Reliable Signal Research Articles

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data

Abstract. Tropical cyclones are responsible for a large share of global damage resulting from natural disasters, and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA), presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with the data from CMIP models offering a reliable solution to resolve tropical cyclones in climate projections. We illustrate this by producing damage projections in representative concentration pathways (RCPs) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses. This model was primary developed to provide the economic and financial community with reliable signals allowing for a better quantification of physical risks in the long term, to estimate, for example, the impact on sovereign debt.

Intraday power trading: toward an arms race in weather forecasting?

We propose the first speculative weather-based algorithmic trading strategy on a continuous intraday power market. The strategy uses neither production assets nor power demand and generates profits purely based on superior information about aggregate output of weather-dependent renewable production. We use an optimized parametric policy based on state-of-the-art intraday updates of renewable production forecasts and evaluate the resulting decisions out-of-sample for one year of trading based on detailed order book level data for the German market. Our strategies yield significant positive profits, which suggests that intraday power markets are not semi-strong efficient. Furthermore, sizable additional profits could be made using improved forecasts of renewable output, which implies that the quality of forecasts is an important factor for profitable trading strategies. This has the potential to trigger an arms race for more frequent and more accurate forecasts, which would likely lead to increased market efficiency, more reliable price signals, and more liquidity.

A brachial artery-based system for blood pressure pulse wave measurement and its signal filtering technique

Human blood pressure (BP) and its pulse wave has always been a popular topic since it’s bound up with the heart related risks. In this paper, we mainly focus on the pulse wave of the brachial artery which is also widely used in the measurement of BP, hence we constructed a brachial artery-based system by using multi-Windkessel model and managed to generate a reliable pulse wave signal with adjustable authentic human parameters. On this basis, the signal filtering technique was also discussed. We mixed noises with different amplitude and frequency into the original signal intentionally, then several filters’ performances were compared. The designed combined filter represented a stable performance, and had little error in BP values, which ensure the stability and reliability of the designed system.

Precision agriculture intelligent connection network based on visual navigation

AbstractIn the process of contemporary agricultural production, the application of intelligent connection and automatic navigation technology of modern agricultural machinery is increasingly common. The application of visual navigation technology in the field of intelligent connection of agricultural machines can provide accurate and reliable positioning signals for agricultural machines so as to carry out intelligent and automatic agricultural machinery operations. In order to realise precision agricultural operation and intelligent navigation, a network of combining decision‐making by using visual navigation recognition and image processing is proposed. The system design mainly includes two parts: visual navigation system and control network. Agricultural machinery uses monocular vision image acquisition to collect farmland image. The computer analyzes the image, extracts the navigation centreline, obtains the navigation path information, and completes the identification and positioning of the navigation path. The simulation process verifies that the designed image navigation precision agricultural intelligent connection network can provide efficient, precise, and intelligent control of agricultural machinery.

A Stroke Estimation Method for Linear Oscillatory Machine Using an Improved Sliding Mode Observer

For the linear compressor system driven by a linear oscillatory machine (LOM), the piston stroke is very important to realize the high working efficiency for the whole system. In order to obtain precise and reliable piston stroke signals without using the position sensor, this article proposes a stroke estimation method for the LOM using an improved sliding mode observer (SMO). For the improved SMO, the integral sliding surface is chosen to reduce the chattering phenomenon and the steady-state error of the system, and a novel reaching law is introduced to construct the sliding control law, which can further reduce the influence of the sliding chattering. Due to the piston stroke being obtained by integrating the back electromotive force (EMF) as estimated by the improved SMO, the pure integration saturation problem will occur due to that the measured current and voltage signals always contain some dc components. In order to cope with this problem, a self-adaptive bandpass filter is designed, which can obtain a filtered signal with almost no amplitude and phase deviation by adaptively adjusting its center frequency. Simulation and experimental results confirm that the proposed improved SMO has higher stroke estimation accuracy than the back EMF integration and the conventional SMO.

Measuring brain beats: Cardiac-aligned fast functional magnetic resonance imaging signals.

Blood and cerebrospinal fluid (CSF) pulse and flow throughout the brain, driven by the cardiac cycle. These fluid dynamics, which are essential to healthy brain function, are characterized by several noninvasive magnetic resonance imaging (MRI) methods. Recent developments in fast MRI, specifically simultaneous multislice acquisition methods, provide a new opportunity to rapidly and broadly assess cardiac-driven flow, including CSF spaces, surface vessels and parenchymal vessels. We use these techniques to assess blood and CSF flow dynamics in brief (3.5min) scans on a conventional 3T MRI scanner in five subjects. Cardiac pulses are measured with a photoplethysmography (PPG) on the index finger, along with functional MRI (fMRI) signals in the brain. We, retrospectively, align the fMRI signals to the heartbeat. Highly reliable cardiac-gated fMRI temporal signals are observed in CSF and blood on the timescale of one heartbeat (test-retest reliability within subjects R2 > 50%). In blood vessels, a local minimum is observed following systole. In CSF spaces, the ventricles and subarachnoid spaces have a local maximum following systole instead. Slower resting-state scans with slice timing, retrospectively, aligned to the cardiac pulse, reveal similar cardiac-gated responses. The cardiac-gated measurements estimate the amplitude and phase of fMRI pulsations in the CSF relative to those in the arteries, an estimate of the local intracranial impedance. Cardiac aligned fMRI signals can provide new insights about fluid dynamics or diagnostics for diseases where these dynamics are important.

