Query: IoT based autonomous percipient irrigation system using raspberry Pi

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Automatic crop irrigation system <=SEP=> India is agriculture based nation. It is necessary to improve the productivity and quality of agro based products. The proposed design is an automatic system that aids the famers in irrigation process. It keeps notifying the farmer through an on-board LCD display and messages that is sent on the farmer's cellular number. This proposed design is also helpful for the farmers who are facing power failure issues to maintain a uniform water supply due to power failure or inadequate and non-uniform water supply. The automatic irrigation system also keeps the farmer updated with all the background activities through a SIM900 Module that sends messages on the registered number. This device can be a turning point for our society. The device is easily affordable by the farmers of the country. This proposed design is helpful for reducing the human labour. This is a low budget system with an essential social application.

Automatic drip irrigation system using fuzzy logic and mobile technology <=SEP=> An intelligent drip irrigation system optimizes water and fertilizer use for agricultural crops using wireless sensors and fuzzy logic. The wireless sensor networks consists of many sensor nodes, hub and control unit. The sensor collects real-time data such as temperature, soil humidity. This data is sent to the hub using the wireless technology. The hub processes the data using fuzzy logic and decides the time duration for keeping the valves open. Accordingly, the drip irrigation system is implemented for a particular amount of time. The whole system is powered by photovoltaic cells and has a communication link which allows the system to be monitored, controlled, and scheduled through cellular text messages. The system can quickly and accurately calculate water demand amount of crops, which can provide a scientific basis for water-saving irrigation, as well as a method to optimize the amount of fertilizer used.

Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network <=SEP=> Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.

AUTOMATED IRRIGATION SYSTEM USING THERMOELECTRIC GENERATOR AS SOIL MOISTURE DETECTOR <=SEP=> This paper shows the design and fabrication of a Thermo-electric generator (TEG) and the implementation of an automated irrigation system using this TEG as a soil moisture detector. The TEG inserted in two heat exchangers is capable of finding the thermal difference between the air and the soil that establishes a relationship with the soil’s moisture condition. Being able to obtain the soil moisture level from the TEG’s output, a microcontroller is used to automate the irrigation system. The irrigation system adapts to the soil area’s condition it irrigates based from the moisture it detects via the TEG. The water consumption of the soil is controlled by the automated irrigation system based on the soil’s condition and therefore, promotes water conservation compared to that of the water consumption of manual irrigation system. It also optimizes plant growth in that it waters it to the correct moisture level at the right time.

A Wireless Design of Low-Cost Irrigation System Using ZigBee Technology <=SEP=> At present, labor-saving and water-saving technology is a key issue in irrigation. A wireless solution for intelligent field irrigation system dedicated to Jew's-ear planting in Lishui, Zhejiang, China, based on ZigBee technology was proposed in this paper. Instead of conventional wired connection, the wireless design made the system easy installation and maintenance. The hardware architecture and software algorithm of wireless sensor/actuator node and portable controller, acting as the end device and coordinator in ZigBee wireless sensor network respectively, were elaborated in detail. The performance of the whole system was evaluated in the end. The long-time smooth and proper running of the system in the field proved its high reliability and practicability. As an explorative application of wireless sensor network in irrigation management, this paper offered a methodology to establish large-scale remote intelligent irrigation system.

Axial Force and Vibroacoustic Analysis of External-Rotor Axial-Flux Motors <=SEP=> This paper presents a detailed analysis of the axial electromagnetic force and vibroacoustic behavior of the external-rotor axial-flux motors. First, the spatial and temporal characteristics of axial force acting on the surface of magnets are derived analytically and validated through the 2-D Fourier decomposition. Subsequently, a multiphysics model is established to calculate the vibration and noise of an external-rotor axial-flux in-wheel motor, which integrates the control model, the electromagnetic model, the structural model, and the acoustic radiation model and takes the uneven distribution of axial force into account. The accuracy of the multiphysics model is verified by the noise test. The vibroacoustic mechanism of axial-flux motors is revealed based on the multiphysics model, the influence of current harmonics and dead time effect on the vibration and noise are also analyzed. Finally, the main orders of tested noise are explained by the theoretical analysis. It is found that the zeroth spatial order axial force is dominant for the generation of the vibration and noise in axial-flux motors. The harmonic current may deteriorate the vibroacoustic behavior, but depending on its amplitude and phase. This study provides guidance for the design of low-noise axial-flux motors.

Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model <=SEP=> Urban land use information plays an essential role in a wide variety of urban planning and environmental monitoring processes. During the past few decades, with the rapid technological development of remote sensing (RS), geographic information systems (GIS) and geospatial big data, numerous methods have been developed to identify urban land use at a fine scale. Points-of-interest (POIs) have been widely used to extract information pertaining to urban land use types and functional zones. However, it is difficult to quantify the relationship between spatial distributions of POIs and regional land use types due to a lack of reliable models. Previous methods may ignore abundant spatial features that can be extracted from POIs. In this study, we establish an innovative framework that detects urban land use distributions at the scale of traffic analysis zones (TAZs) by integrating Baidu POIs and a Word2Vec model. This framework was implemented using a Google open-source model of a deep-learning language in 2013. First, data for the Pearl River Delta (PRD) are transformed into a TAZ-POI corpus using a greedy algorithmby considering the spatial distributions of TAZs and inner POIs. Then, high-dimensional characteristic vectors of POIs and TAZs are extracted using the Word2Vec model. Finally, to validate the reliability of the POI/TAZ vectors, we implement a K-Means-based clustering model to analyze correlations between the POI/TAZ vectors and deploy TAZ vectors to identify urban land use types using a random forest algorithm (RFA) model. Compared with some state-of-the-art probabilistic topic models (PTMs), the proposed method can efficiently obtain the highest accuracy (OA = 0.8728, kappa = 0.8399). Moreover, the results can be used to help urban planners to monitor dynamic urban land use and evaluate the impact of urban planning schemes. ARTICLE HISTORY Received 21 March 2016 Accepted 28 September 2016

Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation <=SEP=> The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL approach directly learns the policy based on data samples. Although using many samples tends to improve the accuracy of policy learning, collecting a large number of samples is often expensive in practice. On the other hand, the model-based RL approach first estimates the transition model of the environment and then learns the policy based on the estimated transition model. Thus, if the transition model is accurately learned from a small amount of data, the model-based approach is a promising alternative to the model-free approach. In this paper, we propose a novel model-based RL method by combining a recently proposed model-free policy search method called policy gradients with parameter-based exploration and the state-of-the-art transition model estimator called least-squares conditional density estimation. Through experiments, we demonstrate the practical usefulness of the proposed method.

Missing data correction in still images and image sequences <=SEP=> The ability to replace missing data in images and video is of key importance to many application fields. The general-purpose algorithm presented here is inspired by texture synthesis techniques but is suited to any complex natural scene and not restricted to stationary patterns. It has the property to be adapted to both still images and image sequences and to incorporate temporal information when available while preserving the simplicity of the algorithm. This method gives very good results in various situations without user intervention. The resulting computational cost is relatively low and corrections are usually produced within seconds.

Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks <=SEP=> This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.

A Smart Home Appliances Power Management System for Handicapped, Elder and Blind People <=SEP=> Smart home has become a visible concept that attracts the collaboration of various areas of science and engineering. Increasing the power efficiency and power management has become an important research topic since a decade. In this research article, the focus is to ease the life of elder and handicapped people. In point of fact as compare to the healthy people, elderly and disabled people experiences difficulties to perform their everyday activities. Elderly and disabled people can be supported by using smart homes by providing them secure, safe, and controlled environments. The developed system allows the users to be able to control the appliances with least physical efforts. More particularly, the designed system allow users to switch the home appliances ON and OFF just by sending message command by using Android application or SMS with the help of a cell phone. Not only the far remote monitoring, but the local management of appliances is made possible by using the Bluetooth technology. The experimental results demonstrate that the user can control the home appliances with the designed Android application by using the voice, Bluetooth, and SMS inputs.

Detecting Singleton Review Spammers Using Semantic Similarity <=SEP=> Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).

Fuzzy sets, advanced fuzzy sets and hybrids <=SEP=> Fuzzy Sets were proposed several years ago with various extensions in later years. Each extension has advantages over the fuzzy sets. Rough sets are used to handle incomplete information. Interval-valued fuzzy sets deal with uncertainty and vagueness. Intuitionistic fuzzy sets contain a sub-interval hesitation degree that lies between membership and non-membership degrees. Soft sets overcome the problem of insufficiency of parameterization. Advanced fuzzy sets have myriad number of advantages due to their applications in realistic examples. This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids. Advanced fuzzy sets include rough sets, intuitionistic fuzzy sets, interval-valued fuzzy sets and soft sets.

