Nuova ricerca

Silvia CASCIANELLI

Ricercatore t.d. art. 24 c. 3 lett. A
Dipartimento di Ingegneria "Enzo Ferrari"

Home | Didattica |


Pubblicazioni

2023 - Embodied Agents for Efficient Exploration and Smart Scene Description [Relazione in Atti di Convegno]
Bigazzi, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

2023 - Evaluating synthetic pre-Training for handwriting processing tasks [Articolo su rivista]
Pippi, V.; Cascianelli, S.; Baraldi, L.; Cucchiara, R.
abstract

In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision sig-nal. We use it to train a simple convolutional neural network (ConvNet) with a fully supervised objective. The vector representations of the images obtained from the pre-trained ConvNet can then be consid-ered as encodings of the handwriting style. We exploit such representations for Writer Retrieval, Writer Identification, Writer Verification, and Writer Classification and demonstrate that our pre-training strat-egy allows extracting rich representations of the writers' style that enable the aforementioned tasks with competitive results with respect to task-specific State-of-the-Art approaches.& COPY; 2023 Elsevier B.V. All rights reserved.

2023 - From Show to Tell: A Survey on Deep Learning-based Image Captioning [Articolo su rivista]
Stefanini, Matteo; Cornia, Marcella; Baraldi, Lorenzo; Cascianelli, Silvia; Fiameni, Giuseppe; Cucchiara, Rita
abstract

2023 - Towards Explainable Navigation and Recounting [Relazione in Atti di Convegno]
Poppi, Samuele; Rawal, Niyati; Bigazzi, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

Explainability and interpretability of deep neural networks have become of crucial importance over the years in Computer Vision, concurrently with the need to understand increasingly complex models. This necessity has fostered research on approaches that facilitate human comprehension of neural methods. In this work, we propose an explainable setting for visual navigation, in which an autonomous agent needs to explore an unseen indoor environment while portraying and explaining interesting scenes with natural language descriptions. We combine recent advances in ongoing research fields, employing an explainability method on images generated through agent-environment interaction. Our approach uses explainable maps to visualize model predictions and highlight the correlation between the observed entities and the generated words, to focus on prominent objects encountered during the environment exploration. The experimental section demonstrates that our approach can identify the regions of the images that the agent concentrates on to describe its point of view, improving explainability.

2022 - Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions [Articolo su rivista]
Cascianelli, Silvia; Cornia, Marcella; Baraldi, Lorenzo; Cucchiara, Rita
abstract

Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and reuse of their content. The task becomes even more challenging when dealing with historical documents due to the variability of the writing style and degradation of the page quality. State-of-the-art HTR approaches typically couple recurrent structures for sequence modeling with Convolutional Neural Networks for visual feature extraction. Since convolutional kernels are defined on fixed grids and focus on all input pixels independently while moving over the input image, this strategy disregards the fact that handwritten characters can vary in shape, scale, and orientation even within the same document and that the ink pixels are more relevant than the background ones. To cope with these specific HTR difficulties, we propose to adopt deformable convolutions, which can deform depending on the input at hand and better adapt to the geometric variations of the text. We design two deformable architectures and conduct extensive experiments on both modern and historical datasets. Experimental results confirm the suitability of deformable convolutions for the HTR task.

2022 - CaMEL: Mean Teacher Learning for Image Captioning [Relazione in Atti di Convegno]
Barraco, Manuele; Stefanini, Matteo; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

2022 - Embodied Navigation at the Art Gallery [Relazione in Atti di Convegno]
Bigazzi, Roberto; Landi, Federico; Cascianelli, Silvia; Cornia, Marcella; Baraldi, Lorenzo; Cucchiara, Rita
abstract

Embodied agents, trained to explore and navigate indoor photorealistic environments, have achieved impressive results on standard datasets and benchmarks. So far, experiments and evaluations have involved domestic and working scenes like offices, flats, and houses. In this paper, we build and release a new 3D space with unique characteristics: the one of a complete art museum. We name this environment ArtGallery3D (AG3D). Compared with existing 3D scenes, the collected space is ampler, richer in visual features, and provides very sparse occupancy information. This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information. Additionally, we annotate the coordinates of the main points of interest inside the museum, such as paintings, statues, and other items. Thanks to this manual process, we deliver a new benchmark for PointGoal navigation inside this new space. Trajectories in this dataset are far more complex and lengthy than existing ground-truth paths for navigation in Gibson and Matterport3D. We carry on extensive experimental evaluation using our new space for evaluation and prove that existing methods hardly adapt to this scenario. As such, we believe that the availability of this 3D model will foster future research and help improve existing solutions.

