contraction of the Al-Si melt during alloying, leading to enhanced and decelerated Si recrystallization in the regions of melt accumulation and depletion, respectively. Furthermore, we demonstrate that the melt contraction can be delayed by carefully choosing adequate alloying conditions. In particular, agglomerations are avoided by increasing the printed Al paste amount or decreasing the peak temperature time or peak temperature, thus enabling a more flexible fabrication of Si solar cells with Al-alloyed rear side.

Highly efficient all-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter

Energy Procedia, 2011

All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter o... more All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter on n-type base material were fabricated and analyzed at Fraunhofer ISE PV-TEC. Three different process sequences are compared to each other. One process flow with shallow phosphorus profiles realizes cells with efficiencies of 19.7%. Using a long thermal oxidation, cells with deep driven-in phosphorus profiles (front and back surface field) were processed where solar cell efficiencies of 20.0% are realized. Both cell efficiencies are independently measured at Fraunhofer ISE CalLab on a designated area of 16.65 cm 2 . The specific contact resistivity of screen-printed and fired silver paste to silicon surfaces with different phosphorus profiles was determined in dependence of several firing conditions. The main developed features of the presented cell structure comprise the firing-stable front and rear passivation layers, the spiking-free passivation layer against aluminum and the deep driven-in phosphorus profile that can be contacted by silver screen-printing paste in a firing step.

$Research paper thumbnail of Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements$

Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements

IEEE Transactions on Electron Devices, Feb 1, 2011

AbstractSurface-passivated and surface-unpassivated aluminum-alloyed p+-layers are characterized... more

Highly efficient all-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter

Energy Procedia, 2011

All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter o... more All-screen-printed back-contact back-junction silicon solar cells with aluminum-alloyed emitter on n-type base material were fabricated and analyzed at Fraunhofer ISE PV-TEC. Three different process sequences are compared to each other. One process flow with shallow phosphorus profiles realizes cells with efficiencies of 19.7%. Using a long thermal oxidation, cells with deep driven-in phosphorus profiles (front and back surface field) were processed where solar cell efficiencies of 20.0% are realized. Both cell efficiencies are independently measured at Fraunhofer ISE CalLab on a designated area of 16.65 cm 2 . The specific contact resistivity of screen-printed and fired silver paste to silicon surfaces with different phosphorus profiles was determined in dependence of several firing conditions. The main developed features of the presented cell structure comprise the firing-stable front and rear passivation layers, the spiking-free passivation layer against aluminum and the deep driven-in phosphorus profile that can be contacted by silver screen-printing paste in a firing step.

Further analysis of aluminum alloying for the formation of p+ regions in silicon solar cells

Energy Procedia, 2011

We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emi... more We present a detailed study on the formation of full-area screen-printed aluminum-alloyed p + emitters and back surface fields for n-and p-type silicon solar cells, respectively. We have investigated the structural properties of agglomerated Al-doped p + Si regions which exhibit serious lateral thickness inhomogeneities. We refined an existing simple model based on wetting phenomena for the formation of agglomerated Al-p + regions. According to the model, the thickness inhomogeneities are caused by the contraction of the Al-Si melt during alloying, leading to enhanced and decelerated Si recrystallization in the regions of melt accumulation and depletion, respectively. Furthermore, we demonstrate that the melt contraction can be delayed by carefully choosing adequate alloying conditions. In particular, agglomerations are avoided by increasing the printed Al paste amount or decreasing the peak temperature time or peak temperature, thus enabling a more flexible fabrication of Si solar cells with Al-alloyed rear side.

$Research paper thumbnail of Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements$

Evaluating the Aluminum-Alloyed $\hbox{p}^{+}$-Layer of Silicon Solar Cells by Emitter Saturation Current Density and Optical Microspectroscopy Measurements

IEEE Transactions on Electron Devices, 2011

AbstractSurface-passivated and surface-unpassivated aluminum-alloyed p+-layers are characterized... more

19.7% Efficient All-Screen-Printed Back-Contact Back-Junction Silicon Solar Cell With Aluminum-Alloyed Emitter

IEEE Electron Device Letters, 2011

A back-contact back-junction solar cell on n-type silicon with an aluminum-alloyed emitter is int... more A back-contact back-junction solar cell on n-type silicon with an aluminum-alloyed emitter is introduced, where both structuring and metallization are realized by screen-printing. The process sequence for realizing the cell with a pitch of 2 mm is displayed and described. The cell parameters are shown for three different emitter coverages on the rear side (45%, 58%, and 72%). An analysis of the saturation current density J 0 of the different areas was carried out on lifetime samples, and the calculated V oc is compared to the measured one. The potentially critical edge of the Al finger is analyzed in a scanning electron microscopy cross section. No damaging of the aluminum paste to the passivation layer and a homogenous p + -layer over the whole finger width is observed. A conversion efficiency of 19.7% is presented for a cell with an aperture area of 16.65 cm 2 that was exclusively processed in Fraunhofer ISE PV-TEC, which is an industrial-like fabrication environment.

