1,721,071 research outputs found
Pupil diameter estimation in visible light
No full textPupil size is a valuable source of information since it can reveal the emotional state, fatigue and ageing process. A lot of research has been carried out in this area with clinical and even psychiatric validity, since the fluctuations in the size of the pupil are closely linked to the autonomic nervous system. The pupil size analysis of oscillations due to contraction and dilation could be a useful instrument for diagnosis of disorders related to their own control mechanisms and an index of neurological disease affecting other nerve centres. Pupillography is the pupil size clinical examination which involves the use of infrared light, which allows performing an optical analysis of the pupil, varying the light conditions and measuring the pupillary diameter in different luminance levels. The aim of the proposed work is to exploit video processing techniques in visible light to calculate the pupil diameter and analyse the pupil diameter changing as a result of the lighting conditions variation
Carla Giovannini su Lucio Gambi e Renato Zangheri
No full text Terreni di studio e di lavoro: Lucio Gambi e Renato Zangheri di Carla Giovannini 1. Il 15 novembre 2007 il rettore dell’Università di Bologna, Pier Ugo Calzolari, intitolò a Lucio Gambi (1920-2006) un’aula del Dipartimento di Discipline storiche al primo piano dello storico complesso di San Giovanni in Monte. L’aula, l’antica stanza del Priore nel cinquecentesco convento dei canonici lateranensi, è oggi uno spazio destinato a lezioni, tesi di laurea, seminari e riunioni che presenta qualch..
Carla Giovannini su Lucio Gambi e Renato Zangheri
No full text Terreni di studio e di lavoro: Lucio Gambi e Renato Zangheri di Carla Giovannini 1. Il 15 novembre 2007 il rettore dell’Università di Bologna, Pier Ugo Calzolari, intitolò a Lucio Gambi (1920-2006) un’aula del Dipartimento di Discipline storiche al primo piano dello storico complesso di San Giovanni in Monte. L’aula, l’antica stanza del Priore nel cinquecentesco convento dei canonici lateranensi, è oggi uno spazio destinato a lezioni, tesi di laurea, seminari e riunioni che presenta qualch..
Autoencoder based Physical Layer Authentication for UAV Communications
No full textThe use of flying Unmanned Aerial Vehicles (UAVs) for communications is becoming more and more widespread, especially in 5G and beyond networks. In such a context, detection and authentication of UAVs is assuming an increasingly important role. In this paper we show that it is possible to distinguish different drones which communicate with a fixed ground base station (BS) on the basis of their channel characteristics and of the micro-Doppler signature associated to the specific features of each UAV. An urban scenario is simulated where UAVs fly at a constant height and channels are affected by Additive White Gaussian Noise (AWGN) and fading. With the aim of helping the BS in its authentication task, we take advantage of a sparse autoencoder trained on the channel of the legitimate transmitter, while data coming from possible attackers are classified as anomalies. We prove that, with proper network training, low levels of false alarm and missed detection can be achieved, especially if the attacker has no line-of-sight link, and that the presence of micro-Doppler actually contribute to enhance the authentication performance
Heart Rate Estimation Using the EVM Method, the FFT and MUSIC Algorithms Under Different Conditions
No full textThe detection of vital parameters is one of the first and foremost task to be performed on a patient to evaluate her/his health status. Among the vital signs, a great importance is assumed by the heart rate, which is defined as the number of heartbeats measured in a minute. Heart rate is generally measured using an electrocardiograph, which allows an accurate recording of the cardiac activity, but requires the placement of some electrodes in specific points of the body. Such an approach involves a direct contact between the device and the patient’s skin, which is often uncomfortable and impractical. In this paper we propose a contactless approach based on RGB video for heart rate extraction, avoiding any type of direct interaction. For this purpose, we exploit videos of the subjects’ faces recorded via smartphone and we then apply the EVM method to estimate the heart rate obtained in a comfortable and non-invasive way. The results obtained with two different methodologies, based on Fast Fourier Transform (FFT) and on the MUltiple SIgnal Classification (MUSIC) algorithm, have been compared to three devices with clinical validity, i.e., a pulse oximeter, a Polar heart rate strap and a blood pressure monitor, which guarantee accuracy at a reduced cost and are easily commercially available. The high accuracy of the developed system is proved by the small difference achieved between the values measured with the developed contactless technique and those obtained with the wearable devices, which results in an error between 1.07% and 4.67%, regardless of the ambient light conditions under which the videos were captured
Physical Layer Authentication Techniques based on Machine Learning with Data Compression
