Pre-Harvest and Post-Harvest Techniques for Plant Disease Detections

As the agriculture industry is growing fast, many efforts are introduced to ensure a high quality of produce. Diseases and defects found in plants and crops affect greatly the agriculture industry. Hence, many techniques and technologies have been developed to help solve or reduce the impact of plant diseases. Imagining analysis tools and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and VOC (Volatile Organic Compound) profiling techniques to detect early symptoms of diseases and defects of plants, fruits, and vegetative produce. These disease detection techniques can be further categorized into two main groups: preharvest disease detection and postharvest disease detection techniques. This paper aims to introduce the available disease detection techniques and to compare them with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this paper considers the efforts to automate imaging techniques to help accelerate the disease detection process. Different approaches are analyzed and compared in terms of work environment, automation, implementation, and accuracy of disease identification along with the future evolution perspective in this field

Robot ontologies for sensor- and Image-guided surgery

Robots and robotics are becoming more com- plex and flexible, due to technological advancement, improved sensing capabilities and machine intelligence. Service robots target a wide range of applications, relying on advanced Human–Robot Interaction. Medical robotics is becoming a leading application area within, and the number of surgical, rehabilitation and hospital assistance robots is rising rapidly. However, the complexity of the medical environment has been a major barrier, preventing a wider use of robotic technology, thus mostly teleoperated, human-in-the-loop control solutions emerged so far. Providing smarter and better medical robots requires a systematic approach in describing and translating human processes for the robots. It is believed that ontologies can bridge human cognitive understanding and robotic reasoning (machine intelligence). Besides, ontologies serve as a tool and method to assess the added value robotic technology brings into the medical environment. The purpose of this paper is to identify relevant ontology research in medical robotics, and to review the state-of-the art. It focuses on the surgical domain, fundamental terminology and interactions are described for two example applications in neurosurgery and orthopaedics

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

Search for physics beyond the standard model in events with a Z boson, jets, and missing transverse energy in pp collisions at √s̅ = 7 TeV

Peer reviewe

Search for the standard model Higgs boson in the decay channel H to ZZ to 4 leptons in pp collisions at sqrt(s) = 7 TeV

A search for a Higgs boson in the four-lepton decay channel H to ZZ, with each Z boson decaying to an electron or muon pair, is reported. The search covers Higgs boson mass hypotheses in the range 110 < mH < 600 GeV. The analysis uses data corresponding to an integrated luminosity of 4.7 inverse femtobarns recorded by the CMS detector in pp collisions at sqrt(s) = 7 TeV from the LHC. Seventy-two events are observed with four-lepton invariant mass m[4 leptons] > 100 GeV (with thirteen below 160 GeV), while 67.1 +/- 6.0 (9.5 +/-1.3) events are expected from background. The four-lepton mass distribution is consistent with the expectation of standard model background production of ZZ pairs. Upper limits at 95% confidence level exclude the standard model Higgs boson in the ranges 134-158 GeV, 180-305 GeV, and 340 -465 GeV. Small excesses of events are observed around masses of 119, 126, and 320 GeV, making the observed limits weaker than expected in the absence of a signal.Comment: Submitted to Physical Review Letter

Measurement of the t(t)over-bar production cross section in pp collisions at root s=7 TeV in dilepton final states containing a tau

The top quark pair production cross section is measured in dilepton events with one electron or muon, and one hadronically decaying tau lepton from the decay t (t) over bar -> (l nu(l))((sic)(h)nu((sic)))b (b) over bar, (l = e, mu). The data sample corresponds to an integrated luminosity of 2.0 fb(-1) for the electron channel and 2.2 fb(-1) for the muon channel, collected by the CMS detector at the LHC. This is the first measurement of the t (t) over bar cross section explicitly including tau leptons in proton- proton collisions at root s = 7 TeV. The measured value sigma(t (t) over bar) = 143 +/- 14(stat) +/- 22(syst) +/- 3(lumi) pb is consistent with the standard model predictions