Optimal prototype design of dry textile electrode-based compression pants for surface electromyography measurements

PurposeTo provide guidelines for the development of textile electrode compression pants that collect reliable signals during surface electromyography (sEMG) measurements and maintain a comfortable level of pressure.Design/methodology/approachTo increase skin adhesion, 12 textile electrode bands for biceps brachii were prepared according to a combination of variables, namely, the type of the textile electrode, the pressure level and the presence or absence of an electrolyte. The dry textile electrode adopted herein was developed in terms of the size and material of the contact area, and a new electrode design was proposed. After examining the optimal design conditions by measuring the sEMGs during isometric exercise of the biceps brachii, prototype pants were designed based on the design variables that gave the most promising evaluation results. The completed prototype pants were verified through isometric thigh muscle exercises.FindingsIt was confirmed that the textile electrode was capable of EMG measurement with an excellent signal quality. Upon considering the comfort of wearing the device and the cost efficiency of dry electrodes, prototype pants that adopted a fit relative to a light clothing pressure (i.e. thigh: 1.3–1.9 kPa), and combined both silicon and silver thread embroidery with a wide contact area for stability, were designed and their sEMG measurements were confirmed.Originality/valueIn this study, wearable clothing based on textile electrodes was developed to ensure a comfortable fit from the wearer's perspective, and a design method was proposed for the development of low-cost SmartWear electrodes and circuits.

Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters

Female bonobos show social swelling by synchronizing their maximum swelling and increasing bonding

Different Old World primates show conspicuous anogenital swelling, with the Maximum Swelling Phase (MSP) signaling the ovulatory phase. MSP synchronization between females has been linked to social dynamics. In bonobos, characterized by female dominance, MSP is not a fully reliable signal of fertility because it may cover anovulatory periods. We investigated whether bonobo females synchronized their MSP and whether this phenomenon was modulated by social factors. Data were collected at LaValléedesSinges (France). In the period 2009–2022, swelling cycles data were collected daily on bonobo females (N = 9). In the period 2018–2022, ethological data (aggression/affiliation/socio-sexual behaviors) were also collected. We found that: (i) females synchronized their MSP and most likely experienced MSP onset following the MSP onset in other females; (ii) synchronization increased as the years spent together by females increased; (iii) synchronization preferentially occurred between females that affiliated less; (iv) synchronization on the MSP was linked to increased female-female socio-sexual contacts, which probably favored MSP synchronization maintenance. Hence, in bonobos MSP can be modulated by social factors and its synchronization, possibly underlying autonomic contagion, might have been positively selected during evolution in relation to the benefits females obtain in terms of intra-group cohesion.

An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments

In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual-based position re- identification and PnP (Perspective-n-Points, PnP) methods. When any agent can receive reliable GNSS signals, GNSS positioning information will greatly improve the positioning accuracy without changing the positioning algorithm framework. In order to achieve this function, we designed a set of two-stage position estimation algorithms. In the first stage, we used the modified ORB-SLAM3 algorithm for position estimation by fusing visual and IMU information. In the second stage, we integrated GNSS positioning and cooperative positioning information using the factor graph optimization (FGO) algorithm. Our framework consists of an UGV as the central server node and three UAVs carried by the UGV, that will collaborate on space exploration missions. Finally, we simulated the influence of different visibility and lighting conditions on the framework function on the virtual simulation experiment platform built based on ROS (robot operating system, ROS) and Unity3D. The accuracy of the cooperative localization algorithm and the single platform localization algorithm was evaluated. In the two cases of GNSS-denied and GNSS-challenged, the error of co-location reduced by 15.5% and 19.7%, respectively, compared with single-platform independent positioning.

