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.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

The fall speeds of sub-100 mu m ice crystals

Estimates for the sedimentation rate of realistic ice crystals at sizes smaller than 100 µm are presented. These calculations, which exploit new results for the capacitance of ice crystals, are compared with laboratory studies and found to be in good agreement. The results highlight a weakness in contemporary ice particle fall speed parametrizations for very small crystals, which can lead to sedimentation rates being overestimated by a factor of two. The theoretical approach applied here may also be useful for calculating the sedimentation rate and mobility of non-spherical aerosol particles.

Inequality, intergenerational mobility and redistributive policies under endogenous information

This paper presents a dynamic model to study how different levels of information about the root determinants of wealth (luck versus effort) can impact inequality and intergenerational mobility through societal beliefs, individual choices and redistributive policies. To my knowledge, the model presented is the first dynamicmodel in which skills are stochastic and both beliefs and voted redistribution are determined endogenously. The model is able to explain a number of empirical facts. Large empirical evidence shows that the difference in the political support for redistribution appears to reflect differences in the social perceptions regarding the determinants of individual wealth and the underlying sources of income inequality. Moreover the beliefs about the determinants of wealth impact individual choices of effort and therefore the beliefs about the determinants of wealth impact inequality and mobility both through choices of effort and redistributive policies. The model generates multiple equilibria (US versus Europe-type) which may account for the observed features not only in terms of societal beliefs and redistribution but also in terms of perceived versus real mobility and inequality.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Exercise programs improve mobility and balance in people with Parkinson's disease

Compromised balance and loss of mobility are among the major consequences of Parkinson's disease (PD). The literature documents numerous effective interventions for improving balance and mobility. The purpose of this study was to verify the effectiveness of two exercise programs on balance and mobility in people with idiopathic PD. Thirty-four participants, with idiopathic PD that ranged from Stage I to Stage III on the Hoehn & Yahr (H&Y) scale, were assigned to two groups. Group 1 (n = 21; 67±9 years old) was engaged in an intensive exercise program (aerobic capacity, flexibility, strength, motor coordination and balance) for 6 months: 72 sessions, 3 times a week, 60 minutes per session; while Group 2 (n = 13; 69±8 years old) participated in an adaptive program (flexibility, strength, motor coordination and balance) for 6 months: 24 sessions, once a week, 60 minutes per session. Balance and basic functional mobility were assessed in pre- and post-tests by means of the Berg Balance Scale and the Timed Up and Go Test. Before and after the interventions, groups were similar in clinical conditions (H&Y, UPDRS, and Mini-Mental). A MANOVA 2 (programs) by 2 (moments) revealed that both groups were affected by the exercise intervention. Univariate analyses showed that participants improved their mobility and balance from pre- to post-test. There were no differences between groups in either mobility or balance results. Both the intensive and adaptive exercise programs improved balance and mobility in patients with PD. © 2009 Elsevier Ltd. All rights reserved.

Development and application of a highly selective biomimetic sensor for detection of captopril, an important ally in hypertension control

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Handgrip and functional mobility in elderly with different levels of physical activity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Controlled Synthesis of Layered Sn3O4 Nanobelts by Carbothermal Reduction Method and Their Gas Sensor Properties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Active crop sensor to detect variability of nitrogen supply and biomass on sugarcane fields

Nitrogen management has been intensively studied on several crops and recently associated with variable rate on-the-go application based on crop sensors. Such studies are scarce for sugarcane and as a biofuel crop the energy input matters, seeking high positive energy balance production and low carbon emission on the whole production system. This article presents the procedure and shows the first results obtained using a nitrogen and biomass sensor (N-Sensor (TM) ALS, Yara International ASA) to indicate the nitrogen application demands of commercial sugarcane fields. Eight commercial fields from one sugar mill in the state of Sao Paulo, Brazil, varying from 15 to 25 ha in size, were monitored. Conditions varied from sandy to heavy soils and the previous harvesting occurred in May and October 2009, including first, second, and third ratoon stages. Each field was scanned with the sensor three times during the season (at 0.2, 0.4, and 0.6 m stem height), followed by tissue sampling for biomass and nitrogen uptake at ten spots inside the area, guided by the different values shown by the sensor. The results showed a high correlation between sensor values and sugarcane biomass and nitrogen uptake, thereby supporting the potential use of this technology to develop algorithms to manage variable rate application of nitrogen for sugarcane.