No full textWireless communications employing multi-carrier transmissions, like orthogonal frequency division multiplexing (OFDM) or single-carrier frequency division multiple access (SCFDMA) may involve the use of a large number of subcarriers. In Internet of Things (IoT) contexts, however, the use of such technologies implies the fast management of large amounts of samples on devices with limited memory and computational resources. The adoption of physical layer authentication protocols in IoT may suffer from this fact, especially when they exploit machine learning algorithms yielding a significant computational burden. For instance, the complexity of Nearest Neighbor classifiers strictly depends on the training set dimension, which is directly proportional to the number of used subcarriers. In order to deal with this issue, we start from a naive approach based on random sampling of the input data to extract features, and then consider more advanced data dimension reduction algorithms, such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). We show that PCA is able to guarantee the best trade-off between authentication performance and complexity, while the application of t-SNE is effective when one wants to reduce data to a very small number of features
Physiological parameters extraction by contactless accelerometric signal analysis during sleep
Full text linkSince sleep problems, like sleep apnea, may pose a serious health concern, the quality of a person’s sleep is a good indicator of overall wellbeing. It is then crucial to continuously monitor people when they are sleeping, especially if they have cardiac or respiratory conditions. The goal of the present paper is to show how to extract physiological parameters from accelerometric signal processing during sleep by applying a non-invasive technology. Using an accelerometric device located under the mattress, we demonstrated the possibility of extracting heart rate and respiratory rate, and then how to use the same signal to implement an automatic algorithm to recognize apneas and, more generally, different activities. The proposed automatic approach has shown good accuracy and dependability, and it may be a useful tool for preventing significant harm during sleep
Autoencoder-based physical layer authentication in a real indoor environment
Full text linkAuthentication of wireless nodes, as in fifth-generation (5G) and Internet of Things (IoT) networks, is an increasingly pressing issue, in order to limit the required computational effort and the necessary overhead. A simplification of the authentication process may therefore be of interest to achieve the satisfaction of stringent performance requirements, such as those envisaged for sixth-generation (6G) networks. This paper provides a study on the feasibility of physical layer authentication (PLA) in a real indoor environment, as an alternative solution to the traditional authentication schemes. To ensure the reliability of the proposed approach a simulated scenario is firstly tested. Subsequently, real-world data are collected through a laboratory setup using a Vectorial Signal Transceiver (VST) and two Universal Software Radio Peripherals (USRPs) to emulate the behavior of the receiver, the legitimate transmitter, and the potential adversary. A machine learning (ML) algorithm is then exploited to act as authenticator. This means that channel fingerprint is extracted from signals to create a dataset used to train a sparse autoencoder. To emulate a real authentication scenario, the autoencoder is trained only on the class of the legitimate user. Once a new message arrives, the autoencoder task is to discern authentic signals from those forged by the adversary. It is shown that a geometric mean of accuracy of more than 90%, with corresponding low levels of false alarm and missed detection, is achievable irrespective of the nodes location, underlining the robustness and versatility of the proposed ML-based PLA approach
Physical Layer Authentication with Cooperative Wireless Communications and Machine Learning
No full textOpposed to classical authentication protocols following a computational security paradigm based on secret credentials and cryptographic primitives, physical layer authentication aims at distinguishing users without shared secrets, by leveraging the natural randomness and uniqueness of transmission channels. We consider the special setting of cooperative wireless communications, in which some relay nodes are located between a supplicant and an authenticator, and we assess the performance of physical layer authentication approaches based on both statistical and machine learning techniques. We show that the presence of relay nodes enabling cooperative communications may improve the performance of physical layer authentication
Automotive radar application for structural health monitoring
No full textMonitoring of communication and residential infrastructures, such as buildings, bridges and tunnels, has always been important to ensure the safety of citizens. In particular, it is of interest to verify the level of mechanical stress to which bridges and buildings are subjected during earthquakes, measuring the oscillations that the structure undergoes during the seismic event, in order to verify the overcoming of safety thresholds, in which stability may be impaired. In this paper the mmWave radars designed for the automotive world is taken into account, since it allows high precision measurements at a relatively low cost. mmWave radars are able to detect vibrations of the order of tens of microns and, therefore, are very useful for monitoring buildings. Furthermore, using MIMO (Multiple Input Multiple Output) mmWave radars, it is possible to implement the Beamforming technology for the spatial shaping of the radio beam, in order to detect more targets simultaneously. In this way it is possible to monitor multiple buildings or structures simultaneously, or different parts of the same structure with a single sensor. The objective of the present work is to show the applicability of automotive radar to the measurement of oscillations suffered by buildings and bridges, in order to be used as a monitoring tool in the event of earthquakes. Results of software simulations, laboratory test and real measurements on infrastructures are provided
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