An Intelligent Cooperative Visual Sensor Network for Urban Mobility.

Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

Detection and localization of gold nanoshells inside cells: near-field approximation.

The optical properties of metal nanoparticles play a fundamental role for their use in a wide range of applications. In hyperthermia treatment, for example, gold nanoshells (NSs, dielectric core+gold shell) pre-embedded in a cancer cell absorb energy when exposed to appropriate wavelengths of a laser beam and heat up, thereby destroying the cancer cell. In this process, nevertheless, healthy tissues (not targeted by the NSs) along the laser path are not affected; this is because most biological soft tissues have a relatively low light absorption coefficient in the near-infrared (NIR) regions-a characteristic known as the tissue optical window. Over such a window, NIR light transmits through the tissues with scattering-limited attenuation and minimal heating, thereby avoiding damage to healthy tissues. As a consequence, the identification of NSs assumed a fundamental role for the further development of such cancer treatment. Recently, we have demonstrated the possibility to identify 100-150 nm diameter gold NSs inside mouse cells using a scanning near-optical microscope (SNOM). In this paper, we provide a numerical demonstration that the SNOM is able to locate NSs inside the cell with a particle-aperture distance of about 100 nm. This result was obtained by developing an analytical approach based on the calculation of the dyadic Green function in the near-field approximation. The implications of our findings will remarkably affect further investigations on the interaction between NSs and biological systems.

N400-like responses to three-chord harmonic sequences with unexpected out of key endings: scalp topography, cortical sources, and perspectives for a clinical use.

A series of ERP components, each provided with both a precise timing with respect to stimulation and a specific cortical localization, reflects the temporal succession of processing stages of music information. This makes the musical stimulus potentially usable to probe residual brain functions in non-communicating patients with disorders of consciousness. In an attempt to find a simple stimulation protocol that was suitable for use in a clinical setting, the purpose of this study was to verify whether a minimum-length musical stimulus, provided with a definite music-syntactic connotation, was still able to elicit musical ERPs in a group of eight healthy subjects. The stimulus was composed of the minimum number of chords necessary and sufficient to enable the subject to predict a plausible closure of the sequence (priming) and, at the same time, to provide him/her with the closing chord of the sequence (target), either congruous (probable closing) or not (improbable closing) to the tonal context. The subject's task was to discriminate and recognize the irregular targets. The components that were expected to be elicited, in this experimental situation, were ERAN, N5, P600/LPC. Conversely, in addition to these former components, we unexpectedly observed a N400-like component. To determine whether this component was a real N400, we submitted our data to a sLORETA analysis in order to identify its cortical generators. Irregular chords showed higher current densities with respect to regular ones on the right-sided medial and superior temporal gyri, superior and inferior parietal lobules, fusiform and parahippocampal gyri, and on the bilateral posterior cingulate cortex. In particular, the N400-like wave seems to share with the word-primed music-elicited N400 certain generators that are located in cortical areas BA 21/37 and BA 22. This suggests that even chord-primed chord targets can convey extra-musical meanings and that, consequently, they might be useful in assessing residual higher-order information-processing capabilities in non-communicating patients with disorders of consciousness.

Efficient Lung Ultrasound Classification.

A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.

A Pilot Study of Infrared Thermography Based Assessment of Local Skin Temperature Response in Overweight and Lean Women during Oral Glucose Tolerance Test.

Obesity is recognized as a major public health issue, as it is linked to the increased risk of severe pathological conditions. The aim of this pilot study is to evaluate the relations between adiposity (and biophysical characteristics) and temperature profiles under thermoneutral conditions in normal and overweight females, investigating the potential role of heat production/dissipation alteration in obesity. We used Infrared Thermography (IRT) to evaluate the thermogenic response to a metabolic stimulus performed with an oral glucose tolerance test (OGTT). Thermographic images of the right hand and of the central abdomen (regions of interests) were obtained basally and during the oral glucose tolerance test (3 h OGTT with the ingestion of 75 g of oral glucose) in normal and overweight females. Regional temperature vs BMI, % of body fat and abdominal skinfold were statistically compared between two groups. The study showed that mean abdominal temperature was significantly greater in lean than overweight participants (34.11 ± 0.70 °C compared with 32.92 ± 1.24 °C, p < 0.05). Mean hand temperature was significantly greater in overweight than lean subjects (31.87 ± 3.06 °C compared with 28.22 ± 3.11 °C, p < 0.05). We observed differences in temperature profiles during OGTT between lean and overweight subjects: The overweight individuals depict a flat response as compared to the physiological rise observed in lean individuals. This observed difference in thermal pattern suggests an energy rate imbalance towards nutrients storage of the overweight subjects.