2022 - Focus on Impact: Indoor Exploration with Intrinsic Motivation [Articolo su rivista]
Bigazzi, Roberto; Landi, Federico; Cascianelli, Silvia; Baraldi, Lorenzo; Cornia, Marcella; Cucchiara, Rita
abstract

2022 - Investigating Bidimensional Downsampling in Vision Transformer Models [Relazione in Atti di Convegno]
Bruno, Paolo; Amoroso, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

Vision Transformers (ViT) and other Transformer-based architectures for image classification have achieved promising performances in the last two years. However, ViT-based models require large datasets, memory, and computational power to obtain state-of-the-art results compared to more traditional architectures. The generic ViT model, indeed, maintains a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. With the goal of increasing the efficiency of Transformer-based models, we explore the application of a 2D max-pooling operator on the outputs of Transformer encoders. We conduct extensive experiments on the CIFAR-100 dataset and the large ImageNet dataset and consider both accuracy and efficiency metrics, with the final goal of reducing the token sequence length without affecting the classification performance. Experimental results show that bidimensional downsampling can outperform previous classification approaches while requiring relatively limited computation resources.

2022 - Spot the Difference: A Novel Task for Embodied Agents in Changing Environments [Relazione in Atti di Convegno]
Landi, Federico; Bigazzi, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

2022 - The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition [Relazione in Atti di Convegno]
Cascianelli, Silvia; Pippi, Vittorio; Maarand, Martin; Cornia, Marcella; Baraldi, Lorenzo; Kermorvant, Christopher; Cucchiara, Rita
abstract

2022 - The Unreasonable Effectiveness of CLIP features for Image Captioning: an Experimental Analysis [Relazione in Atti di Convegno]
Barraco, Manuele; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

2022 - Wind Turbine Power Curve Monitoring Based on Environmental and Operational Data [Articolo su rivista]
Cascianelli, S.; Astolfi, D.; Castellani, F.; Cucchiara, R.; Fravolini, M. L.
abstract

The power produced by a wind turbine depends on environmental conditions, working parameters, and interactions with nearby turbines. However, these aspects are often neglected in the design of data-driven models for wind farms' performance analysis. In this article, we propose to predict the active power and to provide reliable prediction intervals via ensembles of multivariate polynomial regression models that exploit a higher number of inputs (compared to most approaches in the literature), including operational and thermal variables. We present two main strategies: the former considers the environmental measurements collected at the other wind turbines in the farm as additional modeling information for the turbine under analysis; the latter combines multiple models relative to different operative conditions. We validate our approach on real data from the SCADA system of a wind farm in Italy and obtain a MAE of the order of 1.0% of the rated power of the turbine. Moreover, due to the structure of our approach, we can gain quantitative insights on the covariates most frequently selected depending on the working region of the wind turbines.

2021 - Data‐based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering [Articolo su rivista]
Cascianelli, Silvia; Costante, Gabriele; Crocetti, Francesco; Ricci, Elisa; Valigi, Paolo; Luca Fravolini, Mario
abstract

2021 - Explore and Explain: Self-supervised Navigation and Recounting [Relazione in Atti di Convegno]
Bigazzi, Roberto; Landi, Federico; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

Embodied AI has been recently gaining attention as it aims to foster the development of autonomous and intelligent agents. In this paper, we devise a novel embodied setting in which an agent needs to explore a previously unknown environment while recounting what it sees during the path. In this context, the agent needs to navigate the environment driven by an exploration goal, select proper moments for description, and output natural language descriptions of relevant objects and scenes. Our model integrates a novel self-supervised exploration module with penalty, and a fully-attentive captioning model for explanation. Also, we investigate different policies for selecting proper moments for explanation, driven by information coming from both the environment and the navigation. Experiments are conducted on photorealistic environments from the Matterport3D dataset and investigate the navigation and explanation capabilities of the agent as well as the role of their interactions.

