Monitoring of posture allocations and activities by a shoe-based wearable sensor.

Monitoring of posture allocations and activities enables accurate estimation of energy expenditure and may aid in obesity prevention and treatment. At present, accurate devices rely on multiple sensors distributed on the body and thus may be too obtrusive for everyday use. This paper presents a novel wearable sensor, which is capable of very accurate recognition of common postures and activities. The patterns of heel acceleration and plantar pressure uniquely characterize postures and typical activities while requiring minimal preprocessing and no feature extraction. The shoe sensor was tested in nine adults performing sitting and standing postures and while walking, running, stair ascent/descent and cycling. Support vector machines (SVMs) were used for classification. A fourfold validation of a six-class subject-independent group model showed 95.2% average accuracy of posture/activity classification on full sensor set and over 98% on optimized sensor set. Using a combination of acceleration/pressure also enabled a pronounced reduction of the sampling frequency (25 to 1 Hz) without significant loss of accuracy (98% versus 93%). Subjects had shoe sizes (US) M9.5-11 and W7-9 and body mass index from 18.1 to 39.4 kg/m2 and thus suggesting that the device can be used by individuals with varying anthropometric characteristics.

Optical crop sensor for variable-rate nitrogen fertilization in corn: i - plant nutrition and dry matter production

Variable-rate nitrogen fertilization (VRF) based on optical spectrometry sensors of crops is a technological innovation capable of improving the nutrient use efficiency (NUE) and mitigate environmental impacts. However, studies addressing fertilization based on crop sensors are still scarce in Brazilian agriculture. This study aims to evaluate the efficiency of an optical crop sensor to assess the nutritional status of corn and compare VRF with the standard strategy of traditional single-rate N fertilization (TSF) used by farmers. With this purpose, three experiments were conducted at different locations in Southern Brazil, in the growing seasons 2008/09 and 2010/11. The following crop properties were evaluated: above-ground dry matter production, nitrogen (N) content, N uptake, relative chlorophyll content (SPAD) reading, and a vegetation index measured by the optical sensor N-Sensor® ALS. The plants were evaluated in the stages V4, V6, V8, V10, V12 and at corn flowering. The experiments had a completely randomized design at three different sites that were analyzed separately. The vegetation index was directly related to above-ground dry matter production (R² = 0.91; p<0.0001), total N uptake (R² = 0.87; p<0.0001) and SPAD reading (R² = 0.63; p<0.0001) and inversely related to plant N content (R² = 0.53; p<0.0001). The efficiency of VRF for plant nutrition was influenced by the specific climatic conditions of each site. Therefore, the efficiency of the VRF strategy was similar to that of the standard farmer fertilizer strategy at sites 1 and 2. However, at site 3 where the climatic conditions were favorable for corn growth, the use of optical sensors to determine VRF resulted in a 12 % increase in N plant uptake in relation to the standard fertilization, indicating the potential of this technology to improve NUE.

Optical crop sensor for variable-rate nitrogen fertilization in corn: II - indices of fertilizer efficiency and corn yield

Generally, in tropical and subtropical agroecosystems, the efficiency of nitrogen (N) fertilization is low, inducing a temporal variability of crop yield, economic losses, and environmental impacts. Variable-rate N fertilization (VRF), based on optical spectrometry crop sensors, could increase the N use efficiency (NUE). The objective of this study was to evaluate the corn grain yield and N fertilization efficiency under VRF determined by an optical sensor in comparison to the traditional single-application N fertilization (TSF). With this purpose, three experiments with no-tillage corn were carried out in the 2008/09 and 2010/11 growing seasons on a Hapludox in South Brazil, in a completely randomized design, at three different sites that were analyzed separately. The following crop properties were evaluated: aboveground dry matter production and quantity of N uptake at corn flowering, grain yield, and vegetation index determined by an N-Sensor® ALS optical sensor. Across the sites, the corn N fertilizer had a positive effect on corn N uptake, resulting in increased corn dry matter and grain yield. However, N fertilization induced lower increases of corn grain yield at site 2, where there was a severe drought during the growing period. The VRF defined by the optical crop sensor increased the apparent N recovery (NRE) and agronomic efficiency of N (NAE) compared to the traditional fertilizer strategy. In the average of sites 1 and 3, which were not affected by drought, VRF promoted an increase of 28.0 and 41.3 % in NAE and NRE, respectively. Despite these results, no increases in corn grain yield were observed by the use of VRF compared to TSF.

