Frequency Bias Research Articles

Swimming With the COSMED AquaTrainer and K5 Wearable Metabolic System in Breath-by-Breath Mode: Accuracy, Precision, and Repeatability.

To compare ventilatory and cardiorespiratory responses between the COSMED AquaTrainer coupled with the K4b2 and K5 wearable metabolic systems in breath-by-breath mode over a wide range of swimming speeds. Seventeen well-trained master swimmers performed 2 front-crawl 7 × 200-m incremental intermittent protocols (increments of 0.05m·s-1 and 30-s rest intervals, with a visual pacer) with AquaTrainer coupled with either K4b2 or K5. Post hoc tests showed that swimming speed was similar (mean diff.: -0.01 to 0.01m·s-1; P = .73-.97), repeatable (intraclass correlation coefficient: .88-.99; P < .001), highly accurate, and precise (agreement; bias: -0.01 to 0.01m·s-1; limits: -0.1 to 0.1m·s-1) between all conditions. Ventilatory and cardiorespiratory responses were highly comparable between all conditions, despite a "small" effect size for fraction of expired carbon dioxide at the sixth 200-m step (0.5%; ηp2=.12; P = .04) and carbon dioxide production at the fifth, sixth, and seventh 200-m steps (0.3-0.5L·min-1; ηp2=.11-.17; P = .01-.05). We also observed high accuracy, which was greater for tidal volume (0.0-0.1L), minute ventilation (-3.7 to 5.1L·min-1), respiratory frequency (bias: -2.1 to 1.9breaths·min-1), and oxygen uptake (0.0-0.2L·min-1). Bland-Altman plots showed that the distribution inside the limits of agreement and their respective 95% CIs were consistent for all ventilatory and cardiorespiratory data. The repeatability (intraclass correlation coefficient) of tidal volume (.93-.97), minute ventilation (.82-.97), respiratory frequency (.68-.96), fraction of expired carbon dioxide (.85-.95), carbon dioxide production (.77-.95), fraction of expired oxygen (.78-.92), and oxygen uptake (.94-.98) data ranged from moderate to excellent (P < .001-.05). Swimming with the AquaTrainer coupled with K5 (breath-by-breath mode) yields accurate, precise, and repeatable ventilatory and cardiorespiratory responses when compared with K4b2 (previous gold standard). Swimming support staff, exercise and health professionals, and researchers can now relate differences between physiological capacities measured with the AquaTrainer while coupled witheither of these 2 devices.

Contact methods for registering respiratory rate: opportunities and perspectives

Introduction. Respiratory rate is known to be one of the most important indicators reflecting the vital functions of a person. An increase in respiratory rate can be found in many diseases and pathological conditions, for example, in chronic obstructive pulmonary disease, pneumonia, bronchial asthma, myocardial infarction, heart failure, anaemia, etc. Due to the active introduction of telemedicine monitoring into clinical practice, the measurement of the abovementioned indicator is particularly relevant for the purpose of early detection and prevention of complications of chronic non-infectious diseases, as well as dynamic monitoring of the condition of patients in both inpatient and outpatient settings.Aim. To search and update information about existing and promising developments for the control of respiratory rate based on different physical principles.Materials and methods. For this review we used databases PubMed, Scopus, MedLine and eLIBRARY. The following keywords were used for the search: “respiratory rate”, “contact”, “measurement”, “sensor”.Results. Contact methods for measuring respiratory rate include a wide range of sensors based on various physical principles. All types of sensors have their own application, but also they have some drawbacks. In order to achieve maximum accuracy of respiratory rate monitoring, it is necessary to carefully assess the conditions in which the patient is located, selecting the most appropriate technological solutions for them. Probably, complex systems, including several different sensors, are able to overcome many shortcomings. In addition, the development of information analysis methods, machine learning and artificial intelligence technologies can increase the sensitivity and accuracy of methods of measuring respiratory rate, reducing the frequency of bias associated with various artefacts.Conclusion. Thus, technological development opens up wide opportunities for long-term monitoring of vital functions, prevention and timely response to adverse events.

Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election

During the 2020 US presidential election, conspiracy theories about large-scale voter fraud were widely circulated on social media platforms. Given their scale, persistence, and impact, it is critically important to understand the mechanisms that caused these theories to spread. The aim of this preregistered study was to investigate whether retweet frequencies among proponents of voter fraud conspiracy theories on Twitter during the 2020 US election are consistent with frequency bias and/or content bias. To do this, we conducted generative inference using an agent-based model of cultural transmission on Twitter and the VoterFraud2020 dataset. The results show that the observed retweet distribution is consistent with a strong content bias causing users to preferentially retweet tweets with negative emotional valence. Frequency information appears to be largely irrelevant to future retweet count. Follower count strongly predicts retweet count in a simpler linear model but does not appear to drive the overall retweet distribution after temporal dynamics are accounted for. Future studies could apply our methodology in a comparative framework to assess whether content bias for emotional valence in conspiracy theory messages differs from other forms of information on social media.

Single hybrid population but multiple parental individuals at the origin of parthenogenetic rock lizards Darevskia sapphirina and D. bendimahiensis Schmidtler, & Eiselt Darevsky (1994) endemic to the area of Lake Van in East Turkey

Among vertebrates, obligate parthenogenesis is only found in Squamata, where it always has a hybrid origin and a few lizard genera contain most of the known hybridogenous parthenogenetic taxa. Parthenogenesis thus seems to be pre-conditioned at the genus level, but it is not clear how often the encounter between two parental sexually reproducing species can result in the parthenogenetic offspring, nor whether the success of such hybridization event requires certain conditions or the specific time frame. To address this question, we studied the rock lizards of genus Darevskia, where a pair of parental species, D. valentini and D. raddei, as well as their parthenogenetic daughter species D. bendimahiensis and D. sapphirina, are found in close proximity NE of the Lake Van in East Anatolia. Using ddRAD-seq genotyping on 19 parental and 18 hybrid individuals, we found that (i) all parthenogenetic individuals from both D. bendimahiensis and D. sapphirina have a monophyletic origin tracing back to a single initial hybrid population, but their current genetic variation is geographically structured; (ii) unlike the most probable paternal ancestor, the genetically closest extant population of the maternal ancestor is not the geographically nearest one; and (iii) in the parthenogens, about 1% of loci carry multiple haplotypes, frequently differentiated by multiple substitutions. This pattern, in addition to biases in the relative frequency of haplotypes of maternal and paternal origin, does not appear compatible with a scenario of the entire parthenogenic clonal population having descended from a single pair of parental individuals. Instead, the data suggest that multiple parental individual ancestries still persist in the parthenogenetic gene pool. This supports the notion that although hybridization leading to parthenogenesis is generally rare at the level of species, it may be more common at the individual/population level once the right conditions are met.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network.

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregards frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

An Improved Dual Second-Order Generalized Integrator Phased-Locked Loop Strategy for an Inverter of Flexible High-Voltage Direct Current Transmission Systems under Nonideal Grid Conditions

High-voltage flexible power systems, with their intrinsic characteristics, play an increasingly important role in electronic power systems. Synchronization between the inverter and the grid needs to be achieved by a phase-locked loop (PLL), the performance of which determines the quality of power transmission. This paper proposes a PLL adapted to extremely harsh grid conditions. Firstly, the traditional synchronous reference frame PLL and the dual second-order generalized integrator (DSOGI-PLL) are analyzed, and the errors in phase-locking and the shortcomings of these two methods in the presence of DC components in the grid are pointed out. Secondly, based on the harmonic grid voltage, a repetitive control internal model is introduced by DSOGI to realize the real-time tracking and regulation of the harmonic signals in order to suppress the harmonic voltage disturbance. In addition, a DC bias elimination and frequency adaptive method is proposed to solve the problems of DC bias and grid voltage frequency fluctuation in order to achieve adaptive tracking of the grid phase. Finally, the superiority of the proposed strategy is verified through simulations and experiments.