2021 - Learning to Read L'Infinito: Handwritten Text Recognition with Synthetic Training Data [Relazione in Atti di Convegno]
Cascianelli, Silvia; Cornia, Marcella; Baraldi, Lorenzo; Piazzi, Maria Ludovica; Schiuma, Rosiana; Cucchiara, Rita
abstract

Deep learning-based approaches to Handwritten Text Recognition (HTR) have shown remarkable results on publicly available large datasets, both modern and historical. However, it is often the case that historical manuscripts are preserved in small collections, most of the time with unique characteristics in terms of paper support, author handwriting style, and language. State-of-the-art HTR approaches struggle to obtain good performance on such small manuscript collections, for which few training samples are available. In this paper, we focus on HTR on small historical datasets and propose a new historical dataset, which we call Leopardi, with the typical characteristics of small manuscript collections, consisting of letters by the poet Giacomo Leopardi, and devise strategies to deal with the training data scarcity scenario. In particular, we explore the use of carefully designed but cost-effective synthetic data for pre-training HTR models to be applied to small single-author manuscripts. Extensive experiments validate the suitability of the proposed approach, and both the Leopardi dataset and synthetic data will be available to favor further research in this direction.

2021 - Out of the Box: Embodied Navigation in the Real World [Relazione in Atti di Convegno]
Bigazzi, Roberto; Landi, Federico; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
abstract

The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances? In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world.

2021 - Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions [Relazione in Atti di Convegno]
Cojocaru, Iulian; Cascianelli, Silvia; Baraldi, Lorenzo; Corsini, Massimiliano; Cucchiara, Rita
abstract

Handwritten Text Recognition (HTR) in free-layout pages is a valuable yet challenging task which aims to automatically understand handwritten texts. State-of-the-art approaches in this field usually encode input images with Convolutional Neural Networks, whose kernels are typically defined on a fixed grid and focus on all input pixels independently. However, this is in contrast with the sparse nature of handwritten pages, in which only pixels representing the ink of the writing are useful for the recognition task. Furthermore, the standard convolution operator is not explicitly designed to take into account the great variability in shape, scale, and orientation of handwritten characters. To overcome these limitations, we investigate the use of deformable convolutions for handwriting recognition. This type of convolution deform the convolution kernel according to the content of the neighborhood, and can therefore be more adaptable to geometric variations and other deformations of the text. Experiments conducted on the IAM and RIMES datasets demonstrate that the use of deformable convolutions is a promising direction for the design of novel architectures for handwritten text recognition.

2020 - Classification model to estimate MIB-1 (Ki 67) proliferation index in NSCLC patients evaluated with 18F-FDG-PET/CT [Articolo su rivista]
Palumbo, B.; Capozzi, R.; Bianconi, F.; Fravolini, M. L.; Cascianelli, S.; Messina, S. G.; Bellezza, G.; Sidoni, A.; Puma, F.; Ragusa, M.
abstract

Background/Aim: Proliferation biomarkers such as MIB-1 are strong predictors of clinical outcome and response to therapy in patients with non-small-cell lung cancer, but they require histological examination. In this work, we present a classification model to predict MIB-1 expression based on clinical parameters from positron emission tomography. Patients and Methods: We retrospectively evaluated 78 patients with histology-proven non-small-cell lung cancer (NSCLC) who underwent 18F-FDG-PET/CT for clinical examination. We stratified the population into a low and high proliferation group using MIB-1=25% as cut-off value. We built a predictive model based on binary classification trees to estimate the group label from the maximum standardized uptake value (SUVmax) and lesion diameter. Results: The proposed model showed ability to predict the correct proliferation group with overall accuracy >82% (78% and 86% for the low- and high-proliferation group, respectively). Conclusion: Our results indicate that radiotracer activity evaluated via SUVmax and lesion diameter are correlated with tumour proliferation index MIB-1.