Physical models for numerical simulation of Si-based nanoscale FETs and sensors

To continuously improve the performance of metal-oxide-semiconductor field-effect-transistors (MOSFETs), innovative device architectures, gate stack engineering and mobility enhancement techniques are under investigation. In this framework, new physics-based models for Technology Computer-Aided-Design (TCAD) simulation tools are needed to accurately predict the performance of upcoming nanoscale devices and to provide guidelines for their optimization. In this thesis, advanced physically-based mobility models for ultrathin body (UTB) devices with either planar or vertical architectures such as single-gate silicon-on-insulator (SOI) field-effect transistors (FETs), double-gate FETs, FinFETs and silicon nanowire FETs, integrating strain technology and high-κ gate stacks are presented. The effective mobility of the two-dimensional electron/hole gas in a UTB FETs channel is calculated taking into account its tensorial nature and the quantization effects. All the scattering events relevant for thin silicon films and for high-κ dielectrics and metal gates have been addressed and modeled for UTB FETs on differently oriented substrates. The effects of mechanical stress on (100) and (110) silicon band structures have been modeled for a generic stress configuration. Performance will also derive from heterogeneity, coming from the increasing diversity of functions integrated on complementary metal-oxide-semiconductor (CMOS) platforms. For example, new architectural concepts are of interest not only to extend the FET scaling process, but also to develop innovative sensor applications. Benefiting from properties like large surface-to-volume ratio and extreme sensitivity to surface modifications, silicon-nanowire-based sensors are gaining special attention in research. In this thesis, a comprehensive analysis of the physical effects playing a role in the detection of gas molecules is carried out by TCAD simulations combined with interface characterization techniques. The complex interaction of charge transport in silicon nanowires of different dimensions with interface trap states and remote charges is addressed to correctly reproduce experimental results of recently fabricated gas nanosensors.

Interplay of pi–pi stacking and hydrogen bonding in conjugated supramolecular systems studied by solid-state NMR

In this work supramolecular organic systems based on rigid pi-conjugated building blocks and flexible side chains were studied via solid-state NMR spectroscopy. Specifically, these studies focussed on phenylene ethynylene based macrocycles, polymer systems including polythiophenes, and rod-coil copolymers of oligo(p-benzamide) and poly(ethylene glycol). All systems were studied in terms of the local order and mobility. The central topic of this dissertation was to elucidate the role of the flexible side chains in interplay of different non-covalent interactions, like pi-pi-stacking and hydrogen bonding.Combining the results of this work, it can be concluded that the ratio of the rigid block and the attached alkyl side chains can be crucial for the design of an ordered pi-conjugated supramolecular system. Through alkyl side chains, it is also possible to introduce liquid-crystalline phases in the system, which can foster the local order of the system. Moreover in the studied system longer, unbranched alkyl side chains are better suited to stabilize the corresponding aggregation than shorter, branched ones.The combination of non-covalent interactions such as pi-pi-stacking and hydrogen bonding play an important role for structure formation. However, the effect of pi-pi-stacking interaction is much weaker than the effect of hydrogen bonding and is only observed in systems with a suitable local order. Hence, they are often not strong enough to control the local order. In contrast, hydrogen bonds predominantly influence the structural organization and packing. In comparison the size of the alkyl side chains is only of minor importance. The suppression of certain hydrogen bonds can lead to completely different structures and can induce a specific aggregation behavior. Thus, for the design of a supramolecular ordered system the presence of hydrogen bonding efficiently stabilizes the corresponding structure, but the ratio of hydrogen bond forming groups should be kept low to be able to influence the structure selectively.

Integrating the Use of Spring- and Fall-Calving Beef Cows in a Year-round Grazing System (A Progress Report)

