We live in the era of the fourth industrial revolution, where everything - from small objects to entire factories-is smart and connected, and we are also strongly accustomed to comforts and services, but emergent questions are arising. What are the consequences of human activities on terrestrial and aquatic/marine systems? And how does the loss of biodiversity alter the integrity and functioning of ecosystems? It is reasonable to assert that there are correlations between the anthropic pressure and degradation of natural habitats and loss in biodiversity. In fact, the alteration of ecosystem structure affects ecosystem services and resilience, the level of perturbation that an ecosystem can withstand without shifting to an alternative status providing fewer benefits to humans [1]. To that regards, the research studies on cetacean species distribution and conservation status along with their habitats can give an idea of the current impact of human pressure on marine biodiversity and its ecosystem services, being both dolphins and whales key species in the marine food webs. However, although the inherent complexity of food-web dynamics often makes difficult to investigate and quantify the role of marine mammals in the ecosystem [2], the challenge to investigate their ecological significance is leading and highly informative when facing human induced environmental changes from local to global scales. For this reason, dedicated research activities have been performed in the last years to standardize the best practices for sampling and collecting scientific relevant information on the cetaceans in the Gulf of Taranto (Northern Ionian Sea in the Central-Eastern Mediterranean Sea) [3, 4, 5, 6]. Standardized scientific protocols and technological innovations have been brought by integrating interdisciplinary approaches: a genetic study on dolphin's social structure, an automated photo-identification, assisted by intelligent unsupervised algorithms and the study of acoustic signals. Finally, education and citizen science were applied as fundamental to raise awareness on the need of marine environmental protection among the active population, from children to adults.

In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.

The railway maintenance is a particular application context required in order to prevent any dangerous situation. With the growing of the high-speed railway traffic, automatic inspection systems able to detect rail defects, sleepers' anomalies, as well as missing fastening elements, become strategic since they could increase the ability in the detection of defects and reduce the inspection time in order to guarantee more frequent maintenance of the railway network. This paper presents a patented fully automatic and configurable real-time vision system able to detect the presence/absence of the fastening bolts that fix the rails to the sleepers. It gets an accuracy of 99.9%, and, thanks to its parallel processing allowed by a Graphic Processing Unit, reaches an average throughput of 187 km/h, speeding up of about 287% the performance of a quadcore CPU implementation.

Rail inspection is a very important task in railway maintenance, and it is periodically needed for preventing dangerous situations. Inspection is operated manually by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity, as the results are related to the ability of the observer to recognize critical situations. The correspondence presents a patent-pending real-time Visual Inspection System for Railway (VISyR) maintenance, and describes how presence/absence of the fastening bolts that fix the rails to the sleepers is automatically detected. VISyR acquires images from a digital line-scan camera. Data are simultaneously preprocessed according to two discrete wavelet transforms, and then provided to two multilayer perceptron neural classifiers (MLPNCs). The "cross validation" of these MLPNCs avoids (practically-at-all) false positives, and reveals the presence/absence of the fastening bolts with an accuracy of 99.6% in detecting visible bolts and of 95% in detecting missing bolts. A field-programmable gate array-based architecture performs these tasks in 8.09 mus, allowing an on-the-fly analysis of a video sequence acquired at 200 km/h

This paper focuses on the problem of how data representation influences the generalization error of kernel based learning machines like Support Vector Machines (SVM) for classification. Frame theory provides a well founded mathematical framework for representing data in many different ways. We analyze the effects of sparse and dense data representations on the generalization error of such learning machines measured by using leave-one-out error given a finite number of training data. We show that, in the case of sparse data representation, the generalization capacity of an SVM trained by using polynomial or Gaussian kernel functions is equal to the one of a linear SVM. This is equivalent to saying that the capacity of separating points of functions belonging to hypothesis spaces induced by polynomial or Gaussian kernel functions reduces to the capacity of a separating hyperplane in the input space. We show that sparse data representations reduce the generalization error as long as the representation is not too sparse, as in the case of very large dictionaries. Dense data representations, on the contrary, reduce the generalization error also in the case of very large dictionaries. We use two different schemes for representing data in overcomplete Haar and Gabor dictionaries, and measure SVM generalization error on bench mark data set. Moreover we study sparse and dense data representations with frame of data and we show how this leads to Principal Component Analysis.

Inspection of the rail state in order to detect defects is one of the basic tasks in railway maintenance. Rail defects exhibit different properties and are divided in various categories relating to the type and position of flaws on the rail. We propose a technique, based on texture analysis of the rail surface, to detect and classify a particular class of defects: corrugation.

