A travel-guidance engine for visually impaired people

Fino a pochi anni fa, usare i trasporti pubblici poteva essere fonte di confusione e richiedere la comprensione del sistema dei trasporti locali. Più tardi, con la diffusione di dispositivi con localizzazione GPS, reti dati cellulare e Google Maps (inizialmente Google Transit), tutto è cambiato, rendendo possibile la pianificazione di un viaggio mentre si è fuori casa. Nonostante Google Maps disponga di indicazioni stradali più o meno in tutto il mondo e mostri molte informazioni, alcune funzionalità, come l’integrazione degli orari in tempo reale, non sono disponibili in tutte le città, ma sono basate su accordi con le agenzie dei trasporti locali. GoGoBus è un’applicazione Android per l’ausilio al trasporto nella città di Bologna. Combinando diversi servizi, GoGoBus si rivolge a svariati tipi di utilizzatori: offre la pianificazione per i meno pratici del sistema e coloro che usano i trasporti pubblici raramente, dispone di orari in tempo reale per chi usa i mezzi frequentemente, e in più traccia la posizione dell’autobus, ha un supporto vocale e un’interfaccia semplice per persone con disabilità. Progettata appositamente per ipovedenti, l’aspetto più innovativo dell’applicazione è il suo supporto durante il percorso sull’autobus, integrato alla pianificazione del tragitto e agli orari aggiornati in tempo reale. Il sistema traccia la posizione dell’autobus attraverso il GPS del dispositivo mobile, la cui posizione è usata sia per riconoscere quando una fermata viene superata, sia per mostrare informazioni utili come la distanza dalla prossima fermata, il numero di fermate e i minuti rimanenti prima di scendere, e soprattutto notificare l’utente quando deve scendere. L’idea dietro GoGoBus è incrementare la fruibilità dei trasporti pubblici per non vedenti, ma anche per persone che li usano di rado, aumentando ampiamente la loro indipendenza, allo stesso tempo migliorando la qualità del servizio per chi usa i mezzi quotidianamente

Loop corrections and graceful exit in string cosmology

We examine the effect of perturbative string loops on the cosmological pre-big-bang evolution. We study loop corrections derived from heterotic string theory compactified on a $Z_N$ orbifold and we consider the effect of the all-order loop corrections to the Kahler potential and of the corrections to gravitational couplings, including both threshold corrections and corrections due to the mixed Kahler-gravitational anomaly. We find that string loops can drive the evolution into the region of the parameter space where a graceful exit is in principle possible, and we find solutions that, in the string frame, connect smoothly the superinflationary pre-big-bang evolution to a phase where the curvature and the derivative of the dilaton are decreasing. We also find that at a critical coupling the loop corrections to the Kahler potential induce a ghost-like instability, i.e. the kinetic term of the dilaton vanishes. This is similar to what happens in Seiberg-Witten theory and signals the transition to a new regime where the light modes in the effective action are different and are related to the original ones by S-duality. In a string context, this means that we enter a D-brane dominated phase.Comment: 39 pages, Latex, 17 eps figure

Blue Light Photobiomodulation as treatment for peristomal skin disorders: case series

Introduction. Keeping the peristomal skin intact proves to be a challenge for stoma patients and the health care teams that work with them. Peristomal skin complications are shown to affect 36.3% to 73.4% of patients. They are often particularly difficult to treat with topical therapies since the topical medications available are cream-based or ointment type formulations that don’t allow for perfect adhesion of the pouching system to the abdomen’s skin. In this study a preliminary evaluation of the effectiveness of Blue Light Photobiomodulation in the treatment of peristomal skin disorders was performed Methods. Patients carrying ostomy with lesions of types L2, L3, L4, L5, LX (SACS 2.0 classification5) that had not experienced an improvement in 4 weeks of standard therapy were selected for Blue Light therapy.  Blue Light treatment was performed twice a week for 4 weeks, in addition to standard therapy. Tissue repair was evaluated through Wound Bed Score and pain reduction. Results. All the 11 patients enrolled responded to Blue Light treatment with an average WBS improvement of 8.3 points and a significant reduction in pain. Blue Light Photobiomodulation proved decisive in activating the healing process in three patients with pyoderma gangrenous. Conclusions. The positive clinical results suggests that Blue Light Photobiomodulation could be a promising tool in the management of peristomal skin lesions

modeling small scale solar powered orc unit for standalone application

When the electricity from the grid is not available, the generation of electricity in remote areas is an essential challenge to satisfy important needs. In many developing countries the power generation from Diesel engines is the applied technical solution. However the cost and supply of fuel make a strong dependency of the communities on the external support. Alternatives to fuel combustion can be found in photovoltaic generators, and, with suitable conditions, small wind turbines or microhydroplants. The aim of the paper is to simulate the power generation of a generating unit using the Rankine Cycle and using refrigerant R245fa as a working fluid. The generation unit has thermal solar panels as heat source and photovoltaic modules for the needs of the auxiliary items (pumps, electronics, etc.). The paper illustrates the modeling of the system using TRNSYS platform, highlighting standard and "ad hoc" developed components as well as the global system efficiency. In the future the results of the simulation will be compared with the data collected from the 3 kW prototype under construction in the Tuscia University in Italy

