GIANOGLIO C.

To fulfil IEC 62128-2 (EN 50122-2) standard stray current requirements, the new or revamped DC electrified transportation systems shall achieve very good levels of track to ground insulation. This insulation shall be witnessed, usually in construction phases, by means of accurate measurement. Some measurement techniques are suggested by IEC...

Tactile sensing has become crucial in robotic applications such as teleoperation, as it gives information about the object properties that cannot be perceived by other senses. In fact, it is essential that robots are equipped with advanced touch sensing in order to be aware of their surroundings and give a feedback to an operator. Such sensing...

Tactile sensing systems require embedded processing to extract structured information in many application domains as prosthetics and robotics. In this regard, this paper proposes computationally light strategies to pre-process the sensor signals and extract features, feeding single layer feed-forward neural networks (SLFNNs) that proved good...

The deployment of the inference phase in self–standing systems, which have resource–constrained embedded units, is faced with many challenges considering computational cost of the elaboration unit. Therefore, we propose using a learning strategy based on a loss function that leads to finding the best configuration of the prediction...

Artificial tactile systems can facilitate the life of people suffering from a loss of the sense of touch. These systems use sensors and digital, battery-operated embedded units for data processing. Therefore, low-power, resource-constrained devices should host those embedded devices. The paper presents a framework based on 1-D convolutional...

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...

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...

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...

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...

The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on...

The deployment of connectionist models on resource-constrained, low-power embedded systems brings about specific implementation issues. The paper presents a design strategy, aimed at low-end reconfigurable devices, for implementing the prediction operation supported by a single hidden-layer feedforward neural network (SLFN). The paper first...

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...

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...

Artificial tactile sensing systems have gained significant attention in recent years due to their potential to enhance human-machine interaction. Numerous initiatives have been introduced to shift the computational paradigms of these systems towards a more biologically inspired approach, by incorporating neuromorphic computing methods. Despite...

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...

In this article, we propose a radar-based human action recognition system, capable of recognizing actions in real time. Range-Doppler maps extracted from a low-cost frequency-modulated continuous wave (FMCW) radar are fed into a deep neural network. The system is deployed on an edge device. The results show that the system can recognize five...

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...