A Systematic Literature Review With Bibliometric Meta-Analysis Of Deep Learning And 3D Reconstruction Methods In Image Based Food Volume Estimation Using Scopus, Web Of Science And IEEE Database

Purpose- Estimation of food portions is necessary in image based dietary monitoring techniques. The purpose of this systematic survey is to identify peer reviewed literature in image-based food volume estimation methods in Scopus, Web of Science and IEEE database. It further analyzes bibliometric survey of image-based food volume estimation methods with 3D reconstruction and deep learning techniques. Design/methodology/approach- Scopus, Web of Science and IEEE citation databases are used to gather the data. Using advanced keyword search and PRISMA approach, relevant papers were extracted, selected and analyzed. The bibliographic data of the articles published in the journals over the past twenty years were extracted. A deeper analysis was performed using bibliometric indicators and applications with Microsoft Excel and VOS viewer. A comparative analysis of the most cited works in deep learning and 3D reconstruction methods is performed. Findings: This review summarizes the results from the extracted literature. It traces research directions in the food volume estimation methods. Bibliometric analysis and PRISMA search results suggest a broader taxonomy of the image-based methods to estimate food volume in dietary management systems and projects. Deep learning and 3D reconstruction methods show better accuracy in the estimations over other approaches. The work also discusses importance of diverse and robust image datasets for training accurate learning models in food volume estimation. Practical implications- Bibliometric analysis and systematic review gives insights to researchers, dieticians and practitioners with the research trends in estimation of food portions and their accuracy. It also discusses the challenges in building food volume estimator model using deep learning and opens new research directions. Originality/value- This study represents an overview of the research in the food volume estimation methods using deep learning and 3D reconstruction methods using works from 1995 to 2020. The findings present the five different popular methods which have been used in the image based food volume estimation and also shows the research trends with the emerging 3D reconstruction and deep learning methodologies. Additionally, the work emphasizes the challenges in the use of these approaches and need of developing more diverse, benchmark image data sets for food volume estimation including raw food, cooked food in all states and served with different containers

Redes de sensores sem fio de baixo custo para caracterização de vibrações em transporte de cargas críticas

Neste trabalho foi proposto, desenvolvido e testado um método de caracterização e análise de vibração em tempo real que pode ser aplicado no transporte de cargas sensíveis. Para isso, utilizou-se de nós sensores de uma rede de sensores sem fio que realizavam a aquisição dos dados e calculavam a densidade espectral de potencia (power spectral densitiy, PSD). Desta forma, ao transmitir apenas o resultado dos dois pontos mais significativos do PSD para o nó de controle, houve uma redução de 93,4% da quantidade de dados transmitidos, permitindo aumentar o tempo de aquisição dos dados sem recarga da bateria. Outra forma de reduzir o consumo de energia foi a utilização de interrupções por atividade existentes no acelerômetro para acordar o processador, permitindo assim manter o processador em um estado de pouco consumo de energia na maior parte do tempo. Para validar o sistema proposto, foram realizados diversos experimentos utilizando um sistema comercial, onde foi obtido um erro médio relativo na amplitude do PSD de 41% para a frequência de maior energia e 45% para a segunda frequência analisada. Os valores de erro obtidos, embora altos, não invalidam o sistema proposto, uma vez que foi obtido um erro médio absoluto menor que a resolução na identificação das frequências onde ocorreram estes picos. Algumas alterações podem ser feitas com possibilidade de melhoria do sistema proposto, como por exemplo realizar uma calibração do sistema em vez de utilizar os valores informados pelo fabricante e desacoplar mecanicamente o acelerômetro da placa do nó sensor.In this work, a method for characterization and analysis of vibration at real time that can be applied in the transport of sensitive loads was proposed, developed and tested. For that, was used sensors nodes from a wireless sensor network that performed the data acquisition and calculated the power spectral density (PSD). Thus, when transmitting only the result of the two most significant points of the PSD to the control node, there was a reduction of 93.4 % of the amount of data transmitted, allowing to increase the data acquisition time without battery recharging. Another way to reduce power consumption was to use activity interrupts on the accelerometer to wake the processor, keeping the processor in a low power state most of the time. To validate the proposed system, several experiments were performed using a commercial system, where the mean percentage error obtained for PSD amplitude was 41 % for the highest energy frequency and 45 % for the second analyzed frequency. It was high values, but they do not invalidate the proposed system, since the mean absolute error was smaller than the resolution in the identification of the frequencies where these peaks occurred. Some changes can be made with the possibility of improving the proposed system, such as performing a system calibration instead of using the values reported by the manufacturer and detaching the accelerometer from the sensor node board