Multivariate sensor signals collected by aquatic drones involved in water monitoring: A complete dataset

Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today's data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 h of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm for monitoring water quality of catchments. The aquatic drones used for data acquisition are Platypus Lutra boats. Both autonomous and manual drive is used in different parts of the navigation. The dataset is analyzed in the paper “Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework” [1] by means of recent time series clustering/segmentation techniques to extract data-driven models of the situations faced by the drones in the data acquisition campaigns. These data have strong potential for reuse in other kinds of data analysis and evaluation of machine learning methods on real-world datasets [2]. Moreover, we consider this dataset valuable also for the variety of situations faced by the drone, from which machine learning techniques can learn behavioral patterns or detect anomalous activities. We also provide manual labeling for some known states of the drones, such as, drone inside/outside the water, upstream/downstream navigation, manual/autonomous drive, and drone turning, that represent a ground truth for validation purposes. Finally, the real-world nature of the dataset makes it more challenging for machine learning methods because it contains noisy samples collected while the drone was exposed to atmospheric agents and uncertain water flow conditions

Vision based robot-to-robot object handover

This paper presents an autonomous robot-to-robot object handover in the presence of uncertainties and in the absence of explicit communication. Both the giver and receiver robots are equipped with an eye-in-hand depth camera. The object to handle is roughly positioned in the field of view of the giver robot's camera and a deep learning based approach is adopted for detecting the object. The physical exchange is performed by recurring to an estimate of the contact forces and an impedance control, which allows the receiver robot to perceive the presence of the object and the giver one to recognize that the handover is complete. Experimental results, conducted on a couple of collaborative 7 DoF manipulators in a partially structured environment, demonstrate the effectiveness of the proposed approach

Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset

Laser cleaning of gilded wood: a comparative study of colour variations induced by irradiation at different wavelengths

There is a growing interest by art conservators for laser cleaning of wood artworks, since traditional cleaning with chemical solvents can be a source of decay, due to the prolonged action of chemicals after the restoration. In this experiment we used excimer and Nd:YAGlasers, emitting radiation in the ultraviolet (248 nm), visible (532 nm) and near infrared (1064 nm), to investigate the effect of laser interaction on gilded wood samples at different wavelengths

Ordering of droplets and light scattering in polymer dispersed liquid crystal films

We study the effects of droplet ordering in initial optical transmittance through polymer dispersed liquid crystal (PDLC) films prepared in the presence of an electrical field. The experimental data are interpreted by using a theoretical approach to light scattering in PDLC films that explicitly relates optical transmittance and the order parameters characterizing both the orientational structures inside bipolar droplets and orientational distribution of the droplets. The theory relies on the Rayleigh-Gans approximation and uses the Percus-Yevick approximation to take into account the effects due to droplet positional correlations.Comment: revtex4, 18 pages, 8 figure

Functionalization of Carbon Nanomaterial Surface by Doxorubicin and Antibodies to Tumor Markers

The actual task of oncology is effective treatment of cancer while causing a minimum harm to the patient. The appearance of polymer nanomaterials and technologies launched new applications and approaches of delivery and release of anticancer drugs. The goal of work was to test ultra dispersed diamonds (UDDs) and onion-like carbon (OLCs) as new vehicles for delivery of antitumor drug (doxorubicin (DOX)) and specific antibodies to tumor receptors. Stable compounds of UDDs and OLCs with DOX were obtained. As results of work, an effectiveness of functionalization was 2.94 % w/w for OLC-DOX and 2.98 % w/w for UDD-DOX. Also, there was demonstrated that UDD-DOX and OLC-DOX constructs had dose-dependent cytotoxic effect on tumor cells in the presence of trypsin. The survival of adenocarcinoma cells reduced from 52 to 28 % in case of incubation with the UDD-DOX in concentrations from 8.4–2.5 to 670–20 μg/ml and from 72 to 30 % after incubation with OLC-DOX. Simultaneously, antibodies to epidermal growth factor maintained 75 % of the functional activity and specificity after matrix-assisted pulsed laser evaporation deposition. Thus, the conclusion has been made about the prospects of selected new methods and approaches for creating an antitumor agent with capabilities targeted delivery of drugs

Polymer-Dispersed liquid Crystals

Series in Optics and Optoelectronic