Comportamento do NDVI obtido por sensor ótico ativo em cereais

O objetivo deste trabalho foi avaliar, com um sensor ótico ativo, o comportamento do índice de vegetação por diferença normalizada (NDVI - "normalized difference vegetation index"), nas culturas de trigo, triticale, cevada e milho. Cinco experimentos foram conduzidos no Paraná e São Paulo, com variação de classes de solo, doses e fontes de N, e variedades de trigo. As seguintes variáveis foram avaliadas: NDVI, teor de N foliar, matéria seca e produtividade das culturas. Análises de regressões foram realizadas entre as doses de N aplicadas e NDVI, teor de N foliar, matéria seca e produtividade. Análises de correlação entre as variáveis foram realizadas. O trigo, triticale e cevada apresentaram resposta às aplicações de doses crescentes de N, pelo aumento nas leituras do NDVI, no teor de N foliar e na produtividade. Medido pelo sensor ótico ativo utilizado, o NDVI apresenta alto potencial para manejo do N nas culturas do trigo, triticale e cevada, e baixo potencial para a cultura do milho. Há interferência das variedades de trigo nas leituras do sensor ótico ativo.

Sugarcane response to nitrogen rates, measured by a canopy reflectance sensor

Abstract:The objective of this work was to evaluate whether a canopy sensor is capable of estimating sugarcane response to N, as well as to propose strategies for handling the data generated by this device during the decision-making process for crop N fertilization. Four N rate-response experiments were carried out, with N rates varying from 0 to 240 kg ha-1. Two evaluations with the canopy sensor were performed when the plants reached average stalk height of 0.3 and 0.5 m. Only two experiments showed stalk yield response to N rates. The canopy sensor was able to identify the crop response to different N rates and the relationship of the nutrient with sugarcane yield. The response index values obtained from the canopy sensor readings were useful in assessing sugarcane response to the applied N rate. Canopy reflectance sensors can help to identify areas responsive to N fertilization and, therefore, improve sugarcane fertilizer management.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.

Identification of croplands of winter cereals in Rio Grande do Sul state, Brazil, through unsupervised classification of normalized difference vegetation index images

This study aimed to propose methods to identify croplands cultivated with winter cereals in the northern region of Rio Grande do Sul State, Brazil. Thus, temporal profiles of Normalized Difference Vegetation Index (NDVI) from MODIS sensor, from April to December of the 2000 to 2008, were analyzed. Firstly, crop masks were elaborated by subtracting the minimum NDVI image (April to May) from the maximum NDVI image (June to October). Then, an unsupervised classification of NDVI images was carried out (Isodata), considering the crop mask areas. According to the results, crop masks allowed the identification of pixels with greatest green biomass variation. This variation might be associated or not with winter cereals areas established to grain production. The unsupervised classification generated classes in which NDVI temporal profiles were associated with water bodies, pastures, winter cereals for grain production and for soil cover. Temporal NDVI profiles of the class winter cereals for grain production were in agree with crop patterns in the region (developmental stage, management standard and sowing dates). Therefore, unsupervised classification based on crop masks allows distinguishing and monitoring winter cereal crops, which were similar in terms of morphology and phenology.

