ALFI is a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.

Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, the task becomes even more arduous as cells change their morphology over time, can partially overlap, and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can be easily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In this study, we present ALFI, a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.

Epilepsy accounts for a significant proportion of the world's disease burden. Indeed, many research efforts are produced both to investigate the basic mechanism ruling its genesis and to find more effective therapies. In this framework, the use of zebrafish larvae, owing to their peculiar features, offers a great opportunity. Here, we employ transgenic zebrafish larvae expressing GCaMP6s in all neurons to characterize functional alterations occurring during seizures induced by pentylenetetrazole. Using a custom two-photon light-sheet microscope, we perform fast volumetric functional imaging of the entire larval brain, investigating how different brain regions contribute to seizure onset and propagation. Moreover, employing a custom behavioral tracking system, we outline the progressive alteration of larval swim kinematics, resulting from different grades of seizures. Collectively, our results show that the epileptic larval brain undergoes transitions between diverse neuronal activity regimes. Moreover, we observe that different brain regions are progressively recruited into the generation of seizures of diverse severity. We demonstrate that midbrain regions exhibit highest susceptibility to the convulsant effects and that, during periods preceding abrupt hypersynchronous paroxysmal activity, they show a consistent increase in functional connectivity. These aspects, coupled with the hub-like role that these regions exert, represent important cues in their identification as epileptogenic hubs.

We employ Difference of Log-Sum-Exp neural networks to generate a data-driven feedback controller based on Model Predictive Control (MPC) to track a given reference trajectory. By using this class of networks to approximate the MPC-related cost function subject to the given system dynamics and input constraint, we avoid two of the main bottlenecks of classical MPC: the availability of an accurate model for the system being controlled, and the computational cost of solving the MPC-induced optimization problem. The former is tackled by exploiting the universal approximation capabilities of this class of networks. The latter is alleviated by making use of the difference-of-convex-functions structure of these networks. Furthermore, we show that the system driven by the MPC-neural structure is practically stable.

Diamond sensors provide a promising radiation hard solution to the challenges posed by the future experiments at hadron machines. A 3D geometry with thin columnar resistive electrodes orthogonal to the diamond surface, obtained by laser nanofabrication, is expected to provide significantly better time resolution with respect to the extensively studied planar diamond sensors. We report on the development, production, and characterisation of innovative 3D diamond sensors achieving 30% improvement in both space and time resolution with respect to sensors from the previous generation. This is the first complete characterisation of the time resolution of 3D diamond sensors and combines results from tests with laser, ? rays and high energy particle beams. Plans and strategies for further improvement in the fabrication technology and readout systems are also discussed.

Any 3D tracking algorithm has to deal with occlusions: multiple targets get so close to each other that the loss of their identities becomes likely, hence potentially affecting the very quality of the data with interrupted trajectories and identity switches. Here, we present a novel tracking method that addresses the problem of occlusions within large groups of featureless objects by means of three steps: i) it represents each target as a cloud of points in 3D; ii) once a 3D cluster corresponding to an occlusion occurs, it defines a partitioning problem by introducing a cost function that uses both attractive and repulsive spatio-temporal proximity links; iii) it minimizes the cost function through a semi-definite optimization technique specifically designed to cope with the presence of multi-minima landscapes. The algorithm is designed to work on 3D data regardless of the experimental method used: multi--camera systems, lidars, radars and RGB-D systems. By performing tests on public data-sets, we show that the new algorithm produces a significant improvement over the state-of-the-art tracking methods, both by reducing the number of identity switches and by increasing the accuracy of the estimated positions of the targets in real space.

The order-of-arrival (OOA) of a stream of objects moving along a direction is important for decision making in logistic and industrial applications. As an example, the dispatch of luggage in airports requires to determine their OOA at check- points; as another example, the automatic steering of items in supply chains requires to sort and identify them while moving on a conveyor belt. This paper establishes a general framework for determining the OOA of objects that are moving along a monitored direction via radio-frequency identification (RFID) systems. Three techniques for OOA tracking are proposed and developed for objects equipped with RFID tags possessing ranging capabilities. The benefits of the proposed techniques are validated experimentally based on measurements gathered with an RFID system operating in an indoor environment.

Il presente lavoro di tesi si colloca in questo affascinante contesto. In esso viene de- scritto il software progettato; esso e in grado di fornire contenuti informativi aumentati ad un utente, al ne di aiutarlo nelle proprie mansioni lavorative quotidiane, e al contem- po viene descritto il monitoraggio delle sue prestazioni, salvando i tempi di esecuzione, per ogni singolo task somministratogli, assieme alle scelte compiute per soddisfarlo.

