The rise of the Internet of Things has introduced new challenges related to data security and transparency, especially in industries like agri-food where traceability is critical. Traditional cloud-based solutions, while scalable, pose security and privacy risks. This paper proposes a decentralized architecture using Blockchain technology to address these challenges. We deploy IoT sensors connected to a Raspberry Pi for edge processing and utilize Hyperledger Fabric, a private Blockchain, to manage and store data securely. Two approaches are evaluated: computation of a Discomfort Index on the Raspberry Pi (edge processing) versus performing the same computation on-chain using smart contracts. Performance metrics, including latency, throughput, and error rate, are measured using Hyperledger Caliper. The results show that edge processing offers superior performance in terms of latency and throughput, while Blockchain-based computation ensures greater transparency and trust. This study highlights the potential of Blockchain as a viable alternative to centralized cloud systems in IoT environments and suggests future research in scalability, hybrid architectures, and energy efficiency.

Although the Automatic Train Operation (ATO) is consolidated in urban railways, its use on mainlines is still unexplored. Currently, the first prototypes of train with ATO capable of running on mainlines equipped with specific control systems (e.g., ETCS/ERTMS in Europe) have been realized. However, they require the active presence of staff on board. Recent research in innovative solutions for railway efficiency has opened to the possibility of extending the ATO concept to the Unattended Train Operation (UTO), i.e., the full automation of infrastructures and vehicles. In this context, a project based on synergistic collaboration between academia and the national railway industry has led to the definition of a new Vital Control module (VC). VC includes a PCB, managed by a reliable and safe hard Real-Time Operating System (RTOS). The hardware consists of a Eurocard-sized PCB that houses an Ultrazed-EG System on Module as computing core and embeds several communication interfaces to favor the inclusion in existing apparatus. The VC RTOS runs an application logic that acts as a real-time control core for the assessment of the on-cabin equipment operativity. VC is also responsible for detecting UTO-related hazardous situations by intervening with emergency braking. Both VC hardware and software are developed to be compliant with related safety standards. The proposed VC has been included in an automatic testbed to recreate real-time hazardous scenarios. In this context, VC system has proven to be able to mitigate these scenarios ~2 times faster than current ATO protection system.

In this live demonstration the RIDDANCE (distuRb-resIstant Dry electroDe-based brAiN Computer intErface) framework is presented. It is a Brain-Computer Interface (BCI) framework that characterizes and counteracts several factors that make difficult applying BCIs to real life contexts. RIDDANCE exploits data from an 8-channel dry EEG headset by g. Tec, Data collection for training/testing is typically carried out in not controlled environment (often disturbed) to improve the training and the classification robustness. For the purpose RIDDANCE embeds a user-tailored neural network (NN) topology selector that find the most robust model in terms of average validation loss. RIDDANCE is optimized for P300-based tasks and tested in a prototype car driving application.

Abstract Recent advances in technology and railway have led to the introduction of systems and infrastructures capable of driving trains automatically. The Automatic Train Operation (ATO) system has been optimized for active human supervision. The next challenge is to realize ATO systems capable of achieving unsupervised operations on the mainlines. However, at this aim, additional safety functionalities should be provided. In this paper, we propose a pioneer hardware/software Vital Control Module (VCM) architecture capable of expanding the control capabilities of the existing train control system. The VCM includes a Printed Circuit Board (PCB), to be integrated into the cabin equipment, managed by a reliable and safe hard Real-Time Operating System (RTOS). Both hardware and software are developed to be compliant with related safety standards. The VCM integrates an application logic that acts as an on-board equipment control core, assessing the overall operativity in real-time, and promptly issuing emergency brakes if hazardous situations occur. The application logic has been developed with a model-based approach via Simulink/Stateflow tool and implemented as a C-script on the Xilinx Ultrascale + core housed on the PCB. We have used two testbeds to evaluate the VCM performance. Experimental results showed that the Worst-Case Response Time (WCRT) by the application logic is 13.6 times smaller than the most limiting specification-related deadline. The achieved earliness (− 1.8 ms out of 2 ms of deadline) allows for the easy expansion of VCM’s train protection capabilities in the future. Results from the second testbed showed that the VCM can intervene to mitigate hazardous situations ~ 2 times faster than the current automatic train protection systems according to the related standard.

In this paper, we present a novel framework to realize an automatic “accuracy versus complexity” characterization for embeddable emotion recognition systems in healthcare applications. This framework is based on a series of grid searches able to identify highly descriptive features from the input signals, while taking into account implementation ease, memory usage, and classification performance. This paper is articulated on a proof of concept, in which the proposed framework is used to extract an emotion recognition architecture able to discriminate up to 8 emotions, by employing the users' cortical activity and a 3D model for the emotion characterization. The implemented series of grid searches lead to a final low-memory, low-resources and low-complexity emotions recognition processing chain suitable for embedded platforms. The processing chain extracted by the proposed framework has been implemented as a real-time task on a personal care robot processing core. The extracted emotion recognition system tested on cortical signals from a publicly available dataset, showed an accuracy of ~75 % (average) in discriminating 8 emotions and a memory reduction of 70% from a canonical implementation of the same feature extraction step.