The habitual consumer <=SEP=> Consumers sometimes act like creatures of habit, automatically repeating past behavior with little regard to current goals and valued outcomes. To explain this phenomenon, we show that habits are a specific form of automaticity in which responses are directly cued by the contexts (e.g., locations, preceding actions) that consistently covaried with past performance. Habits are prepotent responses that are quick to activate in memory over alternatives and that have a slow-to-modify memory trace. In daily life, the tendency to act on habits is compounded by everyday demands, including time pressures, distraction, and self-control depletion. However, habits are not immune to deliberative processes. Habits are learned largely as people pursue goals in daily life, and habits are broken through the strategic deployment of effortful self-control. Also, habits influence the post hoc inferences that people make about their behavior. © 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.

Reputation and e-commerce : eBay auctions and the asymmetrical impact of positive and negative ratings <=SEP=> This analysis explores the impact and nature of reputation as related to e-commerce by looking at the importance of a seller’s reputational rating on the final bid price associated with eBay auctions. Positive reputational ratings emerged as mildly influential in determining final bid price. However, negative reputational ratings emerged as highly influential and detrimental. Thus, we find strong evidence for the importance of reputation when engaging in e-commerce and equally strong evidence concerning the exaggerated influence of negative reputation. © 2001 Elsevier Science Inc. All rights reserved.

Substrate Modeling and Lumped Substrate Resistance Extraction for CMOS ESD/Latchup Circuit Simulation <=SEP=> Due to interactions through the common silicon substrate, the layout and placement of devices and substrate contacts can have signi cant impacts on a circuit's ESD (Electrostatic Discharge) and latchup behavior in CMOS technologies. Proper substrate modeling is thus required for circuit-level simulation to predict the circuit's ESD performance and latchup immunity. In this work we propose a new substrate resistance network model, and develop a novel substrate resistance extraction method that accurately calculates the distribution of injection current into the substrate during ESD or latchup events. With the proposed substrate model and resistance extraction, we can capture the three-dimensional layout parasitics in the circuit as well as the vertical substrate doping pro le, and simulate these e ects on circuit behavior at the circuit-level accurately. The usefulness of this work for layout optimization is demonstrated with an industrial circuit example.

Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. <=SEP=> OBJECTIVES
To assess whether talking or reading (silently or aloud) could affect heart rate variability (HRV) and to what extent these changes require a simultaneous recording of respiratory activity to be correctly interpreted.


BACKGROUND
Sympathetic predominance in the power spectrum obtained from short- and long-term HRV recordings predicts a poor prognosis in a number of cardiac diseases. Heart rate variability is often recorded without measuring respiration; slow breaths might artefactually increase low frequency power in RR interval (RR) and falsely mimic sympathetic activation.


METHODS
In 12 healthy volunteers we evaluated the effect of free talking and reading, silently and aloud, on respiration, RR and blood pressure (BP). We also compared spontaneous breathing to controlled breathing and mental arithmetic, silent or aloud. The power in the so called low- (LF) and high-frequency (HF) bands in RR and BP was obtained from autoregressive power spectrum analysis.


RESULTS
Compared with spontaneous breathing, reading silently increased the speed of breathing (p < 0.05), decreased mean RR and RR variability and increased BP. Reading aloud, free talking and mental arithmetic aloud shifted the respiratory frequency into the LF band, thus increasing LF% and decreasing HF% to a similar degree in both RR and respiration, with decrease in mean RR but with minor differences in crude RR variability.


CONCLUSIONS
Simple mental and verbal activities markedly affect HRV through changes in respiratory frequency. This possibility should be taken into account when analyzing HRV without simultaneous acquisition and analysis of respiration.

SensePlace2: GeoTwitter analytics support for situational awareness <=SEP=> Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.