Motional resonances of three-dimensional dual-species Coulomb crystals

Journal of Physics B: Atomic, Molecular and Optical Physics, 2018

We investigate the motional resonances of dual-species Coulomb crystals comprised of 9 Be + and 2... more We investigate the motional resonances of dual-species Coulomb crystals comprised of 9 Be + and 24 Mg + ions held in a 4-rod linear Paul trap. Our experimental data and simulations show that the secular motion of such mixed crystals has rich dynamics. Their secular spectra can differ significantly from those of pure ion crystals. We propose a simple model based on mechanical coupling with Coulomb interactions between the two different ion species that explains many features of the secular spectrum. Our findings contribute to a more reliable identification of the ion species in mixed crystals.

Observation of Josephson Harmonics in Tunnel Junctions

arXiv (Cornell University), Feb 17, 2023

Studying magnetic-field resilience of 3D transmons with thin-film AlOx Josephson junctions

Bulletin of the American Physical Society, Mar 16, 2021

Modelagem e tecnologia no ensino da matemática: uso da transparência a partir de dados da administração pública

RESUMO Este artigo aplica a ideia da modelagem no ensino da Matematica para o EJA e para o ensino... more RESUMO Este artigo aplica a ideia da modelagem no ensino da Matematica para o EJA e para o ensino Fundamental, assim como tambem o uso da tecnologia de apoio e a oportunidade de utilizar o material oferecido pelos orgaos publicos na denominacao de transparencia, no objetivo deste aprendizado. Neste caso, o educando utilizara as operacoes basicas do calculo matematico, a soma ou adicao, a subtracao, a multiplicacao e a divisao, influenciado positivamente pela construcao de tabelas, graficos, quadros, etc. Podera comparar e analisar dados em diferentes regioes do pais e disponibilizar suas conclusoes e criticas sobre os temas estudados no formato de apresentacoes e/ou relatorios. Para isso, e papel do educador a funcao de identificar as diferentes etapas de organizacao e implementacao de um projeto de modelagem matematico, com base na Resolucao de Problemas. Palavras-chave : Ensino aprendizagem. Modelagem matematica. Projetos de modelagem. Ensino fundamental.

Efficacy and Safety of [225Ac]Ac-PSMA-617 Augmented [177Lu]Lu-PSMA-617 Radioligand Therapy in Patients with Highly Advanced mCRPC with Poor Prognosis

Pharmaceutics, 2021

The use of 225Ac in prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT),... more The use of 225Ac in prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT), either as monotherapy or in combination with 177Lu, is a promising therapy approach in patients with metastatic castration-resistant prostate carcinoma (mCRPC). In this study, we report the efficacy and safety of [225Ac]Ac-PSMA-617 augmented [177Lu]Lu-PSMA-617 RLT in 177Lu-naive mCRPC patients (n = 15) with poor prognosis (presence of visceral metastases, high total tumor burden with diffuse bone metastases or a short PSA doubling time of <2 months). Biochemical (by PSA serum value) and molecular imaging response (by [68Ga]Ga-PSMA-11 PET/CT) was assessed after two cycles of [177Lu]Lu-PSMA-617 RLT, with at least one [225Ac]Ac-PSMA-617 augmentation. In addition, PSA-based progression-free survival (PSA-PFS), overall survival (OS) and toxicity (according to CTCAE) were analyzed. We observed a biochemical- and molecular imaging-based partial remission in 53.3% (8/15) and 66.7% (10/15) of...

Language-focused Deepfake Detection Using Phonemes, Mouth Movements, and Video Features

The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in g... more The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in generating highly realistic deepfake videos have raised concerns regarding the reliability of our human senses. In response to this challenge, we propose a deepfake detection system based on phonemes, the transcribed text, associated mouth movements, and videoextracted features. As a proof-of-concept, we develop a deepfake detection system specifically designed for the Portuguese language, employing three presidential candidates from the 2022 Brazilian elections. Additionally, we introduce a unique dataset comprising real and fake videos involving these three individuals and deliberately blending their identities. The extracted features consolidate relevant attributes, which we utilized to train multiple classification algorithms. Notably, our computational models demonstrate satisfactory performance when authenticating or detecting fake videos containing at least one of the trained phonemes from the Portuguese language. Hence, we conclude that deepfake detection is feasible, primarily due to the absence of natural expressions, particularly in non-English language deepfake videos. Furthermore, developing individual-guided deepfake detection systems may facilitate the authentication of videos featuring celebrities or politicians during future online events.