Investigating the Role of Model-Based and Model-Free Imaging Biomarkers as Early Predictors of Neoadjuvant Breast Cancer Therapy Outcome.

Imaging biomarkers (IBs) play a critical role in the clinical management of breast cancer (BRCA) patients throughout the cancer continuum for screening, diagnosis, and therapy assessment, especially in the neoadjuvant setting. However, certain model-based IBs suffer from significant variability due to the complex workflows involved in their computation, whereas model-free IBs have not been properly studied regarding clinical outcome. In this study, IBs from 35 BRCA patients who received neoadjuvant chemotherapy (NAC) were extracted from dynamic contrast-enhanced MR imaging (DCE-MRI) data with two different approaches, a model-free approach based on pattern recognition (PR), and a model-based one using pharmacokinetic compartmental modeling. Our analysis found that both model-free and model-based biomarkers can predict pathological complete response (pCR) after the first cycle of NAC. Overall, eight biomarkers predicted the treatment response after the first cycle of NAC, with statistical significance (p-value < 0.05), and three at the baseline. The best pCR predictors at first follow-up, achieving high AUC and sensitivity and specificity more than 50%, were the hypoxic component with threshold 2 (AUC 90.4%) from the PR method, and the median value of kep (AUC 73.4%) from the model-based approach. Moreover, the 80th percentile of ve achieved the highest pCR prediction at baseline with AUC 78.5%. The results suggest that the model-free DCE-MRI IBs could be a more robust alternative to complex, model-based ones such as kep and favor the hypothesis that the PR image-derived hypoxic image component captures actual tumor hypoxia information able to predict BRCA NAC outcome.

Assessment of hand superficial oxygenation during ischemia/reperfusion in healthy subjects versus systemic sclerosis patients by 2D near infrared spectroscopic imaging.

BACKGROUND AND OBJECTIVE: Patients affected by systemic sclerosis (SSc) develop functional and structural microcirculatory dysfunction, which progressively evolves towards systemic tissue fibrosis (sclerosis). Disease initially affects distal extremities, which become preferential sites of diagnostic scrutiny. This pilot investigation tested the hypothesis that peripheral microcirculatory dysfunction in SSc could be non-invasively assessed by 2D Near Infrared Spectroscopic (NIRS) imaging of the hand associated with Vascular Occlusion Testing (VOT). NIRS allows measurement of hemoglobin oxygen saturation (StO2) in the blood perfusing the volume tissue under scrutiny. METHODS: In five normal volunteers and five SSc patients we applied a multispectral oximetry imaging device (Kent camera, Kent Imaging, Calgary, Canada) to acquire StO2 2D maps of the whole hand palm during baseline, ischemia and reperfusion phase. RESULTS: We found significant differences between controls and SSc patients in basal StO2 (82.80 ±â€¯2.51 vs 65.44 ±â€¯7.96%, p = 0.0016), minimum StO2 (59.35 ±â€¯4.29 vs 40.73 ±â€¯6.47%, p = 0.0007), final StO2 (83.83 ±â€¯4.09 vs 68.84 ±â€¯11.41%, p = 0.02) and time to maximum StO2 (40 ±â€¯12.25 vs 62 ±â€¯4.47 s, p = 0.005). CONCLUSIONS: This is, to our knowledge, the first application of 2D NIRS imaging of the whole hand to the investigation of microvascular dysfunction in systemic sclerosis. The image processing presented here considered the StO2 in the entire hand allowing a comprehensive view of the spatial heterogeneity of microvascular dysfunction.

A proactive system for maritime environment monitoring.