Fast Frequency Control Strategy for Direct Drive Wind Turbines Considering the Suppression of Secondary Frequency Drop in the Power Grid

Inertia and damping in the power system are important parameters in frequency regulation and oscillation suppression. Inertia will reduce the rate and amplitude of frequency changes after active power sudden changes. Damping in the system can effectively suppress low-frequency oscillations after grid disturbances. As the penetration rate of wind turbines increases, it will significantly reduce the effective inertia in the power grid and reduce the friendliness of wind power grid connection. To provide better frequency support for the power grid in the event of large disturbances in the power system, this paper proposes a fast frequency control strategy for wind farms that considers the suppression of secondary frequency dips in the power grid. The strategy combines frequency droop control with grid frequency bias control and evaluates the kinetic energy reserve online based on wind power prediction and hourly speed, thus balancing the frequency regulation and suppression of secondary frequency dips in the power grid. A 2.5 MW direct drive wind turbine simulation model was built based on the PSCAD simulation platform, verifying the effectiveness of the proposed comprehensive control strategy.

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T.

At ultrahigh field strengths images of the body are hampered by B1 -field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a "bias field" to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1 -field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1 -field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1 -field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.

Improved QoT estimations through refined signal power measurements and data-driven parameter optimizations in a disaggregated and partially loaded live production network

Accurate quality of transmission (QoT) estimations are essential enablers for future low-margin dynamic optical network operations. However, physical parameter measurement uncertainties and other intractable signal propagation effects degrade the accuracy of QoT estimation, especially in live production networks. The recent trend of network disaggregation further exacerbates the issue, and a vendor-agnostic accurate QoT estimator is much needed. In this paper, we study Gaussian-noise-model-based QoT estimation in a large-scale disaggregated and partially loaded live production network with monitored physical layer data spanning across 8 months. We first propose refining the signal power measurements by combining the inline amplifier and optical channel monitor (OCM) power measurements, followed by estimating the gain and noise power profiles of each inline amplifier, which in turn improves QoT estimation accuracy. We further introduce an optical multiplex section and frequency bias to the analytical model to incorporate intractable location-specific and spectral effects in the network and proposed data-driven parameter optimizations to learn the biases as well as erbium-doped fiber amplifier noise figures. The (mean, standard deviation) of the QoT estimation errors were reduced from (−0.1043, 0.6037) dB using average amplifier power and (−0.7875, 0.6337) dB using OCM power to (−0.0964, 0.4649) dB after input parameter refinements and were further reduced to (0.0046, 0.2377) dB with data-driven parameter optimization. The proposed methodologies are simple procedures that network operators can adopt to optimize analytical-model-based QoT estimators and/or serve as feature engineering procedures preceding machine-learning-based QoT in realistic disaggregated live production networks.

On mechanisms to control SiO2 etching kinetics in low-power reactive-ion etching process using CF4 + C4F8 + Ar + He plasma

This work discusses effects of gas mixing ratios and bias power (at 13.56 MHz and 2 MHz) on both gas-phase characteristics and etching kinetics of SiO2 in the CF4 + C4F8 + Ar + He plasma. The subject was the low input power regime (∼0.05 W/cm3). The substitution of C4F8 for CF4 exhibits no principal influence on electrons- and ions-related plasma parameters, produces weakly increasing F atom density as well as causes a comparable increase in the etching rate. The latter is provided by the chemical etching pathway with the nearly constant reaction probability. The substitution of Ar for He sufficiently affects plasma parameters, reduces the F atom density, but accelerates the etching process due to increasing reaction probability. Probably, the latter traces decreasing surface passivation by fluorocarbon radicals. Bias parameters do not disturb bulk plasma, but do influence the etching kinetics through the ion bombardment energy. The growth of etching rate vs. bias power mainly appears due to increasing efficiency of chemical etching pathway. The 2 MHz bias frequency produces lower etching rates in spite of higher ion bombardment energies. This effect is probably due to higher desorption rate and lower sticking probability for F atoms. It was found that most effective parameters to control etching profile are Ar/He mixing ratio and bias frequency.

