Planck pre-launch status: The optical system

Planck is a scientific satellite that represents the next milestone in space-based research related to the cosmic microwave background, and in many other astrophysical fields. Planck was launched on 14 May of 2009 and is now operational. The uncertainty in the optical response of its detectors is a key factor allowing Planck to achieve its scientific objectives. More than a decade of analysis and measurements have gone into achieving the required performances. In this paper, we describe the main aspects of the Planck optics that are relevant to science, and the estimated in-flight performance, based on the knowledge available at the time of launch. We also briefly describe the impact of the major systematic effects of optical origin, and the concept of in-flight optical calibration. Detailed discussions of related areas are provided in accompanying papers

Modelización de la difusividad de la humedad, la energía de activación y el consumo específico de energía para el grano de maíz húmedo en un secador convectivo de lecho fijo y fluidizado

Thin layer drying characteristics of high moisture corn under fixed, semi fluidized and fluidized bed conditions with high initial moisture content (66.82% wb) in a laboratory fluidized bed convective dryer was studied at air temperatures of 50, 65, 80 and 95°C. In order to find a suitable drying curve, seven thin layer-drying models were fitted to the experimental data of moisture ratio. Among the applied mathematical models, Midilli et al. model was the best for drying behavior prediction in corn thin layer drying. This model presented high values for correlation coefficient (R2). Fick´s second law was used to compute moisture diffusivity with some simplifications. Computed values of moisture diffusivity varied at the boundary of 4.87 × 10–11 – 2.90 × 10–10 m2 s–1 and 1.02 × 10–10 – 1.29 × 10–9 m2 s–1 during the first and second drying falling-rate, respectively. Values of effective moisture diffusivity for corn were also increased as input air temperature was increased. Value of activation energy varied from a minimum of 18.57 to a maximum of 50.74 kJ mol–1 from 50 to 95°C with drying conditions of fixed to fluidized bed. Specific energy consumption (SEC) for thin-drying of high moisture corn was found to be in the range of 0.33 × 106 – 1.52 × 106 kJ kg–1 from 50 to 95°C with drying condition of fluidized and fixed bed, respectively. Increase in air temperature in each air velocity caused decrease in SEC value. These corn properties would be necessary to design the best dryer system and to determine the best point of drying process.Se estudiaron las características del secado en capa delgada del grano de maíz húmedo en condiciones de lecho fijo, semi-fluidizado y fluidizado con alto contenido de humedad inicial (66,82%), en un secador de convección de lecho fluidizado de laboratorio a las temperaturas del aire de 50, 65, 80 y 95°C. Con el fin de encontrar una curva de secado apropiada, se ajustaron siete modelos matemáticos de secado en capa delgada a los datos experimentales de la ratio de humedad. Entre los modelos aplicados, el de Midilli et al., con un alto coeficiente de correlación (R2), fue el mejor para predecir el secado del maíz en capa delgada. Se utilizó la segunda ley de Fick para calcular, con algunas simplificaciones, la difusividad de la humedad, que dio unos valores entre 4,87 × 10–11 – 2.90 y 1,02 × 10–11 – 1.29 m2 s–1 durante la primera y segunda fase de secado de rapidez decreciente, respectivamente. Los valores de la difusividad efectiva de la humedad para el maíz también aumentaron al aumentar la temperatura de entrada del aire. El valor de la energía de activación varió desde un mínimo de 18,57 a un máximo de 50,74 kJ mol–1 entre 50 y 95°C, con condiciones de secado del lecho fijo a fluidizado. El consumo específico de energía (SEC) para secado en capa delgada del grano de maíz húmedo fue entre 0,33 × 106 y 1,52 × 106 kJ kg–1 entre 50 y 95°C, en lecho fluidizado y fijo, respectivamente. Un aumento de la temperatura en la velocidad del aire disminuye el valor de SEC. Es necesario conocer estas propiedades del maíz para diseñar el mejor sistema de secado y para determinar el mejor punto del proceso de secado

Anthracnose Detection on Walnut Tree Leaves using Outdoor Image Processing Methods

