Abstract
It is well-known that information and communication technologies enable many tasks in the context of precision agriculture. In fact, more and more farmers and food and agriculture companies are using precision agriculture-based systems to enhance not only their products themselves, but also their means of production. Consequently, problems arising from large amounts of data management and processing are arising. It would be very useful to have an infrastructure that allows information and agricultural tasks to be efficiently shared and handled. The cloud computing paradigm offers a solution. In this study, a cloud-based software architecture is proposed with the aim of enabling a complete crop management system to be deployed and validated. Such architecture includes modules developed by using Google App Engine, which allows the information to be easily retrieved and processed and agricultural tasks to be properly defined and planned. Additionally, Google's Datastore (which ensures a high scalability degree), hosts both information that describes such agricultural tasks and agronomic data. The architecture has been validated in a system that comprises a wireless sensor network with fixed nodes and a mobile node on an unmanned aerial vehicle (UAV), deployed in an agricultural farm in the Region of Murcia (Spain). Such a network allows soil water and plant status to be monitored. The UAV (capable of executing missions defined by an administrator) is useful for acquiring visual information in an autonomous manner (under operator supervision, if needed). The system performance has been analysed and results that demonstrate the benefits of using the proposed architecture are detailed.

Abstract
The development of smart orthopaedic implants is being considered as an effective solution to ensure their everlasting life span. The availability of electric power to supply active mechanisms of smart prostheses has remained a critical problem. This paper reports the first implementation of a new concept of energy harvesting systems applied to hip prostheses: the multi-source generation of electric energy. The reliability of the power supply mechanisms is strongly increased with the application of this new concept. Three vibration-based harvesters, operating in true parallel to harvest energy during human gait, were implemented on a Metabloc TM hip prosthesis to validate the concept. They were designed to use the angular movements on the flexion-extension, abduction-adduction and inward-outward rotation axes, over the femoral component, to generate electric power. The performance of each generator was tested for different amplitudes and frequencies of operation. Electric power up to 55 µJ/s was harvested. The overall function of smart hip prostheses can remain performing even if two of the generators get damaged. Furthermore, they are safe and autonomous throughout the life span of the implant. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
Instrumented hip implants were proposed as a method to monitor and predict the biomechanical and thermal environment surrounding such implants. Nowadays, they are being developed as active implants with the ability to prevent failures by loosening. The generation of electric energy to power active mechanisms of instrumented hip implants remains a question. Instrumented implants cannot be implemented without effective electric power systems. This paper surveys the power supply systems of seventeen implant architectures already implanted in-vivo, namely from instrumented hip joint replacements and instrumented fracture stabilizers. Only inductive power links and batteries were used in-vivo to power the implants. The energy harvesting systems, which were already designed to power instrumented hip implants, were also analyzed focusing their potential to overcome the disadvantages of both inductive-based and battery-based power supply systems. From comparative and critical analyses of the methods to power instrumented implants, one can conclude that: inductive powering and batteries constrain the full operation of instrumented implants; motion-driven electromagnetic energy harvesting is a promising method to power instrumented passive and active hip implants.

Abstract
Carcass data were collected from 24 kids (average live weight of 12.5 +/- 5.5 kg; range 4.5 to 22.4 kg) of Jarmelista Portuguese native breed, to evaluate bioelectrical impedance analysis (BIA) as a technique for prediction of light kid carcass and muscle chemical composition. Resistance (Rs, Omega) and reactance (Xc, Omega), were measured in the cold carcasses with a single frequency bioelectrical impedance analyzer and, together with impedance (Z, Omega), two electrical volume measurements (Vol(A) and Vol(B), cm(2)/Omega), carcass cold weight (CCW), carcass compactness and several carcass linear measurements were fitted as independent variables to predict carcass composition by stepwise regression analysis. The amount of variation explained by Vol(A) and Vol(B) only reached a significant level (P < 0.01 and P < 0.05, respectively) for muscle weight, moisture, protein and fat-free soft tissue content, even so with low accuracy, with VolA providing the best results (0.326 <= R-2 <= 0.366). Quite differently, individual BIA parameters (Rs, Xc and Z) explained a very large amount of variation in dissectible carcass fat weight (0.814 <= R-2 <= 0.862; P < 0.01). These individual BIA parameters also explained a large amount of variation in subcutaneous and intermuscular fat weights (respectively 0.749 <= R-2 <= 0.793 and 0.718 <= R-2 <= 0.760; P < 0.01), and in muscle chemical fat weight (0.663 <= R-2 <= 0.684; P < 0.01). Still significant but much lower was the variation in muscle, moisture, protein and fat-free soft tissue weights (0.344 <= R-2 <= 0.393; P < 0.01) explained by BIA parameters. Still, the best models for estimation of muscle, moisture, protein and fat-free soft tissue weights included Rs in addition to CCW, and accounted for 97.1% to 99.8% (P < 0.01) of the variation observed, with CCW by itself accounting for 97.0% to 99.6% (P < 0.01) of that variation. Resistance was the only independent variable selected for the best model predicting subcutaneous fat weight. It was also selected for the best models predicting carcass fat weight (combined with carcass length, CL; R-2 = 0.943; P < 0.01) and intermuscular fat weight (combined with CCW; R-2 = 0.945; P < 0.01). The best model predicting muscle chemical fat weight combined CCW and Z, explaining 85.6% (P < 0.01) of the variation observed. These results indicate BIA as a useful tool for prediction of light kids' carcass composition.

Abstract
Energy harvesting solutions such as instrumented orthopaedic implants are under development to power a wide variety of electronic systems including biomedical implants. Three micro-power generators have already been developed as part of a smart hip prosthesis structure. This paper outlines a power management architecture for efficient harvesting of energy to supply power to modules other than those powered by current instrumented implants. Considering that it is impossible to predict the amount of energy harvested by each particular person, the proposed system also comprises an activation circuit and its ultracapacitor energy reservoir as a fourth type of energy to be used when a continuous energy source is needed. The hip prosthesis prototype has now the capability to energize more power demanding loads, intermittently or continuously, such as radio-frequency modules. The proposed architecture enables operation of a Bluetooth low energy (V4.0) embedded device (BLE112 from Bluegiga), part of a wireless body sensor network, up to 50 s, and a MSP430/eZ430-RF2500 (Texas Instruments), which uses the SimpliciTl communication protocol, up to 110 s, solely using the energy produced by one of the generators.

Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data. © 2018 Association for Computing Machinery.