Robust Monocular Depth Estimation under Challenging Conditions

While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather conditions, such as at nighttime or in the presence of rain. In this paper, we uncover these safety-critical issues and tackle them with md4all: a simple and effective solution that works reliably under both adverse and ideal conditions, as well as for different types of learning supervision. We achieve this by exploiting the efficacy of existing methods under perfect settings. Therefore, we provide valid training signals independently of what is in the input. First, we generate a set of complex samples corresponding to the normal training ones. Then, we train the model by guiding its self- or full-supervision by feeding the generated samples and computing the standard losses on the corresponding original images. Doing so enables a single model to recover information across diverse conditions without modifications at inference time. Extensive experiments on two challenging public datasets, namely nuScenes and Oxford RobotCar, demonstrate the effectiveness of our techniques, outperforming prior works by a large margin in both standard and challenging conditions. Source code and data are available at: https://md4all.github.io.Comment: ICCV 2023. Source code and data: https://md4all.github.i

Signal Clustering with Class-independent Segmentation

Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clustering method, which encodes concurrent signals into images, and, for the first time, tackles clustering with image segmentation. Novel loss functions are introduced to optimize a Neural Network to separate the input pulses into pure and non-fragmented clusters. Outperforming a variety of baselines, the proposed approach is capable of clustering inputs directly with a Neural Network, in an end-to-end fashion.Comment: Under Review for IEEE ICASSP 202

Anisotropic String Cosmology at Large Curvatures

We study the effect of the antisymmetric tensor field $B_{\mu\nu}$ on the large curvature phase of string cosmology. It is well-known that a non-vanishing value of $H=dB$ leads to an anisotropic expansion of the spatial dimensions. Correspondingly, in the string phase of the model, including $\alpha '$ corrections, we find anisotropic fixed points of the evolution, which act as regularizing attractors of the lowest order solutions. The attraction basin can also include isotropic initial conditions for the scale factors. We present explicit examples at order $\alpha '$ for different values of the number of spatial dimensions and for different ans\"{a}tze for $H$.Comment: 16 pages, Latex, 2 figure

Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds

Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems. In this paper, we present Panoster, a novel proposal-free panoptic segmentation method for LiDAR point clouds. Unlike previous approaches relying on several steps to group pixels or points into objects, Panoster proposes a simplified framework incorporating a learning-based clustering solution to identify instances. At inference time, this acts as a class-agnostic segmentation, allowing Panoster to be fast, while outperforming prior methods in terms of accuracy. Without any post-processing, Panoster reached state-of-the-art results among published approaches on the challenging SemanticKITTI benchmark, and further increased its lead by exploiting heuristic techniques. Additionally, we showcase how our method can be flexibly and effectively applied on diverse existing semantic architectures to deliver panoptic predictions.Comment: Preprint of IEEE RA-L articl

Segmenting Known Objects and Unseen Unknowns without Prior Knowledge

Panoptic segmentation methods assign a known class to each pixel given in input. Even for state-of-the-art approaches, this inevitably enforces decisions that systematically lead to wrong predictions for objects outside the training categories. However, robustness against out-of-distribution samples and corner cases is crucial in safety-critical settings to avoid dangerous consequences. Since real-world datasets cannot contain enough data points to adequately sample the long tail of the underlying distribution, models must be able to deal with unseen and unknown scenarios as well. Previous methods targeted this by re-identifying already-seen unlabeled objects. In this work, we propose the necessary step to extend segmentation with a new setting which we term holistic segmentation. Holistic segmentation aims to identify and separate objects of unseen unknown categories into instances, without any prior knowledge about them, while performing panoptic segmentation of known classes. We tackle this new problem with U3HS, which finds unknowns as highly uncertain regions and clusters their corresponding instance-aware embeddings into individual objects. By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios. Extensive experiments on public data from MS COCO, Cityscapes, and Lost&Found demonstrate the effectiveness of U3HS for this new, challenging, and assumptions-free setting called holistic segmentation.Comment: Accepted at ICCV 202