01a Articolo in rivista

Nonlinear Hamiltonian systems under sampling

This paper investigates the transformation of Hamiltonian structures under sampling. It is shown that the exact sampled-data equivalent model associated to a given port-Hamiltonian continuous-time dynamics exhibits a discrete-time representation in terms of the discrete gradient, with the same...
Quaternion-Based Attitude Stabilization via Discrete-Time IDA-PBC

In this letter, we propose a new sampled-data controller for stabilization of the attitude dynamics at a desired constant configuration. The design is based on discrete-time interconnection and damping assignment (IDA) passivity-based control (PBC) and the recently proposed Hamiltonian...
Discrete-time energy-balance passivity-based control

In this paper, new results for passivation and stabilization of discrete-time nonlinear systems via energy balancing are established. When specified on sampled-data systems, the approach is constructive for computing stabilizing digital controllers that assign, at all sampling instants, a target...
Sampled-data steering of unicycles via PBC

In this paper, on the basis of a recently proposed discrete-time port-Hamiltonian representation of sampled-data dynamics, we propose a new time-varying digital feedback for steering mobile robots. The quality of the proposed passivity-based control is validated and compared through simulations...
Weakly and semi-supervised detection, segmentation and tracking of table grapes with limited and noisy data

Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks for precision agriculture, enabling robotic harvesting and yield estimation applications. However, modern algorithms are data hungry and it is not always possible to gather enough data to apply the best...
Exploiting Wavelet Recurrent Neural Networks for satellite telemetry data modeling, prediction and control

Multidimensional times series prediction is a challenging task. Only recently the increased data availability has made it possible to tackle with such problems. In this work we devised a novel method to exploit the multiple correlated features in the time series. The recurrent neural networks and...
A self-interpretable module for deep image classification on small data

Deep neural networks are the driving force of the recent explosion of machine learning applications in everyday life. However, they usually require a lot of training data to work well, and they act as black-boxes, making predictions without any explanation about them. This paper presents Memory...
Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices

Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for...
The impact of cultural dimensions on project management performance

PurposeDiverse cultures may make people behave differently and this, in turn, can impact project management. While the relationship between culture and project success has been widely explored, there is a need of addressing the gap in the relationship between culture and project management...
The influence of manufacturing contexts on additive manufacturing-enabled competitive capabilities

Purpose: This paper presents a contingency analysis of additive manufacturing's (AM) impacts, proposes a novel form of AM-enabled competitive capabilities and explores manufacturing contexts (including product-operation-organization-related factors) influencing those capabilities. Design/...