Tracking multiple moving targets allows quantitative measure of the dynamic behavior in systems as diverse as animal groups in biology, turbulence in fluid dynamics and crowd and traffic control. In three dimensions, tracking several targets becomes increasingly hard since optical occlusions are very likely, i.e., two featureless targets frequently overlap for several frames. Occlusions are particularly frequent in biological groups such as bird flocks, fish schools, and insect swarms, a fact that has severely limited collective animal behavior field studies in the past. This paper presents a 3D tracking method that is robust in the case of severe occlusions. To ensure robustness, we adopt a global optimization approach that works on all objects and frames at once. To achieve practicality and scalability, we employ a divide and conquer formulation, thanks to which the computational complexity of the problem is reduced by orders of magnitude. We tested our algorithm with synthetic data, with experimental data of bird flocks and insect swarms and with public benchmark datasets, and show that our system yields high quality trajectories for hundreds of moving targets with severe overlap. The results obtained on very heterogeneous data show the potential applicability of our method to the most diverse experimental situations.

Several automatic approaches have been proposed in the past to compute the refocus distance in digital holography (DH). However most of them are based on a maximization or minimization of a suitable amplitude image contrast measure, regarded as a function of the reconstruction distance parameter. Here we show that, by using the sparsity measure coefficient regarded as a refocusing criterion in the holographic reconstruction, it is possible to recover the focus plane and, at the same time, establish the degree of sparsity of digital holograms, when samples of the diffraction Fresnel propagation integral are used as a sparse signal representation. We employ a sparsity measurement coefficient known as Gini's index thus showing for the first time, to the best of our knowledge, its application in DH, as an effective refocusing criterion. Demonstration is provided for different holographic configurations (i.e., lens and lensless apparatus) and for completely different objects (i.e., a thin pure phase microscopic object as an in vitro cell, and macroscopic puppets) preparation. (C) 2014 Optical Society of America

A robust model predictive control algorithm solving the tracking and the infeasible reference problems for constrained systems subject to bounded disturbances is presented in this technical note. The proposed solution relies on three main concepts: 1) the reformulation of the system in the so-called velocity form to obtain offset-free tracking when constant disturbances are present, 2) the use of a tube-based approach to cope with non-constant but bounded disturbances, 3) the use of reference outputs as arguments of the optimization problem to cope with infeasible references. Convergence results are derived by suitably defining the auxiliary control law and the terminal set used in the problem formulation.

We present a novel radio-frequency identification (RFID) system with capability of localization and tracking of passive or semi-passive tags. Localization and tracking features are enabled by backscatter modulation on ultra-wide bandwidth tag's antenna. A ultra-high frequency signal allows the wake-up of the tags enabling the reduction of energy consumption and ensuring compatibility with existing RFID systems. The overall system as well as the reader and tag architectures are introduced. The localization and tracking performance evaluation is presented in some reference scenarios.

In questa tesi si presenta un sistema (MaD-WiSe) per la gestione efficiente di dati in reti di sensori senza fili (WSN) in scenari statici, e si forniscono diverse tecniche di ottimizzazione validate da risultati sperimentali su una rete di sensori reale. Si presenta inoltre un nuovo linguaggio dichiarativo (EQL) per esprimere eventi compositi da rilevare e tracciare in modo dinamico e autonomo e si fornisce uno schema di implementazione e un simulatore per la valutazione delle performance.

Evaluating Ambient Assisted Living (AAL) systems is challenging due to the complexity and variety of solutions adopted and services offered. EvAAL is an international competition aimed to address this problem by evaluating and assessing the AAL systems components, services and platforms. In 2011 took place the first edition of EvAAL on the special theme of Indoor Localization and Tracking for AAL. This paper describes the technical aspects of the first edition of EvAAL and draws a roadmap for the future editions.

This paper describes the approach adopted to carry out an evaluation of the STEEL eLearning system, developed for an Italian online university. The aim of the evaluation study was to identify the strengths and weaknesses of the system in view of innovative potential and impact. The core of the adopted approach is based on an adaptation of the Technology Acceptance Model (TAM), originally proposed by Davis (1989) as a theory to model how users of a new technological system accept it and take it on, based on two main acceptance factors: perceived usefulness and perceived ease-of-use. Although the TAM was not developed specifically for educational contexts, several authors have subsequently proposed and demonstrated how adaptations of the TAM theory can be used to evaluate the impact of technology even in these contexts. This paper is a step forward along this line of work to perform formative evaluation of eLearning systems. Though based on the TAM, our approach is also grounded on a couple of assumptions. The first is that data concerning actual use of the system and its effectiveness, in terms of concrete actions and achievements of its users, should also be considered, at least to complement and verify the data concerning users' perceptions. A second assumption is that the evaluation should consider all the phases of use of the system (course design, running and evaluation), all the users of the system (students, teachers, and eLearning management), and all the components of the system (the eLearning platform, the learning resources and, last but not least, the pedagogical approach underlying the eLearning system). The resulting model is a three-dimensional one (phases of use, users and components), with three aspects to be considered on each axis. For each of the 27 combinations of these aspects, indicators of usefulness and ease-of-use have been identified, as well as, when available, data concerning actual use (derived from the tracking functions of the platform) and data regarding effectiveness (based on teachers' adoption of new tools and students' exam results). The paper describes the model and its indicators, discusses its pros and cons based on its field test. A summary of the results of the evaluation are presented along with considerations about future research in the field.