Uso de imagens do sensor orbital modis na estimação de datas do ciclo de desenvolvimento da cultura da soja para o estado do Paraná – Brasil

RESUMO O Estado do Paraná caracteriza-se por uma grande variabilidade de épocas de semeadura (DS) e, consequentemente, pelo desenvolvimento máximo vegetativo (DMDV), colheita (DC) e ciclo (CI) para a cultura da soja. O objetivo deste trabalho foi estimar essas datas para o período de primavera-verão do ano-safra de 2011/2012, por meio de séries temporais de imagens do Índice de Vegetação Realçado (do inglês Enhanced Vegetation Index - EVI) do sensor Modis (Moderate Resolution Imaging Spectroradiometer). Gerou-se um perfil espectrotemporal médio de EVI, considerando todos os pixels mapeados como soja dentro de cada município. Estes dados serviram de entrada no software Timesat para estimar os decêndios do ciclo da cultura (DS, DMDV, DC e CI) por municípios. Os resultados mostraram que existe grande variabilidade de datas de plantio em diferentes mesorregiões do Estado. Verificaram-se também divergências entre os resultados encontrados e os dados oficiais de DS e DC. A maior parte da semeadura (65,16%) esteve entre o terceiro decêndio de outubro e o primeiro decêndio de novembro. A maior parte da área de soja do Estado do Paraná (65,46%) teve seu DMDV em janeiro e colheita em março (53,92%).

Use of bispectral index to monitor depth of anesthesia in isoflurane-anesthetized dogs

Objective-To evaluate the correlation between the bispectral index (BIS) and end-tidal isoflurane (ETISO) concentration and compare the use of 3 BIS sensor positions in dogs.Animals-6 adult dogs.Procedures-Mechanically ventilated dogs received pancuronium, and depth of anesthesia was altered by increasing ETISO concentration from 1.5% to 2.3% and 3.0%. The BIS, suppression ratio (relative percentage of isoelectric electroencephalographic waveforms), and signal quality index (SQI) were recorded at each ET, so concentration for each of 3 BIS sensor positions (frontal-occipital, bifrontal, and frontal-temporal positions).Results-The BIS and ETISO concentration were poorly correlated-, regardless of sensor positioning, mean BIS values did not change significantly as ETISO was increased. At 3% isoflurane, regardless of sensor positioning, there was an increase in suppression ratio coincident with BIS < 40 in some dogs, whereas paradoxic increases in BIS (> 60) were recorded in others. Furthermore, at 3.0% isoflurane, the SQI was significantly lower for the bifrontal sensor position (compared with values for the other positions), but low SQI values prevented recording of BIS values from the frontal-occipital sensor position in 2 dogs. Overall, BIS values derived from the 3 sensor positions did not differ.Conclusions and Clinical Relevance-In dogs, BIS values may not reflect changes in depth of isoflurane anesthesia in the absence of noxious stimulation. of the 3 sensor positions, frontal-temporal positioning provided better correlation with changes in depth of anesthesia induced via changes in isoflurane concentrations. However, the sensor placements yielded similar results at SQI values > 50.

Batimetria fluvial estimada com dados orbitais: Estudo de caso no alto curso do rio Paraguai com o sensor ASTER

The Paraguay River is the main tributary of the Paraná River and has an extension of 1.693 km in Brazilian territory. The navigability conditions are very important for the regional economy because most of the central-west Brazilian agricultural and mineral production is transported by the Paraguay waterway. Increased sedimentation along the channel requires continuous dredging to waterway maintenance. Systematic bathymetric surveys are periodically carried out in order to check depth condition along the channel using echo-sounding devices. In this paper, digital image processing and geostatistical analysis methods were used to analyze the applicability of the ASTER sensor to estimate channel depths in a segment of the upper Paraguay River. The results were compared with field data in order to choose the band with better adjustment and to evaluate the standard deviation. Comparing the VNIR bands, the best fit was presented by the red wavelength (band 2; 0,63 - 0,69 μm), showing a good representation of the channel depths shallow than 1,7 m. Applying geostatistical methods, the model accuracy was enhanced from 43 cm to 36 cm and undesired components were slacked. It was concluded that the digital number of band 2, converted to bathymetry information allows a good estimation of river depths and channel morphology.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

Wake up Radio Architecture for Wireless Sensor Networks Using an Ultra Low Power FPGA

In this paper an implementation of a Wake up Radio(WuR) with addressing capabilities based on an ultra low power FPGA for ultra low energy Wireless Sensor Networks (WSNs) is proposed. The main goal is to evaluate the utilization of very low power configurable devices to take advantage of their speed, flexibility and low power consumption instead of the traditional approaches based on ASICs or microcontrollers, for communication frame decoding and communication data control.