Language-focused Deepfake Detection Using Phonemes, Mouth Movements, and Video Features

9th IEEE Latin American Conference on Computational Intelligence (IEEE LA-CCI 2023), 2023

The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in g... more The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in generating highly realistic deepfake videos have raised concerns regarding the reliability of our human senses. In response to this challenge, we propose a deepfake detection system based on phonemes, the transcribed text, associated mouth movements, and videoextracted features. As a proof-of-concept, we develop a deepfake detection system specifically designed for the Portuguese language, employing three presidential candidates from the 2022 Brazilian elections. Additionally, we introduce a unique dataset comprising real and fake videos involving these three individuals and deliberately blending their identities. The extracted features consolidate relevant attributes, which we utilized to train multiple classification algorithms. Notably, our computational models demonstrate satisfactory performance when authenticating or detecting fake videos containing at least one of the trained phonemes from the Portuguese language. Hence, we conclude that deepfake detection is feasible, primarily due to the absence of natural expressions, particularly in non-English language deepfake videos. Furthermore, developing individual-guided deepfake detection systems may facilitate the authentication of videos featuring celebrities or politicians during future online events.

A PHONEME-BASED DEEP FAKE DETECTION SYSTEM

International Journal of Advances in Science, Engineering and Technology (IJASEAT), 2023

What happens if we can no longer trust our senses? There is a general concern that Artificial Int... more What happens if we can no longer trust our senses? There is a general concern that Artificial Intelligence (AI) and Machine Learning (ML) algorithms could soon create extraordinarily realistic fake videos that trick our eyes and ears. Thinking on a counter-response, we propose a new deepfake detection system based on phonemes, their transcribed text, the associated mouth movements, and video-extracted features. As a proof-of-concept, we create a Brazilian Portuguese deepfake detection system using three presidential candidates of the 2022 elections and one of the authors. We also present a novel dataset of authentic and fake videos from these four individuals mixing their identities, which we used to extract features and feed our classification methods. Our classification methods achieved satisfactory results when authenticating (or not) testing videos that contain at least one of the trained phonemes from the Brazilian Portuguese language. In conclusion, we support the hypothesis that deepfake detection is possible due to the lack of expression in the target's mouth, especially in non-English language fake videos. And developing a deepfake detection system with individual-guided classification models may help authenticate videos of celebrities or politicians in future and online events.

CNN-Based Plant Species Categorization Using Natural Images

Automatic identification of plants from natural images is a challenging problem that is relevant ... more Automatic identification of plants from natural images is a challenging problem that is relevant to both the disciplines of Botany and Computer Science. The classification of plant images at the species level is a computer vision task called fine-grained categorization. This categorization problem is particularly complicated due to a large number of plant species, the inter-species similarity, the large-scale variation in appearance, and the lack of annotated data. Despite the availability of dozens of plant identification mobile applications, categorizing plant species from natural images remains an unsolved problem-e.g., most of the existing applications do not address the multiscale nature of this type of image. Furthermore, an automated system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in preserving ecosystem biodiversity and public education but also in numerous agricultural activities such as detecting abnormalities in plants and analyzing food crops. In this dissertation, I present a new approach to the problem of automatically categorizing plant species using photos taken in nature. Essentially, this approach assembles a collection of Convolutional Neural Networks (CNN-based) to create a plant categorization system that I named WTPlant (What's That Plant?). One of the novelties of this system is a preprocessing method that extracts multi-scale samples from natural images, making the classification models more robust to variations in the scale of the plant. A comprehensive experimental evaluation of this new preprocessing method compares its performance with frequently used data augmentation techniques over different classification models of the system. WTPlant also enables the categorization of multiple plant components simultaneously by employing distinct classification pipelines for plants (leaves, branches, bushes, and trees) and flowers. The combination of these multi-organ analyses ensures a broader categorization process. It can be further extended by adding pipelines for fruits, barks, roots, etc., depending on the availability of annotated images. In summary, this new approach locates multiple plant organs in a natural image and guides the extraction of representative samples at various scales used to train and test state-of-the-art CNN classification models. To apply the WTPlant system in a real-world environment, I implement a scale-up process that adapts the classification models. In this process, models have their top classification layers replaced to accommodate a more significant number of plant species. But due to a lack of training data, these models have to be pre-trained to achieve satisfactory performance. As a result, I also implement the integration of domain-specific knowledge to create plant and flower expert classification models. Initially focusing on the University of Hawai'i Mānoa campus plants, this research aims to produce the most accurate system for classifying Hawaiian plants and make it available to botanists, tourists, and the entire community to use. As a case study, I create a mobile version of the WTPlant system to categorize plant species from the Harold L. Lyon Arboretum, a University of Hawai'i Research Unit located at the upper end of the Mānoa Valley.

