1,720,979 research outputs found
On-Line Shelf-Life Prediction in Perishable Goods Chain Through the Integration of WSN Technology With a 1st Order Kinetic Model
No full textThe improvements in sensors and wireless technology offer an effective way to enhance food safety and certification along all the perishable goods supply-chain, in order to reduce food waste and losses, while guaranteeing a high degree of quality and preventing diseases directly related to the use of expired or harmful products. In this paper, a complete system for continuous environmental parameters (i.e. temperature, light exposition and relative humidity) acquisition and real-time shelf-life prediction of monitored product is proposed. An algorithm based on a 1 st order kinetic model of the product quality decay with a variation rate evaluated accordingly to the Arrhenius law is proposed. A case study is also shown, i.e.: data during the storage phase of agricultural product (tomatoes) have been acquired through a wireless sensor networks and uploaded to a cloud service. The collected data, a sample per 15 minutes, are processed by the computation algorithm implemented on laptop: the overall delay due to data download and processing is just about 0,3 s. As consequence, the remaining shelf-life of the food can be estimated with a 5% uncertainty with a 2K temperature sensor, highlighting critical situation in the manufacturing environment and allowing timely intervention
Wireless brain-computer interface for wheelchair control by using fast machine learning and real-time hyper-dimensional classification
No full textThis paper presents a noninvasive brain-controlled P300-based wheelchair driven by EEG signals to be used by tetraplegic and paralytic users. The P300 - an Evoked Related Potential (ERP) - is induced for purpose by visual stimuli. The developed Brain-Computer Interface is made up by: (i) acquisition unit; (ii) processing unit and (iii) navigation unit. The acquisition unit is a wireless 32-channel EEG headset collecting data from 6 electrodes (parietal-cortex area). The processing unit is a dedicated µPC performing stimuli delivery, data gathering, Machine Learning (ML), real-time hyper-dimensional classification leading to the user intention interpretation. The ML stage is based on a custom algorithm (t-RIDE) which trains the following classification stage on the user-tuned P300 reference features. The real-time classification performs a functional approach for time-domain features extraction, which reduce the amount of data to be analyzed. The Raspberry-based navigation unit actuates the received commands and support the wheelchair motion using peripheral sensors (USB camera for video processing, ultrasound sensors). Differently from related works, the proposed protocol for stimulation is aware of the environment. The experimental results, based on a dataset of 5 subjects, demonstrate that: (i) the implemented ML algorithm allows a complete P300 spatio-temporal characterization in 1.95 s using only 22 target brain visual stimuli (88 s/direction); (ii) the complete classification chain (from features extraction to validation) takes in the worst case only 19.65 ms ± 10.1, allowing real-time control; (iii) the classification accuracy of the implemented BCI is 80.5 ± 4.1% on single-trial
Enhancement of Stability to Light in Field-Effect Transistors Based on Cumulenic sp-carbon Wires by Opaque Gating Electrodes
No full textCumulenic sp-hybridized carbon wires are receiving attention both for their excellent intrinsic electronic properties and for their potential exploitation as active semiconducting layers in large-area and flexible organic electronic devices. Such molecules exhibit a highly conjugated backbone chain, an equally high degree of electronic tunability, and offer an opportunity to better understand the elusive carbon allotrope known as carbyne. Among this class of molecules, tetraphenyl[3]cumulene ([3]Ph) has emerged as a reference point for applications in organic field-effect transistors (OFETs), owing to promising charge carrier mobility of > 0.1 cm2/Vs. However, photoinduced degradation of [3]Ph is a significant limitation to its possible future applications. Here, a gating strategy based on an opaque, spray-coated activated carbon (AC) based electrode is investigated to mitigate photodegradation in [3]Ph-based OFETs. Top-gate bottom-contact transistors have been fabricated and tested under various light conditions, comparing devices gated either by a transparent layer of PEDOT:PSS or opaque layers of AC, to probe light-induced degradation. Unlike PEDOT:PSS devices, the AC-gated transistors maintain consistent performance under light, demonstrating potential for practical applications of [3]Ph, similar cumulenes or even other photodegradable semiconductors in organic electronics