IntroductionControl of walnut diseases and pests requires the mapping of the extent of contamination within possible shortest time. Therefore, it is necessary to develop systems to detect and determine the prevalence and location of contamination for researchers and gardeners. Image processing has been proposed as an approach to determine the extent and type of damage to different products in farms and gardens. The aim of this study was to design an algorithm based on the processing of walnut leaf images under natural light conditions in order to provide a rapid and non-destructive detection of diseases for the protection of trees using imaging methods. In this research, the possibility of detecting Anthracnose disease was investigated by processing walnut leaf images. The disease was detected using in situ images taken from the leaves to provide the basis for designing application software on smart mechatronic systems. Materials and MethodsImages of leaves on walnut trees were taken under outdoor light conditions. Color and morphological properties extracted from the images were used to detect the pest on the leaves. Gnomonia leptostyla disease diagnostic algorithm was based on process of color and morphological characteristics, leaves background and disease-stained spots. The range of changes in R, G, and B indices was obtained in histograms and then two-dimensional spaces were analyzed statistically on GR, GB, and BR planes. All points from these regions were used as statistical samples, for which bivariate regressions of GR, GB, and BR were obtained as y = b0 + b1x. Segments containing anthracnose spots from the leaves were segregated by extracting the coordinates of the points in each side on the RGB color space cube. Finally, anthracnose content was detected based on the number of spots detected by the algorithms. The percentage of contamination was used to determine the amount of contamination in each imaged area.Results and DiscussionExamination of the colored spaces indicated that the domain of the anthracnose color components on the GR side has nothing in common with the color components of the leaves. The analysis of color space data revealed that the leaves and anthracnose were more distinguishable on the GB and RB sides, respectively. According to the histogram of the HSV color space, anthracnose spots were isolated from the leaves by determining the H range. In the evaluation of the proposed method for diagnosis of anthracnose, the infection severity calculated by the algorithm with the true infection intensity. T-test results for comparing the mean of the two infection intensity samples showed no significant differences between the two groups at 1% probability level. ConclusionsThe evaluation of the proposed method showed a 98% segregation accuracy for G. leptostyla detection method. Based on the results, the proposed method for detecting anthracnose spots is suitable for determining the contamination severity in the imaged areas

Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm

Repetitive and dangerous tasks such as harvesting and spraying have made robots usable in the greenhouses. The mechanical structure and navigation algorithm are two important parameters in the design and fabrication of mobile greenhouse robots. In this study, a four- wheel differential steering mobile robot was designed and constructed to act as a greenhouse robot. Then, the navigation of the robot at different levels and actual greenhouses was evaluated. The robot navigation algorithm was based on the path learning, so that the route was stored in the robot memory using a remote control based on the pulses transmitted from the wheels encoders; then, the robot automatically traversed the path. Robot navigation accuracy was tested at different surfaces (ceramics, concrete, dense soil and loose soil) in a straight path 20 meters long and a square path, 4×4 m. Then, robot navigation accuracy was investigated in a greenhouse. Robot movement deviation value was calculated using root mean square error (RMSE) and standard deviation (SD). The results showed that the RMSE of deviation of autonomous method from manual control method in the straight path to the length of 20 meters in ceramic, concrete, dense  soil and loose soil were 4.3, 2.8, 4.6 and 8 cm, and in the 4×4 m square route were 6.6, 5.5, 13.1 and 47.1 cm, respectively

Modeling of moisture diffusivity, activation energy and specific energy consumption of high moisture corn in a fixed and fluidized bed convective dryer