Expanding CNN-Based Plant Phenotyping Systems to Larger Environments

Plant phenotyping systems strive to maintain high categorization accuracy when expanding their sc... more Plant phenotyping systems strive to maintain high categorization accuracy when expanding their scopes to larger environments. In this paper, we discuss problems associated with expanding the plant categorization scope. These problems are particularly complicated due to the increase in the number of species and the inter-species similarity. In our approach, we modify previously trained Convolutional Neural Networks (CNNs) and integrate domain-specific knowledge in the fine-tuning process of these models to maintain high accuracy while expanding the scope. This process is the key idea behind our CNN-based expanding approach resulting in plant-expert models. Experiments described in this paper compare the accuracy of an expanded phenotyping system using different plant-related datasets during the training of the CNN categorization models. Although it takes much longer to train these models, our approach achieves better performance compared to models trained without the integration of d...

What's That Plant? WTPlant is a Deep Learning System to Identify Plants in Natural Images

Automatic identification of plant species from natural images is a challenging problem with many ... more Automatic identification of plant species from natural images is a challenging problem with many practical applications in multiple disciplines. An accurate and automated system for plant species identification has important implications in addressing botanical taxonomy gaps, identifying new species, controlling the balance of ecosystems, and estimating yield and resource requirements in agriculture. However, identifying plants from uncontrolled natural images is a challenging problem due to the complexity of natural images, a large number of plant species, inter-species similarity, and the large-scale variance in appearance. In this work, we present a system called WTPlant, specifically designed for identifying plants in natural images. By assembling a collection of Convolutional Neural Networks with stacked/residual blocks and a preprocessing stage for multiscale analysis, WTPlant presents itself as a highly discriminative deep learning approach for this image classification problem.

Programação matemática e evolução diferencial para a otimização de redes de dutos

A otimizacao de uma rede de transporte de derivados de petroleo e um problema complexo e abordado... more A otimizacao de uma rede de transporte de derivados de petroleo e um problema complexo e abordado na literatura atual. A modelagem matematica deste problema proposta neste trabalho cria um problema de otimizacao combinatorial. Metodos de resolucao deste problema atraves da programacao linear inteira mista e de algoritmos heuristicos de evolucao diferencial (Evolucao Diferencial Binaria e Evolucao Diferencial Discretizada) sao propostos utilizando variaveis binarias. Os resultados encontrados com a programacao linear apresentam valores otimos para os benchmarks com pequenos espacos de busca e valores sub-otimos para grandes. Resultados utilizando a evolucao diferencial tambem sao apresentados como uma alternativa de baixo esforco computacional. A aplicacao destes metodos proporciona alternativas para o transporte de diferentes produtos em um horizonte de tempo definido e compara os metodos heuristicos com codificacoes binarias e continuas. Tais resultados incentivam a utilizacao de a...

WTPlant (What's That Plant?)

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018

Despite the availability of dozens of plant identification mobile applications, identifying plant... more Despite the availability of dozens of plant identification mobile applications, identifying plants from a natural image remains a challenging problem - most of the existing applications do not address the complexity of natural images, the large number of plant species, and the multi-scale nature of natural images. In this technical demonstration, we present the WTPlant system for identifying plants in natural images. WTPlant is based on deep learning approaches. Specifically, it uses stacked Convolutional Neural Networks for image segmentation, a novel preprocessing stage for multi-scale analyses, and deep convolutional networks to extract the most discriminative features. WTPlant employs different classification architectures for plants and flowers, thus enabling plant identification throughout all the seasons. The user interface also shows, in an interactive way, the most representative areas in the image that are used to predict each plant species. The first version of WTPlant is trained to classify 100 different plant species present in the campus of the University of Hawai'i at Manoa. First experiments support the hypothesis that an initial segmentation process helps guide the extraction of representative samples and, consequently, enables Convolutional Neural Networks to better recognize objects of different scales in natural images. Future versions aim to extend the recognizable species to cover the land-based flora of the Hawaiian Islands.