Sensory substitution devices (SSDs) enable the compensation of sensory loss by translating information from one modality to another. However, current SSDs face limitations in conveying emotional content, which reduces user engagement and acceptance. This study investigates emotional coherence in visual-to-auditory sensory substitution, focusing on the mapping between visual stimuli and musical features. We introduce a novel experimental protocol that systematically measures emotional responses to various combinations of visual and auditory stimuli monitoring facial expression and physiological parameters, and using the Self-Assessment Manikin (SAM) scale. The collected database, containing data from 36 participants, is analysed to answer three research questions: 1) explore the role of valence and arousal dimensions in driving associations between visual content and musical characteristics, 2) verify emotional coherence between the SAM and the collected data, and 3) predict appropriate musical features for visual stimuli based on emotional response data. Results from our analyses revealed a strong preference for valence-matched stimuli over arousal-matched alternatives, with participants selecting valence-matched options in 83.0% of cases, compared to only 6.0% for arousal-matched stimuli. Furthermore, classical machine learning algorithms were successfully employed to classify high and low scores of the SAM metrics using the multimodal behavioural data, achieving 75.4% accuracy for dominance and 63.4% for arousal. Finally, the developed predictive models show promising results in predicting the selected song, with 67.9% in accuracy, and yield exceptionally low error rates in predicting musical characteristics, with mean squared errors reaching approximately$10^{-29}$for some musical features. These results provide a foundation for developing emotion-aware sensory substitution systems that maintain emotional congruence in the translation from visual to auditory modalities, potentially enhancing user engagement and acceptance of SSDs.

Sensors for continuous glucose monitoring provide real-time data about blood glucose concentration values. The operational duration of these devices ranges between 10 to 15 days, during which they frequently exhibit known errors as documented within the user application notes. This study introduces a novel approach, employing a comprehensive synthetic dataset that simulates 500 responses of 10 CGM sensors, to facilitate their self-calibration and self-learning autonomously, bypassing the need for back-propagation. The proposed solution minimizes sensor errors utilizing a Tiny Radial Basis Function Neural Network (TinyRBF) as its foundation. A variety of calibration intervals, spanning from 1 hour to a full week, were employed to characterize the model. The TinyRBF model demonstrated a reduced demand for computational resources by employing an average of 1.02 neurons, which is less than that required by other compact models such as Legendre Memory Unit (LMU) and Temporal Convolutional Network (TCN) models. This approach yielded a Mean Absolute Error (MAE) of 12.1 mg/dL, with recalibration executed every three days. Ultimately, the TinyRBF model was implemented on an Intelligent Sensor Processing Unit (ISPU), incorporating a low-power instruction set directly within the sensor package. The model quantized to 16 bits exhibited a 33.5% decrease in inference time in contrast to its floating-point counterpart. These findings imply its suitability for implementation within the sensor’s embedded computational resources.

The rapid increase in the number and variety of smart devices connected to the Internet has increased the need to ensure resilience, reliability, and traceability when transferring data within the current Internet of Things (IoT) network. The adoption of Distributed Ledger Technologies (DLT) can provide data with the above-mentioned features, but the low scalability and high cost related to the adoption of classical DLTs, like the blockchains, results in ineffective integration with most of IoT systems. Conversely, other DLTs, DAGs (Directed Acyclic Graph), possess benefits comparable to those of blockchains without presenting most of the limitations that prevent their application in the IoT domain. Therefore we present dRAIN: a distributed Reliable Architecture for IoT Networks. The adoption of this architecture can grant the communication, management, supervision, and updating of distributed IoT devices, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the scalability potential and to assess the actual limitation of the proposed architecture, we developed both a physical and virtual (simulated) Proof of Concept. The results of our analysis show adequate execution times for the operations, guaranteeing high levels of security with acceptable performance, and prove the architecture suitable for most IoT applications that do not require to process external data in real-time.