2020 - Combining Domain Adaptation and Spatial Consistency for Unseen Fruits Counting: A Quasi-Unsupervised Approach [Articolo su rivista]
Bellocchio, E.; Costante, G.; Cascianelli, S.; Fravolini, M. L.; Valigi, P.
abstract

Autonomous robotic platforms can be effectively used to perform automatic fruits yield estimation. To this aim, robots need data-driven models that process image streams and count, even approximately, the number of fruits in an orchard. However, training such models following a supervised paradigm is expensive and unpractical. Extending pre-trained models to perform yield estimation for a completely new type of fruit is even more challenging, but interesting since this situation is typical in practice. In this work, we combine a State-of-the-Art weakly-supervised fruit counting model with an unsupervised style transfer method for addressing the task above. In this sense, our proposed approach is quasi-unsupervised. In particular, we use a Cycle-Generative Adversarial Network (C-GAN) to perform unsupervised domain adaptation and train it alongside with a Presence-Absence Classifier (PAC) that discriminates images containing fruits or not. The PAC produces the weak-supervision signal for the counting network, that can then be used on the target orchard directly. Experiments on datasets collected in four different orchards show that the proposed approach is more accurate than the supervised baseline methods.

2020 - The Role of the Input in Natural Language Video Description [Articolo su rivista]
Cascianelli, Silvia; Costante, Gabriele; Devo, Alessandro; Ciarfuglia, Thomas A; Valigi, Paolo; Fravolini, Mario L
abstract

2020 - [123 I] Metaiodobenzylguanidine (MIBG) Cardiac Scintigraphy and Automated Classification Techniques in Parkinsonian Disorders [Articolo su rivista]
Nuvoli, Susanna; Spanu, Angela; Fravolini Mario, Luca; Bianconi, Francesco; Cascianelli, Silvia; Madeddu, Giuseppe; Palumbo, Barbara
abstract

Purpose: To provide reliable and reproducible heart/mediastinum (H/M) ratio cut-off values for parkinsonian disorders using two machine learning techniques, Support Vector Machines (SVM) and Random Forest (RF) classifier, applied to [123I]MIBG cardiac scintigraphy. Procedures: We studied 85 subjects, 50 with idiopathic Parkinson’s disease, 26 with atypical Parkinsonian syndromes (P), and 9 with essential tremor (ET). All patients underwent planar early and delayed cardiac scintigraphy after [123I]MIBG (111 MBq) intravenous injection. Images were evaluated both qualitatively and quantitatively; the latter by the early and delayed H/M ratio obtained from regions of interest (ROIt1 and ROIt2) drawn on planar images. SVM and RF classifiers were finally used to obtain the correct cut-off value. Results: SVM and RF produced excellent classification performances: SVM classifier achieved perfect classification and RF also attained very good accuracy. The better cut-off for H/M value was 1.55 since it remains the same for both ROIt1 and ROIt2. This value allowed to correctly classify PD from P and ET: patients with H/M ratio less than 1.55 were classified as PD while those with values higher than 1.55 were considered as affected by parkinsonism and/or ET. No difference was found when early or late H/M ratio were considered separately thus suggesting that a single early evaluation could be sufficient to obtain the final diagnosis. Conclusions: Our results evidenced that the use of SVM and CT permitted to define the better cut-off value for H/M ratios both in early and in delayed phase thus underlining the role of [123I]MIBG cardiac scintigraphy and the effectiveness of H/M ratio in differentiating PD from other parkinsonism or ET. Moreover, early scans alone could be used for a reliable diagnosis since no difference was found between early and late. Definitely, a larger series of cases is needed to confirm this data.

2019 - A SCADA-Based Method for Estimating the Energy Improvement from Wind Turbine Retrofitting [Relazione in Atti di Convegno]
Astolfi, D; Castellani, F; Fravolini, Ml; Cascianelli, S; Terzi, L
abstract

2019 - Data-Based Design of Robust Fault Isolation Residuals Using LASSO optimization [Relazione in Atti di Convegno]
Cascianelli, Silvia; Crocetti, Francesco; Costante, Gabriele; Valigi, Paolo; Fravolini, Mario Luca
abstract

2019 - Experimental Prediction Intervals for Monitoring Wind Turbines: an Ensemble Approach [Relazione in Atti di Convegno]
Cascianelli, Silvia; Astolfi, Davide; Costante, Gabriele; Castellani, Francesco; Fravolini, Mario Luca
abstract