In this paper, we propose an innovative architecture that merges the Personal Care Robots (PCRs) advantages with a novel Brain Computer Interface (BCI) to carry out assistive tasks, aiming to reduce the burdens of caregivers. The BCI is based on movement related potentials (MRPs) and exploits EEG from 8 smart wireless electrodes placed on the sensorimotor area. The collected data are firstly pre-processed and then sent to a novel Feature Extraction (FE) step. The FE stage is based on symbolization algorithm, the Local Binary Patterning, which adopts end-to-end binary operations. It strongly reduces the stage complexity, speeding the BCI up. The final user intentions discrimination is entrusted to a linear Support Vector Machine (SVM). The BCI performances have been evaluated on four healthy young subjects. Experimental results showed a user intention recognition accuracy of ~84 % with a timing of ~554 ms per decision. A proof of concept is presented, showing how the BCI-based binary decisions could be used to drive the PCR up to a requested object, expressing the will to keep it (delivering it to user) or to continue the research.

This paper describes the design, implementation and in vivo test of a novel Brain Computer Interface (BCI) for the mechatronic devices control. The method exploits electroencephalogram acquisitions (EEG), and specifically the Movement Related Potentials (MRPs) (i.e., μ and β rhythms), to actuate the user intention on the mechatronic device. The EEG data are collected by only five wireless smart electrodes positioned on the central and parietal cortex area. The acquired data are analyzed by an innovative single-trial classification algorithm that, with respect to the current state of the art, strongly reduces the training time (Minimum: ~1 h, reached: 10 min), as well as the acquisition time - after stimulus - for a reliable classification (Typical: 4-8 s reached: 2 s). As first step, the algorithm performs an EEG time-frequency analysis in the selected bands, making the data suitable for further computations. The implemented machine learning (ML) stage consists of: (i) dimensionality reduction; (ii) statistical inference-based features extraction (FE); (iii) classification model selection. It is also proposed a dedicated algorithm, the MLE-RIDE, for the dimensionality reduction that, jointly with statistical analyses, digitalize the μ and β rhythms, performing the features extraction. Finally, the best support vector machine (SVM) model is selected and used in the on-line classification. As proof of concept, two mechatronic devices have been brain-controlled by using the proposed BCI algorithm: a three-finger robotic hand and an acrylic prototype car. The experimental results, obtained with data from 3 subjects (aged 26±1), showed an accuracy on human will wireless detection of 87.4%, in the real-time binary discrimination, with 33.7ms of computation times.

This study presents a P300‐based brain‐computer interface (BCI) for mechatronic device driving, i.e. without the need for any physical control. The technique is based on a machine learning (ML) algorithm, which exploits a spatio‐temporal characterization of the P300, analyses all the discrimination scenarios through a multiclass classification problem. The BCI is composed of the acquisition unit, the processing unit and the navigation unit. The acquisition unit is a wireless 32‐channel electroencephalography headset collecting data from six electrodes. The processing unit is a dedicated µPC performing stimuli delivery, ML and classification, leading to the user intention interpretation. The ML stage is based on a custom algorithm (tuned residue iteration decomposition) which trains the classifier on the user‐tuned P300 features. The extracted features undergo a dimensionality reduction and are used to define decision boundaries for the real‐time classification. The real‐time classification performs a functional approach for the features extraction, reducing the amount of data to be analyzed. The Raspberry‐based navigation unit actuates the received commands, supporting the wheelchair motion. The experimental results, based on a dataset of seven subjects, demonstrate that the classification chain is performed in 8.16 ms with an accuracy of 84.28 ± 0.87%, allowing the real‐time control of the wheelchair.

A new mobile healthcare system for neuro-cognitive function monitoring and treatment is presented. The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering, and in-cloud distribution to specialized medical personnel. As such, it presents tradeoffs typical of other cyber-physical systems, where hardware, algorithms, and software implementations have to come together in a coherent fashion. The system is based on spatio-temporal detection and characterization of a specific brain potential called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects ( n = 12 healthy, n = 3 mildly cognitive impaired, and n = 2 with Alzheimer's disease involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s), yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.