Current management of Type 1 Diabetes mellitus (T1D) resorts to manual meal announcements from the patient to manage postprandial glycemia; nevertheless, suboptimal glycemic control is observed in real data, with the presence of many hypoglycemic and hyperglycemic events. The utilization of Continuous Glucose Monitoring (CGM) sensors and Artificial Intelligence (AI) is paving the way for improved and automated glycemic control. A step in this direction is represented by the automation of meal detection, which would not require patients to perform tasks such as carbohydrate estimation and meal announcement that are error-prone, especially for children and elderly patients.In this work, we investigate several AI models for meal detection from in silico data of 10 adults, 10 adolescents, and 10 children with T1D using only CGM data, and compare them to the standard detection method based on the glycemic threshold. We generate 30 days of data per patient that include 5 meals per day and introduce human error on carbohydrate estimation to make data more similar to the real ones. The AI models can detect more than 81% of meals from any cohort of patients while producing a relatively small amount of false positives. The feedforward neural network, the support vector machine, and the threshold method are the most promising meal detection strategies for adult, adolescent, and child populations, respectively, and may improve patients’ health and disease management.

The identification of floating particles in liquids in order to characterize their purity and quality is a topic of growing interest in the face of the increasing attention being paid to product quality control and the rising tide of pollution in primary goods such as the drinking water. The problem of microplastics spread in water and food is one of the main issues of attention today, mainly because of the effects on people's health who consume these goods. The monitoring of large volumes of water represents one of the main issues of interest that is driving the development of non-invasive and non-destructive high-precision techniques. Among the most interesting methods of performing this monitoring, optical systems represent a solution of great interest given their negligible, if any, impact on the monitored products and their ability to continuously analyzing the compound of interest. Given a high-quality optical recording system, it is necessary to complement it with a highly reliable and fast detection system to allow large volumes to be monitored in a relatively short time. In this scenario, the current paper brings three main contributions: (i) it defines and models a detection system with controllable reliability, (ii) it presents an online detection algorithm and (iii) it tests the suitability of the proposed system for integration into existing monitoring devices.

IoT sensors networks are often characterized by stringent power requirements and a high probability of sensor fault. This paper, thanks to Graph Signal Processing (GSP), aims to model an IoT scenario to find an optimal sensor configuration for battery-saving applications. In detail, the Girwan Newman method is applied to the graph to find clusters and the performance of the described method is evaluated in terms of signal-noise ratio depending on the fraction of sampled sensors. Tests were performed both on simulated data and real data from the European Environment Agency considering several air pollutants concentrations.

The Graph Signal Processing (GSP) is a mathematical framework that extends the Discrete Signal Processing (DSP) tools such as filtering and signals decomposition to graph data structures. In this paper, we explore the application of the GSP framework to distributed wireless sensor networks to reduce the measured noise and/or estimate the signal of missing sensors. The context is that of distributed monitoring applications, such as the monitoring of large buildings, such as hospitals, public areas and farmlands. Using simulation tools, we analysed the ability of GSP in reducing the noise and in estimating sensor's data in different WSN scenarios. We modelled the sensor networks as Graph structures and apply Graph Shift and Graph Laplacian operations on such graph signals. The analysis of obtained results shows that GSP may represent a valuable tool in the considered scenarios with outstanding performance.

The diffusion of cheap sensing and programmable hardware systems enables today the quick design of complex smart sensor devices, i.e. sensing systems equipped with computational and communication functionalities, ready to be included in IoT systems. In this paper we present a general software architecture for the implementation of such smart sensors. The goal of the proposed model is to provide a reference framework usable in multiple scenarios, suitable for the management of different sensors, and providing a general and standard interface and simple computational functionalities. The paper presents our model, examples of different smart sensors based on our model and the implementation, test and performance assessment of a simple analogue smart sensor. The implementation of the proposed model is available online as a template for the design and development of smart sensors equipped with updatable edge computing functionalities. The performance evaluation of our analogue smart sensor shows that the application of our model has limited overhead which is highly compensated by its flexibility.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

LoRaWAN is a Low Power Wide Area Network (LPWAN) technology which enables low cost and low power IoT device communication even in dense urban areas. LoRa modulation is capable of extracting data from a weak signal in noisy environments. This modulation technique can be useful for the delivery of critical data in the noisy environment. In this study, we investigate LoRaWAN technology in the context of data transmission for health care systems (or critical health data in disaster environments). In the study, we explore the standard and evaluate data frame transmission in LoRaWAN with a custom test-bed system.