The Impact of High-Frequency Chest-Wall Compression on Mechanical Ventilation Delivery and Flow Bias.

It is unclear if high-frequency chest-wall compression (HFCWC) has a role to assist with secretion clearance in patients on mechanical ventilation. The effect of HFCWC on the delivery of mechanical ventilation is unknown. This study describes the effect of HFCWC on mechanical ventilation delivery and flow bias in an orally intubated and mechanically ventilated bench model. An orally intubated mannequin was mechanically ventilated in 5 commonly used modes of ventilation at settings that reflect current practice. HFCWC was applied via a randomized combination of oscillation frequencies and pressure settings. Mechanical ventilator flow, flow bias, and breathing frequency were measured before and during the application of HFCWC. HFCWC led to 3- to 7-fold increases in ventilator-delivered breathing frequency during synchronized intermittent mandatory ventilation, bi-level (with pressure support), bi-level-assist, and pressure-regulated volume control modes of ventilation. Only in the bi-level mode without pressure support was the ventilator breathing frequency unaffected by HFCWC. During HFCWC, peak inspiratory flow to peak expiratory flow ratios toward an expiratory flow bias, particularly at higher HFCWC pressures, only in pressure-regulated volume control and synchronized intermittent mandatory ventilation modes were peak inspiratory flow to peak expiratory flow ratios of <0.9 generated that would facilitate secretion clearance. HFCWC led to 3- to 7-fold increases in ventilator breathing frequency delivered by mechanical ventilation except in the bi-level mode. The bi-level mode may be the optimal mode to use HFCWC to minimize disruption to the delivered ventilator breathing frequency. The peak inspiratory flow to peak expiratory flow ratios < 0.9, the optimal flow bias for secretion clearance, was only achieved in the pressure-regulated volume control and synchronized intermittent mandatory ventilation modes. However, the findings in this bench model with a fixed low compliance may not be generalizable to the patient in the ICU, and we recommend further investigation into the effects of HFCWC in the patient in the ICU.

Development of highly sensitive relative humidity sensor based on nanoporous PCPDTBT thin film

A novel type of capacitive humidity sensor with surface-type electrode geometry is presented in the present study. The high sensitivity and significantly high bandwidth associated with this planar-type humidity sensor (Al/PCPDTBT/Al) allow the facile and reliable estimation of ambient humidity. The use of a polymer sensing material Poly[2,6-(4,4-bis-(2-ethylhexyl)− 4 H-cyclopenta[2,1-b;3,4-b՛]dithiophene)-alt-4,7(2,1,3-benzothiadiazole)] (PCPDTBT) with hierarchical nanoporous surface morphology and large surface/volume ratio ensures the sensor’s stable performance coupled with swift response to ambient humidity changes. Specifically, at a relatively lower frequency bias signal (i.e., 1 kHz), a fairly higher order of humidity sensitivity has been observed, i.e., 415.07 pF/%RH. While, sensitivities at significantly higher AC signal frequencies (i.e., 10 and 100 kHz) have been experimentally determined to be 345.18 pF/%RH and 154.72 pF/%RH, respectively. Likewise, the resistive sensitivity of the humidity sensor has been estimated to be − 3624.88 kΩ/%RH, − 2381.11 kΩ/%RH and − 1338.14 kΩ/%RH, at 1, 10 and 100 kHz test frequencies, respectively. The operating mechanism of the capacitive humidity sensor relies on an increase in the dielectric constant of the PCPDTBT active sensing layer accompanied by the rise in the ambient humidity levels. In addition to enhanced bandwidth and improved capacitive and resistive sensitivity, the mean response & recovery time of the fabricated device have been recorded to be ∼ 4 min, 17 s and ∼8 min, 43 s, respectively.