Animal production, hay production and feeding, and the yields and composition of forage from summer and winter grass-legume pastures and winter corn crop residue fields from a year-round grazing system were compared with those of a conventional system. The year-round grazing system utilized 1.67 acres of smooth bromegrass-orchardgrass-birdsfoot trefoil pasture per cow in the summer, and 1.25 acres of stockpiled tall fescue-red clover pasture per cow, 1.25 acres of stockpiled smooth bromegrass-red clover pasture per cow, and 1.25 acres of corn crop residues per cow during winter for spring- and fall-calving cows and stockers. First-cutting hay was harvested from the tall fescue-red clover and smooth bromegrass-red clover pastures to meet supplemental needs of cows and calves during winter. In the conventional system (called the minimal land system), spring-calving cows grazed smooth bromegrass-orchardgrass-birdsfoot trefoil pastures at 3.33 acres/cow during summer with first cutting hay removed from one-half of these acres. This hay was fed to these cows in a drylot during winter. All summer grazing was done by rotational stocking for both systems, and winter grazing of the corn crop residues and stockpiled forages for pregnant spring-calving cows and lactating fall-calving cows in the year-round system was managed by strip-stocking. Hay was fed to springcalving cows in both systems to maintain a mean body condition score of 5 on a 9-point scale, but was fed to fall-calving cows to maintain a mean body condition score of greater than 3. Over winter, fall-calving cows lost more body weight and condition than spring calving cows, but there were no differences in body weight or condition score change between spring-calving cows in either system. Fall- and spring-calving cows in the yearround grazing system required 934 and 1,395 lb. hay dry matter/cow for maintenance during the winter whereas spring-calving cows in drylot required 4,776 lb. hay dry matter/cow. Rebreeding rates were not affected by management system. Average daily gains of spring-born calves did not differ between systems, but were greater than fall calves. Because of differences in land areas for the two systems, weight production of calves per acre of cows in the minimal land system was greater than those of the year-round grazing system, but when the additional weight gains of the stocker cattle were considered, production of total growing animals did not differ between the two systems.

Evaluation of Year-round Forage Management Systems for Spring- and Fall-Calving Beef Cows (A Progress Report)

A year-round grazing system for spring- and fall-calving cows was developed to compare animal production and performance, hay production and feeding, winter forage composition changes, and summer pasture yield and nutrient composition to that from a conventional, or minimal land system. Systems compared forage from smooth bromegrass-orchardgrass-birdsfoot trefoil pastures for both systems in the summer and corn crop residues and stockpiled grass-legume pastures for the year-round system to drylot hay feeding during winter for the minimal land system. The year-round grazing system utilized 1.67 acres of smooth bromegrassorchardgrass- birdsfoot trefoil (SB-O-T) pasture per cow in the summer, compared with 3.33 acres of (SB-O-T) pasture per cow in the control (minimal land) system. In addition to SB-O-T pastures, the year-round grazing system utilized 2.5 acres of tall fescue-red clover (TFRC) and 2.5 acres of smooth bromegrass-red clover (SBRC) per cow for grazing in both mid-summer and winter for fall- and spring-calving cows, respectively. First-cutting hay was harvested from the TF-RC and SB-RC pastures, and regrowth was grazed for approximately 45 days in the summer. These pastures were then fertilized with 40 lbs N/acre and stockpiled for winter grazing. Also utilized during the winter for spring-calving cows in the year-round grazing system were corn crop residue (CCR) pastures at an allowance of 2.5 acres per cow. In the minimal land system, hay was harvested from three-fourths of the area in SB-O-T pastures and stored for feeding in a drylot through the winter. Summer grazing was managed with rotational stocking for both systems, and winter grazing of stockpiled forages and corn crop residues by year-round system cows was managed by strip-stocking. Hay was fed to maintain a body condition score of 5 on a 9 point scale for spring-calving cows in both systems. Hay was supplemented as needed to maintain a body condition score of 3 for fall-calving cows nursing calves through the winter. Although initial condition scores for cows in both systems were different at the initiation of grazing for both winter and summer, there were no significant differences (P > .05) in overall condition score changes throughout both grazing seasons. In year 1, fall-calving cows in the year-round grazing system lost more (P < .05) body weight during winter than spring-calving cows in either system. In year 2, there were no differences seen in weight changes over winter for any group of cows. Average daily gains of fall calves in the yearround system were 1.9 lbs/day compared with weight gains of 2.5 lbs/day for spring calves from both systems. Yearly growing animal production from pastures for both years did not differ between systems when weight gains of stockers that grazed summer pastures in the year-round grazing system were added to weight gains of suckling calves. Carcass characteristics for all calves finished in the feedlot for both systems were similar. There were no significant differences in hay production between systems for year 1; however, amounts of hay needed to maintain cows were 923, 1373, 4732 lbs dry matter/cow for year-round fall-calving, year-round spring-calving, and minimal land spring-calving cows, respectively. In year 2, hay production per acre in the minimal land system was greater (P < .05) than for the year-round system, but the amounts of hay required per cow were 0, 0, and 4720 lbs dry matter/cow for yearround fall-calving, year-round spring-calving, and minimal land spring-calving cows, respectively.