GASTALDO P.

Depth sensors play a major role in the control pipeline of semi-autonomous prostheses. The feed from these sensors can complement, or even substitute, the information retrieved by RGB cameras. The paper explores the application of depth sensors to affordance detection, to recognize the graspable part of an object in foreground images. The...

The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. The insulation system of an electrical machine is affected by partial discharges (PDs) phenomena that—in the long term—can lead to the breakdown. This in turn may bring about a significant economic loss; wind turbines provide an excellent...

Learning paradigms that use random basis functions provide effective tools to deal with large datasets, as they combine efficient training algorithms with remarkable generalization performances. The paper first considers the affinity between the paradigm of learning with similarity functions and the Extreme Learning Machine (ELM) model, and...

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general...

With the growth of pervasive electronics, the availability of compact digital circuitry for the support of data processing is becoming a key requirement. This paper tackles the design of a digital architecture supporting the n-mode tensormatrix product in fixed point representation. The design aims to minimize the resources occupancy, targeting...

Random-based learning paradigms exhibit efficient training algorithms and remarkable generalization performances. However, the computational cost of the training procedure scales with the cube of the number of hidden neurons. The paper presents a novel training procedure for random-based neural networks, which combines ensemble techniques and...

Image polarity detection opens new vistas in the area of pervasive computing. State-of-the-art frameworks for polarity detection often prove computationally demanding, as they rely on deep learning networks. Thus, one faces major issues when targeting their implementation on resource-constrained embedded devices. This paper presents a design...

Thanks to recent advances in machine learning, some say AI is the new engine and data is the new coal. Mining this 'coal' from the ever-growing Social Web, however, can be a formidable task. In this work, we address this problem in the context of sentiment analysis using convolutional online adaptation learning (COAL). In particular, we...

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network...

Video-based grasp classification can enhance robotics and prosthetics. However, its accuracy is low when compared to e-skin based systems. This paper improves video-based grasp classification systems by including an automatic annotation of the frames that highlights the joints of the hand. Experiments on real-world data prove that the proposed...

Affordance detection in computer vision allows segmenting an object into parts according to functions that those parts afford. Most solutions for affordance detection are developed in robotics using deep learning architectures that require substantial computing power. Therefore, these approaches are not convenient for application in embedded...

Wearable systems require resource-constrained embedded devices for the elaboration of the sensed data. These devices have to host energy-efficient artificial intelligence (AI) algorithms to output information to a human user. In this regard, the single-layer feed-forward neural networks (SLFNNs) proved to be very effective for deployment on...

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...