Bluetooth Low Energy (BLE), also known as Bluetooth Smart, is part of the Bluetooth 4.0 Core specifications, introduced in June 2010 by the Bluetooth Special Interest Group (SIG). BLE is designed as a radio standard that minimizes power consumption and it is particularly suited for low cost, low bandwidth, low power, and low complexity applications. These aspects of the standard represent the reasons of its success in the field of biometric monitoring sensors. The aim of this paper is to present a performance evaluation of a Body Area Network System based on Bluetooth Smart. The evaluation is performed on a real test-bed, composed of offthe-shelf BLE devices. The BLE devices are equipped with software tools developed specifically for this work.

The analysis of EEG evoked potentials strongly relies on the correct alignment of different segments of the recorded EEG activity. The alignment of segments is needed in order to extract event related waves hidden in the background EEG activity. Commonly, the information on onsets is provided by the acquisition system. However, wrong configuration of the recording system or human errors during the acquisition or storage of data may make this information unavailable. Usually, these errors are discovered during the datasets analysis stage, and this stage can take place even several months after the acquisition of datasets. Unluckily, changes on patients status and the expensiveness of EEG registrations make unfeasible to repeat the acquisitions. In this paper we present and evaluate two mechanisms that we included in an EEGLAB plugin for the automatic reconstruction of onsets in EEG-TMS recordings. The methods of the TMS Triggers Reconstruction Software (TRS-TMS) plugin are discussed and evaluated obtaining guidelines for their correct configuration in the routine usage.

TBY-2 cells are widely used in several applications and in particular in the study of programmed cell-death (PCD). However, automatic or semi-automatic computer-based tools supporting the specialist during the measurement process are still missing. In this paper we propose and test a semi-automatic tool for the segmentation of TBY-2 cell and the measurement of cytosol area. The algorithm has been designed in order to be easy to use and to have low complexity. Results obtained on a database of TBY-2 cells confirm that the algorithm can be effectively used by specialists in their daily work.

Although the EDCA access mechanism of the 802.11e standard supports legacy DCF stations, the presence of DCF stations in the WLAN jeopardizes the provisioning of the service guarantees committed to the EDCA stations. The reason is that DCF stations compete with Contention Windows (CWs) that are predefined and cannot be modified, and as a result, the impact of the DCF stations on the service received by the EDCA stations cannot be controlled. In this paper, we address the problem of providing throughput guarantees to EDCA stations in a WLAN in which EDCA and DCF stations coexist. To this aim, we propose a technique that, implemented at the Access Point (AP), mitigates the impact of DCF stations on EDCA by skipping with a certain probability the Ack reply to a frame from a DCF station. When missing the Ack, the DCF station increases its CW, and thus, our technique allows us to have some control over the CWs of the legacy DCF stations. In our approach, the probability of skipping an Ack frame is dynamically adjusted by means of an adaptive algorithm. This algorithm is based on a widely used controller from classical control theory, namely a Proportional Controller. In order to find an adequate configuration of the controller, we conduct a control-theoretic analysis of the system. Simulation results show that the proposed approach is effective in providing throughput guarantees to EDCA stations in presence of DCF stations.

The paper presents a preliminary study of a revised analysis of IEEE 802.11e performance. One of many possible topologies is analyzed in order to emphasize the severe problem of the incapability to prioritize traffic in networks with hidden nodes. The article also provides some innovative conclusions.

Abstract — To this date, many works have been conducted to study the throughput and delay performance of WLANs under saturated conditions and to obtain the configuration that provides optimal performance under these conditions. From these previous works, however, it remains unclear whether this configuration is also appropriate for a WLAN operating under nonsaturation conditions. In this paper we present solid argu-ments which demonstrate that the optimal configuration resulting from saturation is also appropriate for a WLAN operating under nonsaturation conditions. Specifically, we show (via analysis and simulation) that a WLAN configured differently suffers from a number of performance anomalies when operating with finite sending rates. This is an important result for the configuration of WLAN parameters. I.

ACKS (ACK skipping) is a mechanism to enable QoS support in standard IEEE 802.11 WLANs working in infrastructure configuration. The paper presents an analytical model to estimate ACKS performance under saturation conditions, when multiple service classes are considered. Based on this model, the paper proposes an algorithm that optimally configures ACKS networks to provide QoS guarantees for DiffServ-like configurations, that is, for three classes of stations characterized by different QoS requirements. The effectiveness of the proposed algorithm is proved by simulation in different network scenarios.