Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

This paper describes an optical-fiber sensor development and implementation, based on Bragg gratings, to measure the mechanical deformation at the bone surface of a bovine mandible, caused by chewing different types of food and rumination. A suitable encapsulation was developed using a titanium mesh as a transducer and sensor calibration and characterization tests were performed. The proposed sensor was deployed in a living animal transmitting the detected bone deformation to the data collecting instrument. The acquired signal was applied to a pattern-classification algorithm based on decision trees for identifying the chewing process of different foods. The results demonstrate that the sensor is effective and sensitive, capable of capturing the force generated during the masticatory process.

Researchers have linked pavements roughness to acceleration signals provided by smartphones, due to their simple handling and low cost, which might facilitate continuous data collection that is important for pavement management systems (PMS). However, the influence of several factors that may impair the quality of the smartphone data collection have not been clarified yet, which decreases the system reliability for real PMS applications. This paper presents a comparison between vertical accelerations provided by smartphones embedded in vehicles and international roughness index (IRI) values obtained from both rod and level and inertial laser profilometer surveys. The analysis considered the influence of variation of the speed, smartphone model, type of smartphone mount, number of measurement trips, length of segments, and vehicle engine vibrations in the correlation between IRI and root-mean-square of vertical accelerations (RMSVA). The results indicate that generic smartphone applications with IRI prediction models that do not consider the particularities of a calibration process and signal processing can lead to serious errors in the pavement roughness assessment.

Smartphone-based evaluation of urban cycling pavement types and rider comfort

A inclusão digital e o conhecimento das línguas portuguesa e inglesa são temas relevantes emtermos de educação e desenvolvimento socioeconômico porque a forma de comunicação, o uso e acessoàs Tecnologias de Informação e Comunicação (TICs) podem contribuir para minimizar desigualdadessociais. Isso ocorre devido ao amplo uso dessas tecnologias, seja para atividades profissionais, educação,busca e troca de informações e interação entre pessoas ou entretenimento. Aliada à inclusão digital está a inclusão linguística que provê recursos que também contribuem para a inserção do sujeito no mundo do trabalho, por meio do desenvolvimento de habilidades e competências de comunicação, além de fornecer conhecimentos da língua inglesa para o correto uso dos computadores. Assim, cria-se uma nova denominação, a dos excluídos linguístico-digitais. Como forma de prover a inclusão digital e linguística e queestas contribuam para a inclusão social, este artigo tem o objetivo apresentar resultados da aplicação do uso da tecnologia e das línguas portuguesa e inglesa, agregadas à outros conhecimentos, como literatura, por exemplo, para prover inclusão digital e linguística e consequentemente inclusão social.