In this paper we present a P300-hased Brain Computer Interface (BCI) for the remote control of a mechatronic actuator, such as wheelchair, or even a car, driven by EEG signals to be used hy tetraplegic and paralytic users or just for safe drive in case of car. The P300 signal, an Evoked Related Potential (ERP) devoted to the cognitive brain activity, is induced for purpose by visual stimulation. The EEG data are collected by 6 smart wireless electrodes from the parietal-cortex area and online classified by a linear threshold classifier, basing on a suitable stage of Machine Learning (ML). The ML is implemented on a μPC dedicated to the system and where the data acquisition and processing is performed. The main improvement in remote driving car by EEG, regards the approach used for the intentions recognition. In this work, the classification is based on the P300 and not just on the average of more not well identify potentials. This approach reduces the number of electrodes on the EEG helmet. The ML stage is based on a custom algorithm (t-RIDE) which tunes the following classification stage on the user's “cognitive chronometry”. The ML algorithm starts with a fast calibration phase (just ~190s for the first learning). Furthermore, the BCI presents a functional approach for time-domain features extraction, which reduces the amount of data to be analyzed, and then the system response times. In this paper, a proof of concept of the proposed BCI is shown using a prototype car, tested on 5 subjects (aged 26 ± 3). The experimental results show that the novel ML approach allows a complete P300 spatio-temporal characterization in 1.95s using 38 target brain visual stimuli (for each direction of the car path). In free-drive mode, the BCI classification reaches 80.5 ± 4.1% on single-trial detection accuracy while the worst-case computational time is 19.65ms ± 10.1. The BCI system here described can be also used on different mechatronic actuators, such as robots.

In this paper an improved design of an RFID powered swallowable barometric endoradiosonde (ERS) is presented. The ERS consists of a micro fabricated capacitive sensor printed in gold on Polycaprolactone (PCL) on which a transponder chip design in 0.35μm AMS technology is in-plane bounded to the PCL substrate. The implantable or inside the body, transponder (tag) is powered by the external reader at 900MHz through inductively coupled antennas. The tag performs the capacitance to frequency conversion and transmits data back to the reader using load-shift keying (LSK) modulation. The ERS can measure the sensor output frequency with an INL error of 0.4%, sensitivity (Δf/ΔP) of -6.12MHz/kPa, occupies a volume of 1mm3 (transponder only), consumes, 400μW and 360μW for, respectively, dynamic and static power. The sensor has an accuracy of ± 0.1kPa, works in the pressure range of 0-1.99kPa (0-15mmHg). The readout circuit has a ±1.84kHz resolution.

We propose a novel approach that integrates wireless, non-invasive devices with fast, real-time algorithms for large data analysis and biofeedback reaction, to discern the voluntariness of human movement through direct sensing of brain potentials combined with muscular action signal monitoring. The system has been tested in real situations.

This position paper aims to support a control technique in the perishables goods supply-chain through a combination of near real-time wireless sensor network (WSN) for environmental monitoring and further data processing to predict the shelf life of the product. This approach returns a low cost, versatile and efficient tool that can significantly improve the safety and food certification through the organoleptic qualities control using three different sensors, i.e. temperature, light and humidity. In this article, therefore, the advantages of the proposed technique are explained and a case study is presented to support this approach, as well as an example of processing algorithm for shelf life evaluation.

The developments in micro-nano-electronics, biology and neuro-sciences make it possible to imagine a new world where vital signs can be monitored continuously, artificial organs can be implanted in human bodies and interfaces between the human brain and the environment can extend the capabilities of men thus making the dream of Dr. Frankenstein become true. This paper surveys some of the most innovative implantable devices and offers some perspectives on the ethical issues that come with the introduction of this technology.

An RFID reader collecting neural data from implanted electrodes of an electro-corticography ECoG system is proposed. The system also is able to power an implant using a class E power amplifier (PA) while it receives data by an asynchronous demodulation. A standard 65nm CMOS TSMC technology is used. Simulations reveal an average power consumption of the overall system of 19 mW with on a 1.2V supply a 300MHz carrier frequency. The data transmission is 1MHz and a bit error rate (BER) of 0.3% is evaluated.

The design and first measuring results of an ultra-low power 12bit Successive-Approximation ADC for autonomous multi-sensor systems are presented. The comparator and the DAC are optmised for the lowest power consumption. The proposed design has a power consumption of 0.52µW at a bitclock of 50-kHz and of 0.85µW at 100-kHz with a 1.2-V supply. As far as we know, the Figure-of-Merit of 66 fJ/convertion-step is the best reported so far. The ADC was realised in the NXP CMOS 0.14µm technology with an area of 0.35 mm2. Only four metal layers were used in order to allow 3D integration of the sensors.

A fully digital test stimuli generation and on-chip specifications evaluation for cheap, fast, though accurate testing of high resolution ΣΔ ADCs are here presented. Simulations and measurements showed a discrimination threshold on specification parameters up to -90 dBc. The proposed method helps reduce the cost of ADC production test, to extend test coverage and to enable built-in self-test and test-based self-calibration.

The use of simple digital window comparators for the on-chip evaluation of analogue signals is described. It is shown that a window can be created by choosing different gate input configurations. The potential problem of lot-to-lot variation of the comparator window position can be compensated by an on-chip repositioning technique. The components for the implementation comprise of a reference comparator and the evaluation comparators that are described along with the implementation of the technique. It is shown, that this technique allows the automatic lot condition adjustment for the evaluation comparator windows. The technique can optionally provide lot condition diagnostics for the test record as well as diagnosis of the actual window selection of the evaluation comparator.