Using the Journalistic Metaphor to Design User Interfaces That Explain Sensor Data

Facilitating general access to data from sensor networks (including traffic, hydrology and other domains) increases their utility. In this paper we argue that the journalistic metaphor can be effectively used to automatically generate multimedia presentations that help non-expert users analyze and understand sensor data. The journalistic layout and style are familiar to most users. Furthermore, the journalistic approach of ordering information from most general to most specific helps users obtain a high-level understanding while providing them the freedom to choose the depth of analysis to which they want to go. We describe the general characteristics and architectural requirements for an interactive intelligent user interface for exploring sensor data that uses the journalistic metaphor. We also describe our experience in developing this interface in real-world domains (e.g., hydrology).

Validez y fiabilidad del sensor láser del sistema BioLaserSport® para el análisis de la velocidad de la carrera

El análisis de la velocidad de carrera mediante sensores láser permite la obtención de datos en tiempo real siendo ventajosos frente a otros sistemas. El objetivo de este estudio fue valorar la validez y fiabilidad del sensor láser del sistema BioLaserSport® para el cálculo de velocidades medias y máximas mediante estadísticos relativos y absolutos. Los participantes fueron 17 varones (20.85 ± 1.54 años). Se utilizó un sensor láser tipo 1 (LDM301, Jenoptik, Germany) que registró posiciones de los deportistas a 2000 Hz. Los datos se trataron con la rutina DSL-30 creada con DasyLab v.10.0. Para la validación se utilizó un sistema de fotogrametría-2D con una cámara de alta velocidad (Exilim High Speed EX-F1, Casio) y SkillSpector v.1.3.2. (Video4coach, Grubbemollevej). Además, se utilizaron foto-células de doble haz (Polifemo Light, Microgate, Italy) y un cronómetro Racetime2 (Microgate, Italy). Se registraron, durante dos días, tres series de 30 m de carrera a máxima velocidad. El sensor láser proporcionó, con relación a la fotogrametría, diferencias en las velocidades medias y máximas de -0.11 m•s-1 y 0.14 m•s-1, respectivamente, con unos coeficientes de correlación superiores a 0.86, y mayores de 0.92 con las foto-células para las velocidades medias. Este mostró una excelente fiabilidad test-retest para las velocidades medias con un coeficiente de correlación intraclase (ICC) entre 0.7-0.9 y un error estándar de la media (SEM y SEM%), intrasesión e intersesión, menor de 0.05 m•s-1 y 0.12 m•s-1, respectivamente, y menores de 0.75% y de 2%, respectivamente. Para las velocidades máximas,los valores fueron menores de 0.10 m•s-1 y 0.17 m•s-1, respectivamente, y en ambos casos menores a 1.36% y 1,89%. El láser fue capaz de identificar mínimos cambios detectables (MDC y MDC%) intrasesión, para ambas variables, menores a 0.14 m•s-1 y 0.29 m•s-1, respectivamente (< 2.09% y < 3.76%) e intersesión, menores de 0.34 m•s-1 y 0.47 m•s-1 (< 5.56% y < 5.25%), respectivamente. En consecuencia, es un instrumento útil para el análisis de la evolución de la velocidad intrasujeto y entre sujetos en la carrera de velocidad entre 0-30 m, proporcionando resultados en tiempo real, pero se han de considerar los SEM, SEM%, MDC y MDC% para valorar la mejora del rendimiento.