1,721,243 research outputs found
Optimal sizing for a Battery-Supercapacitor Hybrid Energy Storage System
No full textIn this paper, an optimal sizing for a battery supercapacitor (SC) Hybrid Energy Storage System (HESS) for an Electric Vehicle (EV) is proposed. In particular, the aim of the work is the system weight and volume minimization. Two power-split strategies are considered, respectively based on a low-pass filter and on a moving average filter. The sizing results are validated by carrying out simulations in Matlab/Simulink environment and the SoC, current and voltage trends of the sources composing the HESS are evaluated
Supercapacitor-Based Shuttle Bus Characterization for Urban Charging Infrastructure
No full textThe need to reduce CO2 emissions leads the field of research to find the most suitable solutions to combat this phenomenon. The main solution is represented by the replacement of internal combustion engine vehicles with electric ones unitedly to the realization of microgrids for the urban vehicles charging. In this paper, the energy demand of a node of the proposed Urban Charging Infrastructure (UCI) is estimated through the determination of pre-defined shuttle bus loop routes. In addition, a criterion for the sizing of Supercapacitors (SCs) bank used as on-board storage is provided. Finally, simulation and experimental results are compared
A new Frequency Domain Measure of Causality based on Partial Spectral Decomposition of Autoregressive Processes and its Application to Cardiovascular Interactions
Full text linkWe present a new method to quantify in the frequency domain the strength of directed interactions between linear stochastic processes. This issue is traditionally addressed by the directed coherence (DC), a popular causality measure derived from the spectral representation of vector autoregressive (AR) processes. Here, to overcome intrinsic limitations of the DC when it needs to be objectively quantified within specific frequency bands, we propose an approach based on spectral decomposition, which allows to isolate oscillatory components related to the pole representation of the vector AR process in the Z-domain. Relating the causal and non-causal power content of these components we obtain a new spectral causality measure, denoted as pole-specific spectral causality (PSSC). In this study, PSSC is compared with DC in the context of cardiovascular variability analysis, where evaluation of the spectral causality from arterial pressure to heart period variability is of interest to assess baroreflex modulation in the low frequency band (0.04-0-15 Hz). Using both a theoretical example in which baroreflex interactions are simulated, and real cardiovascular variability series measured from a group of healthy subjects during a postural challenge, we show that – compared with DC– PSSC leads to a frequency-specific evaluation of spectral causality which is more objective and more focused on the frequency band of interest
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Performance Investigation of Fountaine Codes on a Simulated Terrestrial Free Space Optical Channel
No full text
Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability
Full text linkThe amount of information exchanged per unit of time between two dynamic processes is
an important concept for the analysis of complex systems. Theoretical formulations and
data-efficient estimators have been recently introduced for this quantity, known as the
mutual information rate (MIR), allowing its continuous-time computation for event-based
data sets measured as realizations of coupled point processes. This work presents the
implementation of MIR for point process applications in Network Physiology and
cardiovascular variability, which typically feature short and noisy experimental time
series. We assess the bias of MIR estimated for uncoupled point processes in the
frame of surrogate data, and we compensate it by introducing a corrected MIR (cMIR)
measure designed to return zero values when the two processes do not exchange
information. The method is first tested extensively in synthetic point processes
including a physiologically-based model of the heartbeat dynamics and the blood
pressure propagation times, where we show the ability of cMIR to compensate the
negative bias of MIR and return statistically significant values even for weakly coupled
processes. The method is then assessed in real point-process data measured from
healthy subjects during different physiological conditions, showing that cMIR between
heartbeat and pressure propagation times increases significantly during postural stress,
though not during mental stress. These results document that cMIR reflects physiological
mechanisms of cardiovascular variability related to the joint neural autonomic modulation
of heart rate and arterial compliance
Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
Full text linkIn this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of delta, theta, alpha, and beta electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability. MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain–body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly heart rate-respiration and delta-theta, theta-alpha, alpha-beta), but also statistically significant interactions between the two subnetworks (mainly heart rate-beta and heart rate-delta). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain–heart interactions and of brain–brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress
Adaptive scheduling of acceleration and gyroscope for motion artifact cancelation in photoplethysmography
Full text linkBackground and objective: Recently, various algorithms have been introduced using wrist-worn photo-plethysmography (PPG) to provide high accuracy of instantaneous heart rate (HR) estimation, including during high-intensity exercise. Most studies focus on using acceleration and/or gyroscope signals for the motion artifact (MA) reference, which attenuates or cancels out noise from the MA-corrupted PPG signals. We aim to open and pave the path to find an appropriate MA reference selection for MA cancelation in PPG.Methods: We investigated how the acceleration and gyroscope reference signals correlate with the MAs of the distorted PPG signals and derived both mathematically and experimentally an adaptive MA reference selection approach. We applied our algorithm to five state-of-the-art (SOTA) methods for the performance evaluation. In addition, we compared the four MA reference selection approaches, i.e. with acceleration signal only, with gyroscope signal only, with both signals, and using our proposed adaptive selection.Results: When applied to 47 PPG recordings acquired during intensive physical exercise from two differ-ent datasets, our proposed adaptive MA reference selection method provided higher accuracy than the other MA selection approaches for all five SOTA methods.Conclusion: Our proposed adaptive MA reference selection approach can be used in other MA cancelation methods and reduces the HR estimation error.Significance: We believe that this study helps researchers to address acceleration and gyroscope signals as accurate MA references, which eventually improves the overall performance for estimating HRs through the various algorithms developed by research groups
- …