(Invited) Nanostructured Electrochemical Devices and Self-Powered Systems for Biosensing

Self-powered systems for biosensing have attracted tremendous research interest in recent years, mainly due to the rapidly expanding market of wearable and portable devices for applications in clinical diagnosis and physiological monitoring. In our work, novel and unique hierarchical nanostructures were designed and synthesized to realize electrochemical devices with high performance, especially the sensor stability and energy storage capability. Meanwhile, scalable and printable approach was developed to integrate these electrochemical devices into monolithically integrated self-powered systems. The as-developed nanostructured electrochemical devices in conjunction with printable approach show great potency in fabrication of various wearable integrated self-powered devices for personalized healthcare monitoring applications. Our research highlights are as follow: Development of nanoporous membranes for electrochemical sensor applications. It eliminates enzymes escaping and provides sufficient surface area for molecular/ion diffusion and interactions, so as to ensure the sustainable catalytic activities of the sensors and generate reliable measurable signals during noninvasive monitoring. The highly improved stability of sensors is extremely desirable for investigation of metabolic activities in physiological systems.A fully integrated and self-powered system in a smartwatch fashion for continuous monitoring of sweat glucose levels during both equilibrium status and dynamic activities. The smartwatch can be self-powered by flexible photovoltaic cells, without external charging, the harvested energy can also be stored in the flexible Zn-MnO2 batteries as backup power source. It is also capable for real-time and in situ data analysis/display with integrated circuit board and E-ink screen.A monolithically integrated self-powered smart sensor system with energy supplied by fully printable planar supercapacitors and embedded solar cells, was fabricated on plastic substrate with inkjet printing technique as a proof-of-concept. The as-developed printable nanostructured electrochemical devices in conjunction with printable approach for system integration show great potency in fabrication of various wearable integrated self-powered devices for personalized healthcare monitoring applications.

Polydopamine assembled stable core-shell nanoworms-DNAzyme probe for selective detection of Pb2+ and in living cells imaging

Lead ion (Pb2+) is a major non-degradable environmental pollutant and poses threat to human health. DNAzymes have proved to be a promising tool for the detection of extracellular metal ions. Owing to cellular uptake, enzymatic degradation and reliable signal output, using DNAzymes to quantitative intracellular sensing of Pb2+ still presents challenges. In this study, we report a core-shell nanoworms-DNAzyme nanoprobe combing hairpin-like DNAzyme (H DNAzyme) with polydopamine-coated Au cores plasmonic “nanoworms” (Au@PDA NWs) for imaging of Pb2+ in living cells. PDA with good biocompatibility, high near-infrared (NIR) fluorescence quenching efficiency, and provides a larger surface for further functionalization of biomolecules. H DNAzyme can loaded onto Au@PDA NWs via coordination interaction and π–π stacking, resulting in significant quenching of their fluorescence by PDA shells and Au cores. In the presence of target Pb2+, the H DNAzyme are activated and release Cy5 fluorophore-labeled short strands from Au@PDA NWs. Therefore, an increased fluorescence signal is observed and allows imaging of Pb2+ in living cells. Moreover, the highly selective of H DNAzyme make the proposed method effectively discriminate against other nonspecific molecules. The Au@PDA NWs-H DNAzyme nanoprobe shows good biocompatibility and obviously decreased background fluorescence, which can monitor Pb2+ levels and biodistribution. The developed nanoprobe holds great potential for further extend to bioimaging of other metal ions.

Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.

Soft song provokes stronger aggressive responses than broadcast song in the Plain Laughingthrush (Pterorhinus davidi)

Many bird species produce low‐amplitude acoustic signals, known as soft songs, which are widely believed to function primarily in aggressive interactions. However, there are surprisingly few experimental studies that have examined whether soft songs are reliable signalling strategies for escalating aggressive interactions. Here, we investigate the functions of soft song in a colour‐marked population of the Plain Laughingthrush Pterorhinus davidi, using playback experiments combined with specimen‐based simulated territory intrusions. We found that playback of male soft song provoked quicker response and stronger aggressive intent by territorial pairs compared with playback of male broadcast song. Responses by territorial pairs to male soft song playback were significantly stronger compared with responses to broadcast song playback, with a greater number of flights over the speaker, more time spent within 1 m of the speaker and closer approaches to the speaker. Furthermore, closer approach with the production of soft song by focal males was the best predictor of aggressive intent in combined playback and specimen intrusion experiments. Our results suggest that male soft song is used as an aggressive signal in the Plain Laughingthrush, and represents the first evidence of such for a monogamous temperate duetting species.

Organizing Reliable Polymer Electrode Lines in Flexible Neural Networks via Coffee Ring-Free Micromolding in Capillaries.