2019 - Precision computation of wind turbine power upgrades: An aerodynamic and control optimization test case [Articolo su rivista]
Astolfi, D.; Castellani, F.; Fravolini, M. L.; Cascianelli, S.; Terzi, L.
abstract

Wind turbine upgrades have recently been spreading in the wind energy industry for optimizing the efficiency of the wind kinetic energy conversion. These interventions have material and labor costs; therefore, it is fundamental to estimate the production improvement realistically. Furthermore, the retrofitting of the wind turbines sited in complex environments might exacerbate the stress conditions to which those are subjected and consequently might affect the residual life. In this work, a two-step upgrade on a multimegawatt wind turbine is considered from a wind farm sited in complex terrain. First, vortex generators and passive flow control devices have been installed. Second, the management of the revolutions per minute has been optimized. In this work, a general method is formulated for assessing the wind turbine power upgrades using operational data. The method is based on the study of the residuals between the measured power output and a judicious model of the power output itself, before and after the upgrade. Therefore, properly selecting the model is fundamental. For this reason, an automatic feature selection algorithm is adopted, based on the stepwise multivariate regression. This allows identifying the most meaningful input variables for a multivariate linear model whose target is the power of the upgraded wind turbine. For the test case of interest, the adopted upgrade is estimated to increase the annual energy production to 2.660.1%. The aerodynamic and control upgrades are estimated to be 1.8% and 0.8%, respectively, of the production improvement.

2018 - Dimensionality reduction strategies for cnn-based classification of histopathological images [Relazione in Atti di Convegno]
Cascianelli, Silvia; Bello-Cerezo, Raquel; Bianconi, Francesco; Fravolini, Mario L; Belal, Mehdi; Palumbo, Barbara; Kather, Jakob N
abstract

2018 - Full-GRU Natural Language Video Description for Service Robotics Applications [Articolo su rivista]
Cascianelli, Silvia; Costante, Gabriele; Ciarfuglia, Thomas Alessandro; Valigi, Paolo; Fravolini, Mario Luca
abstract

2018 - Hand-designed local image descriptors vs. off-the-shelf CNN-based features for texture classification: an experimental comparison [Relazione in Atti di Convegno]
Bello-Cerezo, Raquel; Bianconi, Francesco; Cascianelli, Silvia; Fravolini, Mario Luca; Di Maria, Francesco; Smeraldi, Fabrizio
abstract

2018 - Visual Localization in the Presence of Appearance Changes Using the Partial Order Kernel [Relazione in Atti di Convegno]
Abdollahyan, Maryam; Cascianelli, Silvia; Bellocchio, Enrico; Costante, Gabriele; Ciarfuglia, Thomas A; Bianconi, Francesco; Smeraldi, Fabrizio; Fravolini, Mario L
abstract

2017 - Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using 123I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT) [Abstract in Rivista]
Cascianelli, S; Tranfaglia, C; Fravolini, Ml; Bianconi, F; Minestrini, M; Nuvoli, S; Tambasco, N; Dottorini, Me; Palumbo, B
abstract

2017 - Robust visual semi-semantic loop closure detection by a covisibility graph and CNN features [Articolo su rivista]
Cascianelli, Silvia; Costante, Gabriele; Bellocchio, Enrico; Valigi, Paolo; Fravolini, Mario L; Ciarfuglia, Thomas A
abstract

2017 - Role of an artificial neural network classifier, a classification tree (ClT), to diagnose Parkinson's disease in early phase by using 123I-FP-CIT brain SPECT data [Abstract in Rivista]
Palumbo, B; Santonicola, A; Cascianelli, S; Nuvoli, S; Fravolini, Ml; Minestrini, M; Scialpi, M; Tambasco, N; Spanu, A; Madeddu, G
abstract

2017 - Role of artificial intelligence techniques (automatic classifiers) in molecular imaging modalities in neurodegenerative diseases [Articolo su rivista]
Cascianelli, Silvia; Scialpi, Michele; Amici, Serena; Forini, Nevio; Minestrini, Matteo; Luca Fravolini, Mario; Sinzinger, Helmut; Schillaci, Orazio; Palumbo, Barbara
abstract

2016 - A robust semi-semantic approach for visual localization in urban environment [Relazione in Atti di Convegno]
Cascianelli, Silvia; Costante, Gabriele; Bellocchio, Enrico; Valigi, Paolo; Fravolini, Mario L; Ciarfuglia, Thomas A
abstract