The goal of this work is to propose a method to solve the problem of underwater navigation of an autonomous vehicle with visual means. We refer to the ability of an autonomous system to determine its motion with respect to the environment. The method we propose is a two stage approach: matching of features extracted from 2D images acquired at different times and egomotion parameter computation. Both algorithms are based on optimization approaches minimizing appropriate energy functions. The matching stage recovers correspondences between sparse high interest feature points of two successive images useful to perform the second stage of egomotion parameter estimation. Experimental results obtained in real context show the robustness of the method.

Sparse optical flow estimated by an image sequence, often, is enough to obtain useful information about camera motion. Normally, matching among features on image sequence is estimated by radiometric similarity. When the camera is moving on a flat surface (planer navigation) additional constraints can be introduced in order to obtain a more robust estimation of the optical flow. In this paper, an energy based optimization method to compute a robust sparse optical flow using global constraints induced by camera motion, is proposed. Convergence is reached in few iterations, making the proposed method suitable for real-time applications.

The main aim of this paper is to describe an efficient approach for egomotion parameters estimation in order to solve the passive navigation problem. Passive navigation is the ability of an autonomous agent to determine its motion with respect to the environment. The two main egomotion parameters allowing to perform passive navigation are the heading direction and the time to collision with the environment. A lot of approaches have been proposed in literature in order to estimate the above parameters, most of which work well only if the motion is a predominant translation forward or small amounts of noise are present in the input data. In our work we propose a method computing in real time, using a least square approach, the egomotion parameters from same robust feature correspondences. The approach we propose is based on the observation that the radial structures of the 2D motion field generated by passive navigation can be approximated by a 2D motion field corresponding to the projection on the image plane of a translational motion on both X horizontal axis and Z axis. The aim is to show that the heading direction can be computed correctly from translational motion parameters, though large rotations are performed by the vehicle.

Goal-oriented navigation of a mobile robot by landmark based techniques is a straightforward and suitable approach. Most of the methods would normally give an estimation of the position and orientation of the vehicle, but often they are not able to provide a good estimate of the uncertainty in the measurement. That information is useful in application where multisensor fusion is requested. Our method permits to determine the vehicle location and relative uncertainty, when its orientation is obtained by a heading sensor, using a visual landmark based method. The approach is straightforward and suitable for real time performance on general purpose hardware. Experimental results are provided by implementation on our autonomous mobile vehicle SAURO.

Fuzzy logic is a useful tool for realizing a direct mapping between perceptual situations and control commands in robotic applications that do not require internal representation or planning. It allows explicit programming and automatic learning from suitable training data to be mixed in several ways in order to produce the necessary control rules. Automatic learning enables both the reduction of the annoying and error-prone explicit programming work and the evolution in time of the controller in order to cope with dynamically changing environments. At any time an expert can verify and eventually modify the knowledge of the fuzzy system according to its personal experience. Two methods for the automatic extraction of rules from training data have been proved on a fuzzy control system for wall-following. The system is intended to grow including all the behaviors required for the safe navigation of a autonomous mobile vehicle and their arbitration. Distance measures supplied by an ultrasonic sensor ring have been chosen as sensory data. The training data have been collected during operator-driven runs of the vehicle. The same data have been used by the two methods for building rule bases that have proved effectively in driving a real AGV (a TRC Labmate base) in an indoor environment.

The goal of this work is to propose a method to solve the problem of passive navigation with visual means. The method is a two stage approach: matching of feature extracted from 2D images of a sequence at different times and egomotion parameter computation. Both algorithms are based on a least-square-error technique to minimize appropriate energy functions. Experimental results obtained in real context show the robustness of the method.

The development of new techniques in sequencing nuclei acids has produced a great amount of sequence data and has led to the discovery of new relationships. In this paper, we study a method for parallelizing the algorithm WORDUP, which detects the presence of statistically significant patterns in DNA sequences. WORDUP implements an efficient method to identify the presence of statistically significant oligomers in a non-homologous group of sequences. It is based on a modified version of the Boyer-Moore algorithm, which is one of the fastest algorithms for string matching available in the literature. The aim of the parallel version of WORDUP presented here is to speed up the computational time and allow the analysis of a greater set of longer nucleotide sequences, which is usually impractical with sequential algorithms.

This paper is concerned with the probleni of determining the position of the vehicle both in goal-oriented and free- path navigations. A vision system for self location of a mobile robot has been devel- oped. By means of few known landmarks, the system has the ability of continuously monitoring position and compare these es- timates with the measures provided by the odometers. Besides a procedure has been im- plemented that recovers the position of the vehicle when the other sensors completely fail.

Recovering three-dimensional visible surfaces is an important cue in computer vision. Like most of the inverse problems it is hard to be solved due to the insufficient and inaccurate data that can be collected using passive sensors. Data fusion and integration over time can be used to overcome this problem. In this paper the depth and orientation are acquired from a scene and are used together to build a dense map of the visible surface for each of several points of view. An incremental estimator is used to integrate these maps, as soon as they become available, in a more reliable result.>