Thin layer drying characteristics of high moisture corn under fixed, semi fluidized and fluidized bed conditions with high initial moisture content (66.82% wb) in a laboratory fluidized bed convective dryer was studied at air temperatures of 50, 65, 80 and 95°C. In order to find a suitable drying curve, seven thin layer-drying models were fitted to the experimental data of moisture ratio. Among the applied mathematical models, Midilli et al. model was the best for drying behavior prediction in corn thin layer drying. This model presented high values for correlation coefficient (R2). Fick´s second law was used to compute moisture diffusivity with some simplifications. Computed values of moisture diffusivity varied at the boundary of 4.87 × 10�11 � 2.90 × 10�10 m2 s�1 and 1.02 × 10�10 � 1.29 × 10�9 m2 s�1 during the first and second drying falling-rate, respectively. Values of effective moisture diffusivity for corn were also increased as input air temperature was increased. Value of activation energy varied from a minimum of 18.57 to a maximum of 50.74 kJ mol�1 from 50 to 95°C with drying conditions of fixed to fluidized bed. Specific energy consumption (SEC) for thin-drying of high moisture corn was found to be in the range of 0.33 × 106 � 1.52 × 106 kJ kg�1 from 50 to 95°C with drying condition of fluidized and fixed bed, respectively. Increase in air temperature in each air velocity caused decrease in SEC value. These corn properties would be necessary to design the best dryer system and to determine the best point of drying process.Se estudiaron las características del secado en capa delgada del grano de maíz húmedo en condiciones de lecho fijo, semi-fluidizado y fluidizado con alto contenido de humedad inicial (66,82%), en un secador de convección de lecho fluidizado de laboratorio a las temperaturas del aire de 50, 65, 80 y 95°C. Con el fin de encontrar una curva de secado apropiada, se ajustaron siete modelos matemáticos de secado en capa delgada a los datos experimentales de la ratio de humedad. Entre los modelos aplicados, el de Midilli et al., con un alto coeficiente de correlación (R2), fue el mejor para predecir el secado del maíz en capa delgada. Se utilizó la segunda ley de Fick para calcular, con algunas simplificaciones, la difusividad de la humedad, que dio unos valores entre 4,87 × 10�11 � 2.90 y 1,02 × 10�11 � 1.29 m2 s�1 durante la primera y segunda fase de secado de rapidez decreciente, respectivamente. Los valores de la difusividad efectiva de la humedad para el maíz también aumentaron al aumentar la temperatura de entrada del aire. El valor de la energía de activación varió desde un mínimo de 18,57 a un máximo de 50,74 kJ mol�1 entre 50 y 95°C, con condiciones de secado del lecho fijo a fluidizado. El consumo específico de energía (SEC) para secado en capa delgada del grano de maíz húmedo fue entre 0,33 × 106 y 1,52 × 106 kJ kg�1 entre 50 y 95°C, en lecho fluidizado y fijo, respectivamente. Un aumento de la temperatura en la velocidad del aire disminuye el valor de SEC. Es necesario conocer estas propiedades del maíz para diseñar el mejor sistema de secado y para determinar el mejor punto del proceso de secado

Orange Recognition on Tree Using Image Processing Method Based on Lighting Density Pattern

Within the last few years, a new tendency has been created towards robotic harvesting of oranges and some of citrus fruits. The first step in robotic harvesting is accurate recognition and positioning of fruits. Detection through image processing by color cameras and computer is currently the most common method. Obviously, a harvesting robot faces with natural conditions and, therefore, detection must be done in various light conditions and environments. In this study, it was attempted to provide a suitable algorithm for recognizing the orange fruits on tree. In order to evaluate the proposed algorithm, 500 images were taken in different conditions of canopy, lighting and the distance to the tree. The algorithm included sub-routines for optimization, segmentation, size filtering, separation of fruits based on lighting density method and coordinates determination. In this study, MLP neural network (with 3 hidden layers) was used for segmentation that was found to be successful with an accuracy of 88.2% in correct detection. As there exist a high percentage of the clustered oranges in images, any algorithm aiming to detect oranges on the trees successfully should offer a solution to separate these oranges first. A new method based on the light and shade density method was applied and evaluated in this research. Finally, the accuracies for differentiation and recognition were obtained to be 89.5% and 88.2%, respectively

Analysis of Microscopic Image Textural Features of Artichoke Leaf Extract Powder Produced from Vacuum Spray Drying