2016 - Comparison of the diagnostic performance of two methods of semi-quantitative analysis of 123I-FP-CIT brain SPECT images in mild Parkinson's disease [Abstract in Rivista]
Palumbo, B; Nuvoli, S; Cascianelli, S; Santonicola, A; Fravolini, Ml; Tambasco, N; Scialpi, M; Spanu, A; Madeddu, G
abstract

2016 - I-123-FP-CIT brain SPECT: tracer uptake values of right putamen are the most discriminant to diagnose Parkinson's disease [Abstract in Rivista]
Palumbo, B; Cascianelli, S; Santonicola, A; Minestrini, M; Buresta, T; Fravolini, Ml; Tambasco, N; Scialpi, M; Nuvoli, S; Spanu, A; Madeddu, G
abstract

2016 - SmartSEAL: A ROS based home automation framework for heterogeneous devices interconnection in smart buildings [Relazione in Atti di Convegno]
Bellocchio, Enrico; Costante, Gabriele; Cascianelli, Silvia; Valigi, Paolo; Ciarfuglia, Thomas A
abstract

2015 - 123-I-MIBG cardiac scintigraphy quantitative analysis in Parkinson's disease (PD) and Parkinsonism (P) differential diagnosis: a classification tree (CIT) classifier additional contribute [Abstract in Rivista]
Nuvoli, S; Palumbo, B; Fravolini, Ml; Piras, B; Dachena, G; Cascianelli, S; Spanu, A; Madeddu, G
abstract

2015 - A Learning Strategy for the Autonomous Control of Type 1 Diabetes [Articolo su rivista]
Fravolini Mario, Luca; Cascianelli, Silvia; Fabietti Pier, Giorgio
abstract

2015 - Cut-off values to diagnose Parkinson's disease by means of 123I-FP-CIT brain SPECT semiquantitave data as evaluated by Classification Tree algorithm [Abstract in Rivista]
Palumbo, B; Cascianelli, S; Sabalich, I; Santonicola, A; Buresta, T; Fravolini, Ml; Tambasco, N; Nuvoli, S; Spanu, A; Madeddu, G
abstract

2015 - The classification tree (CIT) classifier applied to 123I-MIBG cardiac scintigraphy in differentiating parkinson's disease (PD) from parkinsonisms (P) [Abstract in Rivista]
Nuvoli, Susanna; Palumbo, Barbara; Fravolini, Mario; Piras, Bastiana; Dachena, Graziana; Buresta, Tommaso; Cascianelli, Silvia; Spanu, Angela; Madeddu, Giuseppe
abstract

2014 - Experimental Evaluation of Two Pitot Free Analytical Redundancy Techniques for the Estimation of the Airspeed of an UAV [Articolo su rivista]
Fravolini, M. L.; Rhudy, M.; Gururajan, S.; Cascianelli, S.; Napolitano, M.
abstract

A measurement device that is extremely important for Unmanned Aerial Vehicle (UAV) guidance and control purposes is the airspeed sensor. As the parameters of feedback control laws are conventionally scheduled as a function of airspeed, an incorrect reading (e.g. due to a sensor fault) of the Pitot-static tube could induce an incorrect feedback control action, potentially leading to the loss of control of the UAV. The objective of this study is to establish the accuracy and reliability of the two airspeed estimation techniques for eventual use as the basis for real-time fault detection of anomalies occurring on the Pitot-static tube sensor. The first approach is based on an Extended Kalman Filter (EKF) and the second approach is based on Least Squares (LS) modeling. The EKF technique utilizes nonlinear kinematic relations between GPS, Inertial Measurement Unit and Air Data System signals and has the advantage of independence from knowledge of the aircraft model. The LS method is based on explicit knowledge of the aircraft model and has the advantage of on-line computation of the airspeed estimate, with minimal computational effort. The performance analysis was carried out with flight data from the WVU YF-22 UAV research platform. The results of the analysis indicate that the two methods provide essentially comparable performance in terms of mean (∼1 m/s) and standard deviation (∼1.5 m/s) of the airspeed estimation error which is about the 5% of the mean in-flight velocity of 32 m/s.