Towards P300-based mind-control: a non-invasive quickly trained BCI for remote car driving
No full textThis paper presents a P300-based Brain Computer Interface (BCI) for the control of a mechatronic actuator (i.e. wheelchairs, robots or even cars), driven by EEG signals for assistive technology. The overall architecture is made up by two subsystems: the Brain-to-Computer System (BCS) and the mechanical actuator (a proof of concept of the proposed BCI is shown using a prototype car). The BCS is devoted to signal acquisition (6 EEG channels from wireless headset), visual stimuli delivery for P300 evocation and signal processing. Due to the P300 inter-subject variability, a first stage of Machine Learning (ML) is required. The ML stage is based on a custom algorithm (t-RIDE) which allows a fast calibration phase (only ~190 s for the first learning). The BCI presents a functional approach for time-domain features extraction, which reduces the amount of data to be analyzed. The real-time function is based on a trained linear hyper-dimensional classifier, which combines high P300 detection accuracy with low computation times. The experimental results, achieved on a dataset of 5 subjects (age: 26 ± 3), show that: (i) the ML algorithm allows the P300 spatio-temporal characterization in 1.95 s using 38 target brain visual stimuli (for each direction of the car path); (ii) the classification reached an accuracy of 80.5 ± 4.1% on single-trial P300 detection in only 22 ms (worst case), allowing real-time driving. For its versatility, the BCI system here described can be also used on different mechatronic actuators
An Edible Rechargeable Battery
No full textThis is a repository copy of the published article Advanced Materials 2023, 221140
Designing a cyber-physical system for fall prevention by cortico-muscular coupling detection
No full textThe authors present wearable noninvasive electronics that prevent a human from falling. It deducts a probable fall from EEG and EMG information and provides a real-time alarm signal for protection
A digital processor architecture for combined EEG/EMG falling risk prediction.
No full textThe brain signal anticipates the voluntary movement with patterns that can be detected even 500ms before the occurrence. This paper presents a digital signal processing unit which implements a real-time algorithm for falling risk prediction. The system architecture is designed to operate with digitized data samples from 8 EMG (limbs) and 8 EEG (motor-cortex) channels and, through their combining, provides 1 bit outputs for the early detection of unintentional movements. The digital architecture is validated on an FPGA to determine resources utilization, related timing constraints and performance figures of a dedicated real-time ASIC implementation for wearable applications. The system occupies 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory of an Altera Cyclone V FPGA for a processing latency lower than 1ms. Outputs are available in 56ms, within the time limit of 300 ms, enabling decision taking for active control. Comparisons between Matlab (used as golden reference) and measured FPGA outputs outline a very low residual numerical error of about 0.012% (worst case) despite the higher float precision of Matlab simulations and losses due to mandatory dataset conversion for validation
Gait analysis and quantitative drug effect evaluation in Parkinson disease by jointly EEG-EMG monitoring
Get PDFThis work addresses the rising need for a diagnostic tool for the evaluation of the effectiveness of a drug treatment in Parkinson disease, allowing the physician to monitor of the patient gait at home and to shape the treatment on the individual peculiarity. In aim, we present a cyber-physical system for real-time processing EEG and EMG signals. The wearable and wireless system extracts the following indexes: (i) typical activation and deactivation timing of single muscles and the duty cycle in a single step (ii) typical and maximum co-contractions, as well as number of co-contraction/s. The indexes are validated by using Movement Related Potentials (MRPs). The signal processing stage is implemented on Altera Cyclone V FPGA. In the paper, we show in vivo measurements by comparing responses before and after the drug (Levodopa) treatment. The system quantifies the effect of the Levodopa treatment detecting: (i) a 17% reduction in typical agonist-antagonist co-contractions time (ii) 23.6% decrease in the maximum co-contraction time (iii) 33% decrease in number of critical co-contraction. Brain implications shows a mean reduction of 5% on the evaluated potentials
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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