The ability to remotely detect and monitor oil spills is becoming increasingly important due to the high demand of oil-based products. Indeed, shipping routes are becoming very crowded and the likelihood of oil slick occurrence is increasing. In this frame, a fully integrated remote sensing system can be a valuable monitoring tool. We propose an integrated and interoperable system able to monitor ship traffic and marine operators, using sensing capabilities from a variety of electronic sensors, along with geo-positioning tools, and through a communication infrastructure. Our system is capable of transferring heterogeneous data, freely and seamlessly, between different elements of the information system (and their users) in a consistent and usable form. The system also integrates a collection of decision support services providing proactive functionalities. Such services demonstrate the potentiality of the system in facilitating dynamic links among different data, models and actors, as indicated by the performed field tests.

A simulation model for analysing brain structure deformations.

Recent developments of medical software applications--from the simulation to the planning of surgical operations--have revealed the need for modelling human tissues and organs, not only from a geometric point of view but also from a physical one, i.e. soft tissues, rigid body, viscoelasticity, etc. This has given rise to the term 'deformable objects', which refers to objects with a morphology, a physical and a mechanical behaviour of their own and that reflects their natural properties. In this paper, we propose a model, based upon physical laws, suitable for the realistic manipulation of geometric reconstructions of volumetric data taken from MR and CT scans. In particular, a physically based model of the brain is presented that is able to simulate the evolution of different nature pathological intra-cranial phenomena such as haemorrhages, neoplasm, haematoma, etc and to describe the consequences that are caused by their volume expansions and the influences they have on the anatomical and neuro-functional structures of the brain.

Brain volumes characterisation using hierarchical neural networks.

Objective knowledge of tissue density distribution in CT/MRI brain datasets can be related to anatomical or neuro-functional regions for assessing pathologic conditions characterised by slight differences. The process of monitoring illness and its treatment could be then improved by a suitable detection of these variations. In this paper, we present an approach for three-dimensional (3D) classification of brain tissue densities based on a hierarchical artificial neural network (ANN) able to classify the single voxels of the examined datasets. The method developed was tested on case studies selected by an expert neuro-radiologist and consisting of both normal and pathological conditions. The results obtained were submitted for validation to a group of physicians and they judged the system to be really effective in practical applications.

A wize mirror for lifestyle improvement.

This paper discusses the problem of fostering lifestyle changes towards healthier habits via tailored user guidance. We present a novel multisensory device, the Wize Mirror, which will be able to detect semeiotic face signs related to cardio-metabolic risk, and encourage users to reduce their risk by improving their lifestyle. Offering a proper user guidance requires solving three main issues: user profiling, definition of a wellness index based on biophysical data, and personalized guidance by means of coaching and supportive messages. For each of these issues, the solutions proposed in the EU FP7 Project SEMEOTICONS are presented, highlighting their advantages with respect to the state-of-the-art.

Decision support in heart failure through processing of electro- and echocardiograms.

OBJECTIVE: Signal and imaging investigations are currently key components in the diagnosis, prognosis and follow up of heart diseases. Nowadays, the need for more efficient, cost-effective and personalised care has led to a renaissance of clinical decision support systems (CDSSs). The purpose of this paper is to present an effective way of achieving a high-level integration of signal and image processing methods in the general process of care, by means of a clinical decision support system, and to discuss the advantages of such an approach. From the wide range of heart diseases, heart failure, whose complexity best highlights the benefits of this integration, has been selected. METHODS: After an analysis of users' needs and expectations, significant and suitably designed image and signal processing algorithms are introduced to objectively and reliably evaluate important features involved in decisional problems in the heart failure domain. Then, a CDSS is conceived so as to combine the domain knowledge with advanced analytical tools for data processing. In particular, the relevant and significant medical knowledge and experts' knowhow are formalised according to an ontological formalism, suitably augmented with a base of rules for inferential reasoning. RESULTS: The proposed methods were tested and evaluated in the daily practice of the physicians operating at the Department of Cardiology, University Magna Graecia, Catanzaro, Italy, on a population of 79 patients. Different scenarios, involving decisional problems based on the analysis of biomedical signals and images, were considered. In these scenarios, after some training and 3 months of use, the CDSS was able to provide important and useful suggestions in routine workflows, by integrating the clinical parameters computed through the developed methods for echocardiographic image segmentation and the algorithms for electrocardiography processing. CONCLUSIONS: The CDSS allows the integration of signal and image processing algorithms into the general process of care. Feedback from end-users has been positive.