Manipulation of phonon states in ion traps by shortcuts to adiabaticity

Shortcuts to adiabaticity (STA) provides the possibility of high accuracy manipulation of phonon states in ion traps. We propose a scheme realized experimentally for manipulating phonon states using STA and confirmed its effectiveness through generating Fock states. Our results show that the duration of the STA manipulation of phonon states is 16 times faster than that of the adiabatic evolution, and the non-resonant excitation can be suppressed by laser bias frequency, which are confirmed by experimental results. Moreover, we also carried out an experimental research on the robustness of STA, showing good robustness respect to the pulse shape deformation, bias noises and stochastic noise. This might lead to a useful step toward realizing fast and noise-resistant quantum manipulation within current experimental capacity.

Indium tin oxide nanoparticles induced molecular rearrangement in nematic liquid crystal material

In the present article, we report the influence of indium tin oxide (ITO) nanoparticles (NPs) on the macroscopic molecular arrangement of a nematic liquid crystal (NLC) material namely 4 cyano-4ʹ-pentylbiphenyl (5CB). To achieve the objective, we have adopted various concentrations (0.25, 0.5 and 1 wt%) of ITO NPs in 5CB and filled them in the homogeneously aligned sample cells to observe their influence on the dielectric and optical texture of the NLC under investigation. Firstly, polarizing optical micrographs under crossed polarizers were recorded to observe the uniform dispersion and miscibility of ITO NPs in the nematic phase of 5CB. These micrographs also infer the molecular rearrangement of 5CB material that is occurred under the simultaneous and cumulative anchoring effect induced by ITO NPs and aligning layers. Furthermore, the dielectric parameters (permittivity, loss and dielectric loss factor) of pristine and composites as the function of frequency, temperature and DC biasing have been studied. Surprisingly, dielectric permittivity and relaxation frequency of 0.25 wt% ITO NPs composite was found to be the maximum that is decreased with further increment in the concentration of ITO NPs. We have also confirmed the thermal stability of 0.25 wt% ITO NP-induced changes in 5CB. Apart from it, by virtue of temperature-dependent dielectric anisotropy and optical textures, we have reported a significant decrement (∼14 %) in clearing temperature of 5CB. These results will pave the way to devise a system with ITO NPs as the dopant in order to obtain desired phase transition temperature of liquid crystal materials.

Sb + Lu co-doped TiO2 ceramics with ultralow loss, high permittivity, and excellent DC bias voltage stability

Dielectric materials with large dielectric constants are increasingly used in electronics. In this study, dense (Sb0.5Lu0.5)xTi1-xO2 ceramics with strong electron-pinning clusters were prepared using a solid-state reaction. (Sb0.5Lu0.5)0.99Ti0.01O2 exhibited a giant dielectric constant of 1.51 × 104 and an ultralow dielectric loss of 0.004 at room temperature (RT). This ceramic also exhibited excellent frequency, temperature, and DC bias stabilities (RT–250 °C, 20 Hz–5 MHz, 0–32 V) owing to the high grain boundary resistance and strong electron-pinning effect. XPS, impedance spectroscopy, and relaxation behavior analysis revealed that electron-pinning, internal barrier layer, and electrode effects resulted in excellent dielectric behavior. This study demonstrates that the construction of defect clusters with strong electron-pinning effects is an effective method for obtaining giant dielectric ceramics with ultralow loss and temperature/DC bias stability.