With an increase in the demand for smart wearable systems, artificial synapse arrays for flexible neural networks have received considerable attention. A synaptic device with a two-terminal configuration is promising for complex neural networks because of its ability to scale to a crossbar array architecture. To realize practical crossbar arrays with a high density, it is essential to achieve reliable electrode lines that act as signal terminals. However, an effective method to develop intrinsically flexible signal lines in artificial neural networks has not been developed. In this study, we achieved reliable polymer signal lines for flexible neural networks using coffee ring-free micromolding in capillaries (MIMIC). In a typical MIMIC, the outward convective flow of the polymer solution inherently deteriorates the pattern fidelity. To achieve reliable conducting polymer (CP) lines, we precisely controlled the flow of the polymer solution in the MIMIC by inducing the Marangoni force. When the convective and Marangoni flows for the solution were balanced in the MIMIC, the CP line patterns were reliably produced with high fidelity. The developed CP lines exhibited superior conductivity and high mechanical flexibility. Moreover, flexible memristor arrays consisting of CP signal lines demonstrated a high potential for realizing practical neuromorphic systems linked to artificial intelligence.

Awards are career catalysts for young talents in association football

ABSTRACT Research question Despite the potential importance of awards as a possible career catalyst, research on awards is still in its infancy, specifically in sports management. Here, we address this shortcoming in the sports economics and management literature by exploring data from youth association football. Research methods Estimating different probit regression models, we analyze whether an early career award, the so-called Fritz Walter Medal, significantly affects the awardee’s career trajectory in a highly competitive environment, i.e. German youth football, where performance differences are often still hardly perceivable. Results and Findings We find that receiving an award seems, per se, to be a robust positive signal for a player’s future career success. Intriguingly, though, both the award characteristics and the awardee’s age only add limited explanatory power. Implications Our empirical results indicate that winning a prestigious award can represent a reliable status-oriented signal for individuals, especially young gifted talents entering highly competitive job markets.

Point-of-Care Immunoassay Based on a Multipixel Dual-Channel Pressure Sensor Array with Visual Sensing Capability of Full-Color Switching and Reliable Electrical Signals.

The point-of-care (POC) method with affordability and portability for the sensitive detection of biological substances is an emerging topic in rapid disease screening and personalized medicine. In this work, we demonstrated a diverse responsive platform based on a dual-channel pressure sensor (DCPS). The DCPS had a multilayer flexible architecture consisting of a photonic hydrogel with chromatic transitions and a piezoresistive pressure sensor as the electrical data transmission unit, both of which had the property of pressure-induced mechanical stimulus feedback. By incorporating a platinum nanoparticles-labeled detection antibody (PtNPs-dAb) into the sandwich-type immunoreaction for the target carcinoembryonic antigen (CEA, as a model analyte), gas decomposition could be triggered by the addition of hydrogen peroxide (H2O2) to induce a significant increase under pressure in a closed chamber. Meanwhile, the DCPS enabled an accurate electrical signal output, and the photonic hydrogel converted spatiotemporal stimuli into eye-readable colorations with string brilliance. In this way, the target concentration could be quantificationally related to the electrical response and intuitively perceived through visible color alterations. Under optimal conditions, a sensitive determination of CEA was performed in a detectable range of 0.3-60 ng/mL with a limit of detection (LOD) of 0.13 ng/mL. In addition, the proposed protocol had satisfactory selectivity, accuracy, and reproducibility. Furthermore, an array-based immunoassay device was fabricated to conceptually validate its application potential in high-throughput biomedical detection and inspire a dual-signal POC diagnostic platform in a friendly way for resource-limited settings.

Point-by-point inscribed sapphire parallel fiber Bragg gratings in a fully multimode system for multiplexed high-temperature sensing.

We study the point-by-point inscription of sapphire parallel fiber Bragg gratings (sapphire pFBGs) in a fully multimode system. A parallel FBG is shown to be critical in enabling detectable and reliable high-order grating signals. The impacts of modal volume, spatial coherence, and grating location on reflectivity are examined. Three cascaded seventh-order pFBGs are fabricated in one sapphire fiber for wavelength multiplexed temperature sensing. Using a low-cost, fully multimode 850-nm interrogator, reliable measurement up to 1500°C is demonstrated.

Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection.

With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.

A Survey of FDD-Based Channel Estimation Schemes With Coordinated Multipoint

New enabling technologies that include millimeter wave (mmWave) communication, massive multiple-input-multiple-output (MIMO), and ultradense networks are set to transform the wireless communication systems. These emerging technologies contribute toward the evolution of fifth generation (5G) systems that promise greater connectivity, throughput, mobility, and reliability for communication networks. This article presents a comprehensive overview of techniques for channel estimation, particularly, frequency division duplex (FDD)-based estimation techniques for a communication network. An exhaustive literature survey of FDD-based estimation schemes is presented, with a comprehensive review of the coordinated multipoint (CoMP) strategies in 5G radio networks, and its reliance on acquisition of channel state information (CSI). Since accurate CSI promotes the adoption of channel precoding, and reliable signal detection in FDD-based 5G systems, therefore, channel estimation for CSI in conjunction with CoMP transmission is preferable. We also present future research directions for FDD-based channel estimation and CoMP in 5G.