1,721,041 research outputs found
Diagnostics of electrocatalytic systems by electrochemical impedance spectroscopy
Full text linkThe demand for new electrochemical reaction technologies and related engineering aspects is rising due to the current transition to green technologies and to use of renewable electricity sources. In this context, research of new electro-catalytic pathways to improve processes' efficiency and reduce their costs is essential. Electrochemical characterizations are usually employed in the study of new electrocatalysts and electrochemical systems. Among them, electrochemical impedance spectroscopy (EIS) is nowadays highly exploited to investigate charge transport and transfer phenomena. Never-theless, EIS can be used for other purposes as well. This review will focus on the use of the EIS technique as a diagnostic tool in the electrocatalysis field. Surprisingly, among the numerous electrochemical reactions, the areas in which EIS is employed as a monitoring tool are very few. The most important are the ones with a high technological maturity level and, therefore, that are already employed in the industry or for commercial applications: e.g., batteries, fuel cells, and bio-sensors. Devices belonging to these groups need to control their working conditions with fast and reliable methods, even at the cost of losing a small amount of precision degree. In this perspective, EIS is often used in combination with machine learning algorithms to develop easy-to-use diagnostic devices that aim to a rapid indication of the system status rather than a precise understanding of all the underlying processes
A novel system for measuring visual potentials evoked by passive head-mounted display stimulators
Full text linkPurpose
The objective of this work is to evaluate the
performances of a novel integrated device, based on
passive head-mounted display (HMD), for the pattern
reversal visual evoked potential (PR-VEP) clinical
test.
Methods
Google Cardboard
Ò
is used as passive
HMD to generate the checkerboard pattern stimuli
through an Android
Ò
application. Electroencephalo-
graphic signals are retrieved and processed over 20
subjects, 12 females and 8 males between 20 and
26 years. Morphological PR-VEPs and frequency
response were compared with previous literature
results, to test the reproducibility and the efficacy of
the proposed solution.
Results
PR-VEPs evoked by our novel prototype
showed typical triphasic waveforms in moderate
agreement with those obtained with other more
expensive HMDs and standard commercial devices.Statistical analysis did not highlight strong differences
among the systems over the features analyzed except
for the P100 amplitude and peak time (**
p
0.005).
Conclusion
The proposed solution opens the door
for a new generation of non-invasive first-level
diagnostic devices of optic nerve pathologies inex-
pensive and easy to access
Evaluation of Machine Learning Models for Water Stress Detection Using Stem Impedance
No full textFood security, producing enough food for every person on the planet, is becoming a significant issue. Increasing world population and climate change are setting new challenges to food production. Water stress can cause severe damage to crops, and detecting and preventing this threat is crucial. Smart agriculture and the use of sensors directly on the field is a promising and rapidly evolving solution. Data collected by a large number of sensors must be analyzed and efficiently interpreted. In this context, machine learning is an effective solution. This article conducts a comparative analysis of several well-established machine learning models, all trained on a dataset enriched with a novel parameter for the assessment of plant health, the stem electrical impedance (modulus and phase). This feature gives promising results since it is a direct parameter of the plant itself. Moreover, the inclusion of the stem impedance parameter significantly boosted the model's performance, notably enhancing the effectiveness, particularly evident in the case of the top-performing model in this study, the random forest algorithm. When incorporating stem electrical impedance, this model achieved an impressive F1 score of 98%, markedly surpassing the 88% obtained in its absence. As a complementary analysis, a permutation feature performance analysis was conducted, highlighting the potential of stem impedance modulus as a promising feature for evaluating plant watering conditions. The removal of impedance modulus from the training model resulted in an average classification performance loss of 25% in terms of F1 score, suggesting how impedance monitoring is a promising approach for plant health management
Unveiling Mental Workload via PPG: Morphological and Respiratory Feature–Driven Machine Learning Classification
No full textThe integration of the latest artificial intelligence (AI)-based technologies in high-risk operational sectors requires, as an essential prerequisite, the availability of reliable feedback on the mental workload (MWL) perceived by the operator. In this context, the analysis of variations in physiological signals remains one of the most promising and scalable approaches for estimating MWL in varied application scenarios. This study proposes the development of a predictive model solely based on the analysis of the photoplethysmographic (PPG) signal, a technology that is easily integrable into wearable systems and already widely used in clinical and consumer applications. In addition to the traditional variables associated with heart rate variability (HRV), which have been explored in previous literature, this work introduces an innovative analysis of morphological parameters of the individual pulse wave, which have not been previously investigated in the context of MWL. Furthermore, by reconstructing the respiratory contribution from the PPG signal, additional features related to respiratory variability were derived. The machine learning models were trained using the publicly available MAUS dataset, which includes recordings from 22 subjects exposed to controlled cognitive workload conditions through the N-back test. The obtained results, with an accuracy of 81.5% in the binary classification between low and high MWL levels, confirm the effectiveness of the proposed approach and highlight its potential for continuous, non-invasive monitoring of mental workload through a single wearable sensor
Characterization of two novel low frequency microphones for photoacoustic gas sensors
No full textThe photoacoustic principle for measuring gas concentration is well established based upon macromechanical assembly. However, microsystem based photoacoustic could benefit from smaller size and lower cost, but needs a high resolution microphone to be competitive. Here we present the characterization of two dedicated low frequency microphones. Both microphones have been fabricated in the same MPW foundry process, but additional post processing step enabling narrow ventilation slots through membranes of multiple thickness that increase the sensitivity of microphones, has been used. Preliminary results indicate a sensitivity of 800 μV/V-Pa, sufficient for high resolution photoacoustic gas sensor
Carbon Nanotubes as an Effective Opportunity for Cancer Diagnosis and Treatment
No full textDespite the current progresses of modern medicine, the resistance of malignant tumors to present medical treatments points to the necessity of developing new therapeutic approaches. In recent years, numerous studies have focused their attention on the promising use of nanomaterials, like iron oxide nanowires, zinc oxide or mesoporous silica nanoparticles, for cancer and metastasis treatment with the advantage of operating directly at the bio-molecular scale. Among them, carbon nanotubes emerged as valid candidates not only for drug delivery, but also as a valuable tool in cancer imaging and physical ablation. Nevertheless, deep investigations about carbon nanotubes’ potential bio-compatibility and cytotoxicity limits should be also critically addressed. In the present review, after introducing carbon nanotubes and their promising advantages and drawbacks for fighting cancer, we want to focus on the numerous and different ways in which they can assist to reach this goal. Specifically, we report on how they can be used not only for drug delivery purposes, but also as a powerful ally to develop effective contrast agents for tumors’ medical or photodynamic imaging, to perform direct physical ablation of metastasis, as well as gene therapy
An Hardware-In-the-Design Methodology for Wireless Sensor Networks based on Event-Driven Impulse Radio Ultra-Wide Band
No full textThis paper conceptualizes and comments an Hardware-In-the-Loop-based methodology for the design and test of wireless links based on Impulse-Radio UWB. The paper analyses standard HDL simulation EDA tools for interfacing compatibility with the physical peripherals in a generic HIL configuration to define a general Hardware-In-the-Design methodology, i.e. the HIL validation in the first steps of a standard design flow. Our concept has been compared with AMS-HDL models and standard simulation flows and with real-time simulations on a flexible platform (e.g. FPGA). The design methodology is applied to a Wireless Sensor Network design based on event-driven IR-UWB (one-way node-to-node communication). Advantages of the introduced HIDF technique compared to standard design flows are presented and discusse
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