Improved PSS timing synchronization algorithm and IoT application in cross-border e-commerce risk identification

In this paper, a new timing synchronization algorithm for the main synchronous signal (PSS) is proposed for the risk identification of cross-border e-commerce in the Internet of things, aiming at the problems of poor performance of anti frequency bias and high computational complexity of the improved PSS timing synchronization algorithm. Based on the piecewise correlation algorithm, the normalized frequency deviation of PSS sequence is preset. The segmented ones are pre stored at the terminal by using the conjugate symmetry of PSS sequence. The fast correlation of each segment correlation window is realized by combining convolution and overlapping reservation block method. Then, the threshold judgment is made after the time delay accumulation of the correlation values of the segments is made, so as to complete the joint detection of timing synchronization and coarse frequency deviation. The simulation results show that the algorithm can improve the performance of the system anti frequency offset effectively, reduce the complexity of the calculation and show that the timing synchronization conditions of the Internet can be satisfied. At the same time, under the background of the current development of cross-border logistics, this paper reviews the current research status of Transnational E-commerce logistics and Transnational E-commerce logistics risk. By comparing the advantages and disadvantages of various risk assessment methods, neural network and genetic algorithm are selected as the basic risk assessment methods in this paper. Based on the improved PSS timing synchronization algorithm and the Internet of things, the risk indicators of e-commerce logistics transnational will be selected from five risk dimensions: platform risk, customs clearance risk, organizational risk, process risk and environmental risk. Through the comprehensive literature and expert’s opinion, the logistics risk assessment index system of cross-border e-commerce is established.

Incident Power and Relative Phase Distribution Mapping in Reconfigurable Intelligent Surfaces Using Energy Harvesting

One of the major challenges of reconfigurable intelligent surfaces (RISs) is the large state-selection search space that demands real-time programmability and fast processing. The knowledge of incident power and relative phase distribution across an RIS can potentially simplify the state-selection process. Herein, we present a novel method of obtaining incident power and relative phase distribution using energy harvesting circuits (rectifiers) that can be integrated into the RIS. Four versions of the energy harvesting circuit are proposed that offer great control over the sensitivity of the system. The sensitivity of the proposed rectifier circuit is -42 dBm for direct rectification. The inclusion of direct current (DC) bias and radio frequency (RF) amplifiers greatly increases the sensitivity of the rectifiers. The received power and phase distribution across the unit dipoles of RIS are simulated to demonstrate the potential of the proposed method. Experiments are performed in an indoor environment at 2.1 GHz. Experimental results show that the proposed energy harvesting method can successfully predict the received power distribution for angular and perpendicular incidence of the incident radio waves.

Frequency-independent smartphone peripherals energy consumption estimation

The power consumption of mobile devices is a hot topic these days, and it is important to address it when developing applications. One of the most popular ways to measure it is accessing internal sensors using Android Debug Bridge (ADB). We discovered that measurement frequency may skew the power readings. Based on this approach we propose our own algorithm for calculating smartphone energy consumption constants — the power in milliamperes at nominal voltage for different peripherals states. Our algorithm takes measurement frequency bias into account, and its results are compared with the method previously published in literature as well as the baseline data from power profile. We conclude that the developed approach provides better estimation.

Performance Assessment of Chirps, Persiann – CDR, IMERG, and TMPA Precipitation Products across Nepal

The observation of hydrological as well as meteorological data is very essential for any kind of hydraulic and hydrological study. In Nepal, due to the significant variation of topography and climatic characteristics as well it is necessary to establish the meteorological stations densely, here only 231 meteorological stations are available and are handled by government organizations Department of Hydrology and Meteorology (DHM). Even though, due to many limitations, the satellite data is very useful for the water resources study. There are so many satellite products available but the performance of these products varies from place to place. In this study, the performance of four satellite products i.e., Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Integrated Multi-Satellite Retrievals for GPM (IMERG), and TRMM Multi-satellite Precipitation Analysis (TMPA) all over Nepal are evaluated with different elevation bands. The performance of each product is evaluated by Probability of Detection (POD), Critical Success Index (CSI), Frequency Bias Index (FBI), False Alarm Ratio (FAR), Root Mean Square Error (RMSE), and Percentage Bias (PBIAS). After analysis of each product, the PERSIANN-CDR data set gives a reasonable performance for all elevation bands after bias correction.