Biomedical Signal and Image Processing Spring 2008 <=SEP=> Introduction In this chapter we will examine how we can generalize the idea of transforming a time series into an alternative representation, such as the Fourier (frequency) domain, to facilitate systematic methods of either removing (filtering) or adding (interpolating) data. In particular, we will examine the techniques of Principal Component Analysis (PCA) using Singular Value Decomposition (SVD), and Independent Component Analysis (ICA). Both of these techniques utilize a representation of the data in a statistical domain rather than a time or frequency domain. That is, the data are projected onto a new set of axes that fulfill some statistical criterion, which implies independence, rather than a set of axes that represent discrete frequencies such as with the Fourier transform, where the independence is assumed. Another important difference between these statistical techniques and Fourier-based techniques is that the Fourier components onto which a data segment is projected are fixed, whereas PCA-or ICA-based transformations depend on the structure of the data being analyzed. The axes onto which the data are projected are therefore discovered. If the structure of the data (or rather the statistics of the underlying sources) changes over time, then the axes onto which the data are projected will change too 1. Any projection onto another set of axes (or into another space) is essentially a method for separating the data out into separate components or sources which will hopefully allow us to see important structure more clearly in a particular projection. That is, the direction of projection increases the signal-to-noise ratio (SNR) for a particular signal source. For example, by calculating the power spectrum of a segment of data, we hope to see peaks at certain frequencies. The power (amplitude squared) along certain frequency vectors is therefore high, meaning we have a strong component in the signal at that frequency. By discarding the projections that correspond to the unwanted sources (such as the noise or artifact sources) and inverting the transformation, we effectively perform a filtering of the recorded observation. This is true for both ICA and PCA as well as Fourier-based techniques. However, one important difference between these techniques is that Fourier techniques assume that the projections onto each frequency component are independent of the other frequency components. In PCA and ICA we attempt to find a set of axes which are independent of one another in some sense. We assume there are a set of independent 1 …

Formal Semantics and Analysis of BPMN Process Models using Petri Nets ? <=SEP=> The Business Process Modelling Notation (BPMN) is a standard for capturing business processes in the early phases of systems development. The mix of constructs found in BPMN makes it possible to obtain models with a range of semantic errors. The ability to statically check the semantic correctness of models is thus a desirable feature for modelling tools based on BPMN. However, the static analysis of BPMN models is hindered by ambiguities in the standard specification and the complexity of the language. The fact that BPMN integrates constructs from graph-oriented process definition languages with features for concurrent execution of multiple instances of a subprocess and exception handling, makes it challenging to provide a formal semantics of BPMN. Even more challenging is to define a semantics that can be used to analyse BPMN models. This paper proposes a formal semantics of BPMN defined in terms of a mapping to Petri nets, for which efficient analysis techniques exist. The proposed mapping has been implemented as a tool that generates code in the Petri Net Markup Language. This formalisation exercise has also led to the identification of a number of deficiencies in the BPMN standard specification.

Performance Enhancement of Boost Converter Based on PID Controller Plus Linear-to-Nonlinear Translator <=SEP=> Here, A PID controller plus a novel linear-to-nonlinear translator is proposed and applied to the boost converter to improve the operating performance of the boost converter under large transient variations in load all over the quiescent dc input voltage range. The proposed translator is inserted between the PID controller and the main power stage such that the PID controller considers the behavior of the boost converter to be quasi-linear, thereby suppressing the effect of nonlinear behavior of the boost converter on the PID controller. Besides, variations in dc gain of the control-to-output transfer function with the translator are smaller than those without the translator, thus making system control easy to attain. Also, one-comparator counter-based sampling without any A-D converter is presented herein to realize the proposed control strategy. As mentioned above, the proposed control strategy can make the boost converter stably operated under large load transient responses all over the quiescent dc input voltage range.

The Virtual Reality Modeling Language and Java <=SEP=> Why VRML and Java together? Over 20 million VRML browsers have been shipped with Web browsers, making interactive 3D graphics suddenly available for any desktop. Java adds complete programming capabilities plus network access, making VRML fully functional and portable. This is a powerful new combination, especially as ongoing research shows that VRML plus Java provide extensive support for building large-scale virtual environments (LSVEs). This article provides historical background, a detailed overview of VRML 3D graphics, sample VRML-Java test programs, and a look ahead at future work.

Single actuator wave-like robot (SAW): design, modeling, and experiments. <=SEP=> In this paper, we present a single actuator wave-like robot, a novel bioinspired robot which can move forward or backward by producing a continuously advancing wave. The robot has a unique minimalistic mechanical design and produces an advancing sine wave, with a large amplitude, using only a single motor but with no internal straight spine. Over horizontal surfaces, the robot does not slide relative to the surface and its direction of locomotion is determined by the direction of rotation of the motor. We developed a kinematic model of the robot that accounts for the two-dimensional mechanics of motion and yields the speed of the links relative to the motor. Based on the optimization of the kinematic model, and accounting for the mechanical constraints, we have designed and built multiple versions of the robot with different sizes and experimentally tested them (see movie). The experimental results were within a few percentages of the expectations. The larger version attained a top speed of 57 cm s(-1) over a horizontal surface and is capable of climbing vertically when placed between two walls. By optimizing the parameters, we succeeded in making the robot travel by 13% faster than its own wave speed.