Introduction The artichoke is part of the foods from the vegetable group that provide important nutrients like vitamin A and C, potassium and fiber which used as a food and medicine. In the pharmaceutical sector, dried extracts are used in the preparation of pills and capsules. Dried extracts can be prepared from the dehydration of a concentrated extractive solution from herbal materials (leaves, roots, seeds, etc.), resulting in a dried powder. The spray drying is widely used in the preparation of dried powders from extracts of medicinal plants, fruit pulps. One of the newly developed spray drying techniques is an ultrasonic vacuum method, which strengths of spray drying by incorporation of ultrasonic atomizer and vacuum chamber. Nowadays, image processing has been applied to food images, as acquired by different microscopic systems, to obtain numerical data about the morphology and microstructure of the analyzed foods. For this purpose, microscopy and image processing techniques could be considered as proper tools to evaluate qualitatively and quantitatively the food microstructure, making possible to carry out numerical correlations between microstructure data, as obtained from the images, and the textural properties of food powders. The textural characteristics of the obtained dried powders are determined by means of a perfect detection by scanning electron microscopy (SEM) pictures, and analyzed with a statistical approach for image texture studies, which calls the gray level co-occurrence matrix (GLCM) technique. The object of this study was to illustrate the application of image processing to the study of texture properties from extract powder using GLCM texture analysis and some vacuum spray dryer conditions effect on the texture features of mass particles and single particle SEM images. Materials and Methods After preparing water extract solution from artichoke leaves, extracts were dried under four conditions of vacuum spray drying (according to Table 1). To study the texture of the obtained dried extract powders, different representative features are extracted from the GLCM matrix. The angular second moment (ASM), which is defined as a measure of the homogeneity of the image, the contrast parameter (CT), which represents the amount of local variations given by differences in the gray values in the image. The correlation value (CR), which is a measure of gray tone linear dependencies in the image depending on the direction of the measure (different θs). The inverse difference moment value (IDM), which, similar to ASM, quantifies the homogeneity of the image, however, using a different equation, the entropy parameter (ET), which is a measure that is inversely related to the order given by the gray tones in the image. Rangefilt and stdfilt calculates the local range and local standard deviation of an image respectively. Entropyfilt calculates the local entropy of a grayscale image also. Parameters (ASM, CT, CR and IDM were analyzed in four directions (0º, 45º, 90º, and 135º). Results and Discussion The results of analysis of variance showed that, the difference between the textural features of a single particle and mass particles in four different conditions vacuum spray dryer was significant statistically. Texture analysis was demonstrated that larger ASM, CR, and IDM values indicate less roughness, whereas larger CT and ET values indicate more roughness. At lower inlet temperature and higher vacuum pressure, water diffusion in the material to be slower and allowing the deformation process in the particles to be more pronounced. Consequently, it was possible to observe that generated smaller particles are rougher and less spherical. When the concentration is increased, due to the constant concentration of the additive, the ratio of excipient (lactose) to extraction decreased, as a result were formed a greater number of particles with rougher surfaces. According to these conditions, the values of CT, ET, rangefilt and stdfilt were larger while ASM, CR, and IDM values were smaller. By analyzing the effect of the angle on the oriented textural characteristics, the contrast and correlation parameter were maximum at the angles of 45 and 135 degrees and 0 and 90 degrees respectively. Conclusions Image processing could be auxiliary tools for understanding and characterizing complex systems such as food and biological materials. In this study imaging-based technique was developed to evaluate the texture properties of artichoke leaf extract powder at different conditions of vacuum spray drying. The use of higher temperatures and lower vacuum pressures contributed to faster evaporation rate and production of smoother and larger particles, thereby increasing ASM, CR, and IDM values and reducing CT, ET, Rangefilt and stdfilt. Furthermore, the contrast and entropy parameters showed inverse trends in comparison with correlation, energy and homogeneity. Decrease of solution concentration resulted in the more presence of lactose in the composition of extract/excipient improves the textural properties of powders. The direction parameter had also affected on GLCM textural features. Two oriented textural characteristics (contrast and correlation) also showed significant differences with respect to the nature of particle texture in different directions of measurement. The obtained data extracted from image analysis may provide valuable information to understand the role of structure with respect to product functionality

Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars

Comparison of the precision of three commonly used GPS models

Introduction: Development of science in various fields has caused change in the methods to determine geographical location. Precision farming involves new technology that provides the opportunity for farmers to change in factors such as nutrients, soil moisture available to plants, soil physical and chemical characteristics and other factors with the spatial resolution of less than a centimeter to several meters to monitor and evaluate. GPS receivers based on precision farming operations specified accuracies are used in the following areas: 1) monitoring of crop and soil sampling (less than one meter accuracy) 2) use of fertilizer, pesticide and seed work (less than half a meter accuracy) 3) Transplantation and row cultivation (precision of less than 4 cm) (Perez et al., 2011). In one application of GPS in agriculture, route guidance precision farming tractors in the fields was designed to reduce the transmission error that deviate from the path specified in the range of 50 to 300 mm driver informed and improved way to display (Perez et al., 2011). In another study, the system automatically guidance, based on RTK-GPS technology, precision tillage operations was used between and within the rows very close to the drip irrigation pipe and without damage to their crops at a distance of 50 mm (Abidine et al., 2004). In another study, to compare the accuracy and precision of the receivers, 5 different models of Trimble Mark GPS devices from 15 stations were mapped, the results indicated that minimum error was related to Geo XT model with an accuracy of 91 cm and maximum error was related to Pharos model with an accuracy of 5.62 m (Kindra et al., 2006). Due to the increasing use of GPS receivers in agriculture as well as the lack of trust on the real accuracy and precision of receivers, this study aimed to compare the positioning accuracy and precision of three commonly used GPS receivers models used to specify receivers with the lowest error for precision farming operations as well as the efficiency of the work done in different situations. Materials and Methods: In this study, three commonly used GPS models belong to GARMIN CO. were selected for comparison. This company is the world biggest manufacturer of GPS device. Three models include eTrex VISTA, MAP 60 csx and MAP 78s that in recent years have been the most widely used receivers in precision agriculture (Figure 1, Table 1). To assess the accuracy and precision of the receivers, 9 recording stations were selected in a field (20×20 m2) and detailed mapping by the odolite camera under high precision compass networks and regular conditions (figure 2) was identified. To reduce the error of multi-path, a relatively open and unobstructed place in the Abbas Abad field of Bu-Ali Sina University were considered. This study was conducted in a Completely Randomized Design (CRD) with factorial analysis to examine three factors, at three levels, each in three replication including weather conditions (clear, partially cloudy and full cloudy sky), time of day (9 am, 12 am and 4 pm) and three different models of receiver (MAP 60 csx, eTrex VISTA and MAP 78s), in 9 local stations. Difference of deviation value at each station with the mean value of latitude and longitude recorded at same station was used to precision calculate on (equation 1) and the difference of deviation value at each station with a deviation of the actual position latitude and longitude of the same station was used to calculate the accuracy (equation 2). The base station position (No.1) was determined with an accurately large-scale map. Then, the positions of other stations were defined with camera and compass in exact rectangular grid by underlying base station. Mean error for each station using equation (3) and the precision and accuracy and the definitions of each receiver was calculated. Results and Discussion: To display the geographical distribution stations and the registered location data for GPS devices ArcView software (v3.3) was used (Fig.3). The real location of stations and registered by each receiver position has been determined. Information recorded in Map Source software, including all longitude and latitudes registered for each station and receiver were transferred to Excel Software (2007). Table 2 shows the mean precision values recorded in each weather conditions. The results obtained by equation 1 (the mean error at each station) showed that the GPS MAP 78s model has the lowest error of 91 cm, VISTA eTrex model has a maximum error of 4.7 meters and MAP 60 csx model has mean error of about 2.64 meters. The analysis of variance of models and weather conditions and the time of day with the interactions between factors have been shown in Table 3. Results showed that there is significant difference (0.01 <P value) between models, but there is no significant difference between the date and time positioning precision of different receivers models. Investigating of the interactions between the receiver models and the weather conditions showed no significant effect of them and the interaction between the receiver models and the measured time difference is not significant. These results showed that weather conditions and time of day is the same effect on positioning precision of GPS receivers used in this research. These results were consistent with the study of Jose and colleagues (Jose et al., 2006). The mean Comparison test of LSD (at 5% level) for the accuracy and precision of the models showed the significant difference for all models (Table 4). Figures 4 and 5 respectively show the accuracy and precision of three models of GPS receiver at different times of day and different weather conditions. Conclusions: Effect of daylight hours on positioning precision was very low; also the effect of different weather conditions may reduce the accuracy of GPS positioning to size of few centimeters. Overall, the results indicated that between the three factors include the models, the effects of weather and time only receiver models had significant effect in precision. The lowest error between the models was belonged to MAP 78s (91 cm) and the maximum error was belonged to eTrex VISTA model with the 4.7 m. In addition, results of this study showed that the correct application of GPS receivers in different conditions and select of appropriate receiver can be reduced positioning error considerably. According to the result the MAP 78s GPS receiver could be used for precision farming operations in the range of 1 to 3 meter such as crop monitoring and soil sampling and the other receivers (eTrex VISTA and MAP 60 csx) could be used in operations that require less precision (range of 3 to 5 meters)

High accuracy space structures monitoring by a close-range photogrammetric network

The Planck mission of the European Space Agency (ESA) is designed to image the anisotropies of the Cosmic Background Radiation Field over the whole sky. To achieve this aim, sophisticated reflectors are used as part of the Planck telescope receiving system. The system consists of secondary and primary reflectors which are sections of two different ellipsoids of revolution with mean diameters of 1 and 1.6 meters. Deformations of the reflectors which influence the optical parameters and the gain of receiving signals are investigated in vacuum and at very low temperatures. For this investigation, among the various high accuracy measurement techniques, photogrammetry was selected. With respect to the photogrammetric measurements, special considerations should be taken into account in different steps of design and processing, such as determinability of additional parameters under the given network configuration, datum definition, reliability and precision issues as well as workspace limits and propagating errors from different sources. We have designed an optimal close-range photogrammetric network by heuristic simulation for the primary and secondary reflectors with a relative precision better than 1:1,000,000 to achieve the requested accuracies. A least squares best-fit ellipsoid was developed to determine the optical parameters of the reflector. In this paper we will report about our network design and the results of real measurements based on the tests executed by Alcatel Alenia Space France (AASF) under European Space Technology and Research Center (ESTEC) contract in vacuum and under very low temperatures.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177