In magnetic confinement Nuclear Fusion video cameras have become routine diagnostics, which can produce gigabytes of data per discharge. New tools and methods are required to manipulate the frames of these videos to obtain the required information. New algorithms have been developed, which implement pattern recognition methods to search the repositories of videos on the basis of their content and not on their address. Real time automatic analysis of videos has motivated the development of machine learning tools to classify objects in images. The identification and tracking of objects in the videos of Tokamak diagnostics requires the computation of image descriptors, which are insensitive to rotation, translation and rescaling. The Hu moments have proved to be quite effective in performing this image analysis task.

The movements of ten spiny lobsters, Palinurus elephas (Palinuridae) released in the Capo Gallo-Isola delle Femmine MPA (Italy) were monitored with ultrasonic telemetry. Four specimens left the area within the first two days. A nomadic movement pattern was detected for six lobsters which did not leave the rocky cliff.

While quantitative analysis of dynamic biological cell motions in vivo is of great biomedical interest, acquiring 3-D (plus time) information is difficult due to the lack of imaging tools with sufficient spatial and temporal resolution. A novel 3-D high-speed microscopic imaging system is developed to enable 3-D time series data acquisition, based on a defocusing technique (DDPIV). Depth coordinate Z is resolved by the triangular image patterns generated by a mask with three apertures forming an equilateral triangle. Application of this technique to microscale imaging is validated by calibration of targets spread over the image field. 1-?m fluorescent tracer particles are injected into the blood stream of 32h post-fertilization developing zebrafish embryos to help describe cardiac cell motions. 3-D and velocity fields of cardiovascular blood flow and trajectories of heart-wall motions are obtained.

An innovative and simple method, exploiting a pinhole camera to perform the sun tracking, is proposed. The tracking technique has been applied to a sunlight collection system, based on solar concentrators coupled to optical fibres. Using an equatorial configuration, the movements to align the optical collectors in the sun direction are preformed in two directions: on the temporal axis and on the declination axis. The methodology to track the sun position employs a double guiding system that uses two complementary procedures. The first one provides the preliminary orientation, then the second realises the fine positioning and adjustments. The first tracking system is of passive type and it drives the motors to correctly orient the head every day of the year. The second one is of dynamic type and it employs an optical pointing system. The core of this active tracking system is the sun pointer, which works as a double pinhole camera without lenses. The pointer has two sensors, with decreasing Field of View, that are used in sequence, improving the precisions of the sun tracking. The sun tracking technique has been applied to a system for illumination of internal spaces installed in a museum, where it has been tested in working conditions.

L'analisi delle sequenze video che mostrano il movimento dell'USV (Unmanned Space Vehicle) nella evoluzione della sua dinamica di ammaraggio è stata condotta al fine di determinare alcuni caratteri cinematici del moto dell'oggetto non facilmente determinabili con misuratori tradizionali. La stima di questi elementi del moto dell'USV è stata condotta mediante l'applicazione di due differenti metodi: l'analisi dei contorni dell'oggetto in movimento e il tracking di finestre all'interno dell'immagine. L'algoritmo di tracking è stato applicato sia su scala locale, ovvero il tracking di piccole finestre contenenti punti salienti dell'USV, sia su scala globale, ovvero il tracking dell'intero oggetto. L'interesse per le due scale è stato originato dalla incertezza riscontrata nella misura delle rotazioni di piccole finestre associate a punti singolari dell'USV. La motivazione alla base della scelta degli algoritmi riguarda la necessità di estrarre misure robuste della orientazione e della velocità angolare dell'USV, nonché la stima della velocità di impatto dei piani di coda al termine della sua evoluzione dinamica. Il software di tracking è stato esteso a più forme di mappatura della finestra di interrogazione (traslazionale, conforme, affine, omotetica o prospettica). La natura 2D delle immagini a disposizione (vista monoculare), l'assenza di elementi in grado di permettere una calibrazione, le condizioni di illuminazione della scena e la presenza di disturbi di varia origine non permettono di andare oltre una stima qualitativa delle grandezze investigate. Nonostante questo, l'analisi dei risultati mostra alcune potenzialità dei metodi proposti di un certo interesse, in particolare per quello che riguarda la stima sia delle velocità di impatto dei piani di coda sia delle velocità e delle accelerazioni angolari.