Graph-based distributed cooperative navigation for a general multi-robot measurement model <=SEP=> Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, to a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly-developed method is computationally efficient.

Validating a Deep Learning Framework by Metamorphic Testing <=SEP=> Deep learning has become an important tool for image classification and natural language processing. However, the effectiveness of deep learning is highly dependent on the quality of the training data as well as the net model for the learning. The training data set for deep learning normally is fairly large, and the net model is pretty complex. It is necessary to validate the deep learning framework including the net model, executing environment, and training data set before it is used for any applications. In this paper, we propose an approach for validating the classification accuracy of a deep learning framework that includes a convolutional neural network, a deep learning executing environment, and a massive image data set. The framework is first validated with a classifier built on support vector machine, and then it is tested using a metamorphic validation approach. The effectiveness of the approach is demonstrated by validating a deep learning classifier for automated classification of biology cell images. The proposed approach can be used for validating other deep learning framework for different applications.

File Fragment Classification-The Case for Specialized Approaches <=SEP=> Increasingly advances in file carving, memory analysis and network forensics requires the ability to identify the underlying type of a file given only a file fragment. Work to date on this problem has relied on identification of specific byte sequences in file headers and footers, and the use of statistical analysis and machine learning algorithms taken from the middle of the file. We argue that these approaches are fundamentally flawed because they fail to consider the inherent internal structure in widely used file types such as PDF, DOC, and ZIP. We support our argument with a bottom-up examination of some popular formats and an analysis of TK PDF files. Based on our analysis, we argue that specialized methods targeted to each specific file type will be necessary to make progress in this area.

Self-traits and motivations as antecedents of digital media flow and addiction: The Internet, mobile phones, and video games <=SEP=> 

Mobile robot localization and object pose estimation using optical encoder, vision and laser sensors <=SEP=> A key problem of a mobile robot system is how to localize itself and detect objects in the workspace. In this paper, a multiple sensor based robot localization and object pose estimation method is presented. First, optical encoders and odometry model are utilized to determine the pose of the mobile robot in the workspace, with respect to the global coordinate system. Next, a CCD camera is used as a passive sensor to find an object (a box) in the environment, including the specific vertical surfaces of the box. By identifying and tracking color blobs which are attached to the center of each vertical surface of the box, the robot rotates and adjusts its base pose to move the color blob into the center of the camera view in order to make sure that the box is in the range of the laser scanner. Finally, a laser range finder, which is mounted on the top of the mobile robot, is activated to detect and compute the distances and angles between the laser source and laser contact surface on the box. Based on the information acquired in this manner, the global pose of the robot and the box can be represented using the homogeneous transformation matrix. This approach is validated using the Microsoft Robotics Studio simulation environment.

An In Vitro Study on the Effects of Post-Core Design and Ferrule on the Fracture Resistance of Endodontically Treated Maxillary Central Incisors <=SEP=> BACKGROUND
Endodontically treated teeth have significantly different physical and mechanical properties compared to vital teeth and are more prone to fracture. The study aims to compare the fracture resistance of endodontically treated teeth with and without post reinforcement, custom cast post-core and prefabricated post with glass ionomer core and to evaluate the ferrule effect on endodontically treated teeth restored with custom cast post-core.


MATERIALS AND METHODS
A total of 40 human maxillary central incisors with similar dimensions devoid of any root caries, restorations, previous endodontic treatment or cracks were selected from a collection of stored extracted teeth. An initial silicone index of each tooth was made. They were treated endodontically and divided into four groups of ten specimens each. Their apical seal was maintained with 4 mm of gutta-percha. Root canal preparation was done and then post core fabrication was done. The prepared specimens were subjected to load testing using a computer coordinated UTM. The fracture load results were then statistically analyzed. One-way ANOVA was followed by paired t-test.


RESULTS
1. Reinforcement of endodontically treated maxillary central incisors with post and core, improved their fracture resistance to be at par with that of endodontically treated maxillary central incisor, with natural crown. 2. The fracture resistance of endodontically treated maxillary central incisors is significantly increased when restored with custom cast post-core and 2 mm ferrule.


CONCLUSION
With 2 mm ferrule, teeth restored with custom cast post-core had a significantly higher fracture resistance than teeth restored with custom cast post-core or prefabricated post and glass ionomer core without ferrule.

UAV-based crop and weed classification for smart farming <=SEP=> Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants.