Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving

IDRA (IDeal Resource Allocation): Computing ideal speedups in parallel logic programming

We present a technique to estimate accurate speedups for parallel logic programs with relative independence from characteristics of a given implementation or underlying parallel hardware. The proposed technique is based on gathering accurate data describing one execution at run-time, which is fed to a simulator. Alternative schedulings are then simulated and estimates computed for the corresponding speedups. A tool implementing the aforementioned techniques is presented, and its predictions are compared to the performance of real systems, showing good correlation.

Night time performance of a storage integrated solar thermophotovoltaic (SISTPV) system

Energy storage at low maintenance cost is one of the key challenges for generating electricity from the solar energy. This paper presents the theoretical analysis (verified by CFD) of the night time performance of a recently proposed conceptual system that integrates thermal storage (via phase change materials) and thermophotovoltaics for power generation. These storage integrated solar thermophotovoltaic (SISTPV) systems are attractive owing to their simple design (no moving parts) and modularity compared to conventional Concentrated Solar Power (CSP) technologies. Importantly, the ability of high temperature operation of these systems allows the use of silicon (melting point of 1680 K) as the phase change material (PCM). Silicon's very high latent heat of fusion of 1800 kJ/kg and low cost ($1.70/kg), makes it an ideal heat storage medium enabling for an extremely high storage energy density and low weight modular systems. In this paper, the night time operation of the SISTPV system optimised for steady state is analysed. The results indicate that for any given PCM length, a combination of small taper ratio and large inlet hole-to-absorber area ratio are essential to increase the operation time and the average power produced during the night time. Additionally, the overall results show that there is a trade-off between running time and the average power produced during the night time. Average night time power densities as high as 30 W/cm(2) are possible if the system is designed with a small PCM length (10 cm) to operate just a few hours after sun-set, but running times longer than 72 h (3 days) are possible for larger lengths (50 cm) at the expense of a lower average power density of about 14 W/cm(2). In both cases the steady state system efficiency has been predicted to be about 30%. This makes SISTPV systems to be a versatile solution that can be adapted for operation in a broad range of locations with different climate conditions, even being used off-grid and in space applications.