APICELLA T.

Robust landing pad detection plays a major role in Autonomous Unmanned Aerial Vehicles (UAVs). This problem can be approached using deep neural networks for vision-based inference. However, the full integration of deep learning algorithms into the small UAVs is still challenging for their limited resources. This paper presents a landing pad...

One of the main issues concerning Battery Electric Vehicles (BEVs) is represented by range anxiety. This problem becomes crucial considering commercial vehicles equipped with electric Power Take Off (ePTO), which acts as power supplier for auxiliary loads. The paper presents a technique to estimate the reliability of power consumption...

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature...

Embedding the ability of sentiment analysis in smart devices is especially challenging because sentiment analysis relies on deep neural networks, in particular, convolutional neural networks. The paper presents a novel hardware-friendly detector of image polarity, enhanced with the ability of saliency detection. The approach stems from a...

In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging task. The main challenges consist of occlusions, different filling materials and lighting conditions. The mass of an object constitutes key information for the robot to...

Providing user customized experience is one of the main goals for present-day electronic smart devices. Image polarity detection plays a crucial role in understanding users' preferences due to the fact that information is massively represented by means of pictures. State-of-the-art frameworks are based on deep learning networks and continue...

Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geometry and physical properties of these surfaces as well as occlusions. In this paper, we focus on occlusions of an object that is hand-held by a person manipulating it....

Affordance detection consists in predicting the possibility of a specific action on an object. While this problem is generally defined for fully autonomous robotic platforms, we are interested in affordance detection for a semi-autonomous scenario, with a human in the loop. In this scenario, a human first moves their robotic prosthesis (e.g....

Machine learning in embedded systems has become a reality, with the first tools for neural network firmware development already being made available for ARM microcontroller developers. This paper explores the use of one of such tools, namely the STM X-Cube-AI, on mainstream ARM Cortex-M microcontrollers, analyzing their performance, and...