Monitoring robot actions for error detection and recovery

Reliability is a serious problem in computer controlled robot systems. Although robots serve successfully in relatively simple applications such as painting and spot welding, their potential in areas such as automated assembly is hampered by programming problems. A program for assembling parts may be logically correct, execute correctly on a simulator, and even execute correctly on a robot most of the time, yet still fail unexpectedly in the face of real world uncertainties. Recovery from such errors is far more complicated than recovery from simple controller errors, since even expected errors can often manifest themselves in unexpected ways. Here, a novel approach is presented for improving robot reliability. Instead of anticipating errors, researchers use knowledge-based programming techniques so that the robot can autonomously exploit knowledge about its task and environment to detect and recover from failures. They describe preliminary experiment of a system that they designed and constructed

Two-dimensional imaging of the spin-orbit effective magnetic field

We report on spatially resolved measurements of the spin-orbit effective magnetic field in a GaAs/InGaAs quantum-well. Biased gate electrodes lead to an electric-field distribution in which the quantum-well electrons move according to the local orientation and magnitude of the electric field. This motion induces Rashba and Dresselhaus effective magnetic fields. The projection of the sum of these fields onto an external magnetic field is monitored locally by measuring the electron spin-precession frequency using time-resolved Faraday rotation. A comparison with simulations shows good agreement with the experimental data.Comment: 6 pages, 4 figure

Semanticizing syntactic patterns in NLP processing using SPARQL-DL queries

Some recent works on natural language semantic parsing make use of syntax and semantics together using different combination models. In our work we attempt to use SPARQL-DL as an interface between syntactic information given by the Stanford statistical parser (namely part-of-speech tagged text and typed dependency representation) and semantic information obtained from the FrameNet database. We use SPARQL-DL queries to check the presence of syntactic patterns within a sentence and identify their role as frame elements. The choice of SPARQL-DL is due to its usage as a common reference language for semantic applications and its high expressivity, which let rules to be generalized exploiting the inference capabilities of the underlying reasoner

Dynamic Underwater Glider Network for Environmental Field Estimation

A coordinated dynamic sensor network of autonomous underwater gliders to estimate three-dimensional time-varying environmental fields is proposed and tested. Integration with a network of surface relay nodes and asynchronous consensus are used to distribute local information and achieve the global field estimate. Field spatial sparsity is considered, and field samples are acquired by compressive sensing devices. Tests on simulated and real data demonstrate the feasibility of the approach with relative error performance within 10

photochemical performance of carpobrotus edulis in response to various substrate salt concentrations

Abstract Substrate salinity is one of the main abiotic factors limiting plant establishment, growth and distribution in coastal habitats. Nevertheless, few studies have investigated the interaction between salt concentration and duration of exposure on the physiology and growth of Carpobrotus edulis, an important invasive plant species growing in coastal dune habitats. In this study, four salinity treatment cycles of different length (three, six, twelve and twenty-four days) at salinity of 0 M, 0.1 M, 0.2 M and 0.3 M were imposed. A significant response in plant growth was elicited after 24 days of treatment. The main shoot length (MSL) and stem biomass (SBMS) increased by 11% and 4%, respectively at 0.1 M and by 25% and 6% at 0.2 M compared with the control. At 0.3 M MSL did not significantly differ from the control while SBMS was 18% lower. Moreover, C. edulis showed a high photoprotection mechanism efficiency resulting in a high carotenoid to chlorophyll ratio increase which was two, three and four times higher than the control at 0.1 M, 0.2 M and 0.3 M, respectively. Photochemically, the quantum yield of photosynthesis (ΦPSII) was 17%, 50% and 52% lower than the control at 0.1 M, 0.2 M and 0.3 M. The ΦPSII decrease was associated with a low leaf nitrogen content (NL) decrease (16%, 21% lower than the control at 0.1 M and 0.2 M, respectively). By contrast, NL had the highest decrease (41% lower than the control) at 0.3 M, which constrains the growth capacity. Overall, C. edulis was able to modulate its response to salinity. The salt stimulated shoot elongation at low or moderate salt concentrations could confer a competitive advantage making C. edulis even more efficient in establishing within the areas which it colonizes. Since the expansion of C. edulis may be enhanced by the forecasted increase in soil salinity, it will be of paramount importance to apply effective management practices in areas invaded by C. edulis to limit its expansion and preserve the native plant biodiversity

Naval Target Classification by Fusion of Multiple Imaging Sensors Based on the Confusion Matrix

This paper presents an algorithm for the classification of targets based on the fusion of the class information provided by different imaging sensors. The outputs of the different sensors are combined to obtain an accurate estimate of the target class. The performance of each imaging sensor is modelled by means of its confusion matrix (CM), whose elements are the conditional error probabilities in the classification and the conditional correct classification probabilities. These probabilities are used by each sensor to make a decision on the target class. Then, a final decision on the class is made using a suitable fusion rule in order to combine the local decisions provided by the sensors. The overall performance of the classification process is evaluated by means of the "fused" confusion matrix, i.e. the CM pertinent to the final decision on the target class. Two fusion rules are considered: a majority voting (MV) rule and a maximum likelihood (ML) rule. A case study is then presented, where the developed algorithm is applied to three imaging sensors located on a generic air platform: a video camera, an infrared camera (IR), and a spotlight Synthetic Aperture Radar (SAR)

Low absorption InP/InGaAs-MQW phase shifters for optical switching

InP/InGaAs-MQW phase shifters with low absorption loss and low electroabsorption loss have been realized. Phase shift efficiency for TE-polarized light at lambda =1.55 mu m was 6.8 degrees V/sup -1/ mm/sup -1/ with negligible absorption loss and at lambda =1.51 mu m the efficiency was 8.9 degrees V/sup -1/ mm/sup -1/ with 5 dB/cm absorption los

Price dispersion: the case of pasta

Scopo della ricerca è indagare la possibilità di utilizzare scanner data sugli acquisti di pasta per costruire indici dei prezzi spaziali bilaterali e multilaterali utilizzando un approccio binario nella loro costruzione.The aim of our research is to explore the possibility of utilizing scanner data on pasta purchases to build bilateral and multilateral spatial price indexes, taking a binary approach in the latter.1 Pasta plays a major role in the Italian diet. Historically, pasta consumption was mainly concentrated in the Southern regions of the country but today pasta is perhaps the product most representative of the eating habits of the Italians. The range of pasta producers runs from firms of longstanding tradition (some of them mainly directed towards local markets, such as Mastromauro in Puglia) to well known international brands (such as Barilla and De Cecco). The marked increase in pasta prices over the last two years has aroused great interest, but with little focus on spatial price diversity. This study stems from the availability of an extremely detailed panel dataset (Nielsen data) on values and quantities of pasta purchased. This data was produced by the use of bar-code scanning at retail outlets and thus includes information which provides weights at an elementary level. The use of scanner data to construct price indexes is not new in literature and there is a widespread consensus on the advantages of this approach in achieving more representative indexes. Average prices (unit values) show a marked spatial price variability: even when only considering the five bestselling products, regional prices vary greatly. The paper is set out as follows: Sect. 2 provides a description of the pasta scanner dataset and briefly looks for price variability; in Sect. 3 the requirements of comparability and representativity in the case of pasta are discussed; Sect. 4 deals with the methods and formulas chosen to obtain indexes for the regional comparisons of prices; Sect. 5 shows empirical results; in Sect. 6 a brief conclusion and suggestions for future work are given