Single Sensor Research Articles

Fault diagnosis of plunger pump based on audio signal combined with meta-transfer learning

To solve the problems of insufficient samples and weak fault features of audio signals in the fault diagnosis of plunger pump, this paper proposes a fault diagnosis method of plunger pump based on audio signal combined with meta-transfer learning (MTL-PAFD). The method takes the audio signals of the plunger pump as samples, which are acquired by a single sensor. Through the Gammatone filter bank processing, the representation ability of the audio signal under strong noise interference is effectively improved. Then combined with meta-transfer learning, the few-shot fault diagnosis of plunger pump is realized. In addition, according to the actual needs of fault diagnosis of plunger pump, the test method of meta-transfer learning in fault diagnosis application is improved, which can process unknown fault classes adaptively. Experimental results show that MTL-PAFD has a fault diagnosis accuracy of 91.41% for seen classes. After fast adaptive learning, it can achieve an accuracy of 89.64% when identifying unseen fault classes.

DESIGN AND MODELLING OF GROUND PROXIMITY WARNING SYSTEM (GPWS) FOR TRAINING AID

Controlled flight into terrain (CFIT) is the most common type of aircraft crashes in the world of aviation today. Following the sheer volume of terrain collisions, there was an introduction of CFIT classification to enable the grouping into one category of all crashes involving aircrafts colliding with terrains. To avert CFIT accidents, the Ground Proximity Warning System (GPWS) was introduced onboard the aircraft for the flight crew to be aware of the moment the aircraft is approaching a ground terrain via automatic and very distinct acoustic warning. This paper presents a digital display ground proximity warning system as a teaching aid with audio prompt using ultrasonic sensor to produce forward distance from aircraft to obstacle ahead, height, inclination of aircraft in relation to its terrain beneath the aircraft and a digital computer interface for instrument visualization for better performance using graphic user interface (GUI) thereby enabling clearer understanding on GPWS. The Arduino IDE using Arduino sketch and C language were used on the Arduino Uno. The aircraft was integrated with different electronic modules which includes Arduino single board, Ultrasonic sensor, accelerometer and transceiver module, and all these uses the Arduino to read the signal from the sensor, processes and transmit to the base station wirelessly via radio frequency. Simulation results indicates that the responses of the models are a replica of the actual aircraft operations.

Research on Environment Perception System of Quadruped Robots Based on LiDAR and Vision

Due to the high stability and adaptability, quadruped robots are currently highly discussed in the robotics field. To overcome the complicated environment indoor or outdoor, the quadruped robots should be configured with an environment perception system, which mostly contain LiDAR or a vision sensor, and SLAM (Simultaneous Localization and Mapping) is deployed. In this paper, the comparative experimental platforms, including a quadruped robot and a vehicle, with LiDAR and a vision sensor are established firstly. Secondly, a single sensor SLAM, including LiDAR SLAM and Visual SLAM, are investigated separately to highlight their advantages and disadvantages. Then, multi-sensor SLAM based on LiDAR and vision are addressed to improve the environmental perception performance. Thirdly, the improved YOLOv5 (You Only Look Once) by adding ASFF (adaptive spatial feature fusion) is employed to do the image processing of gesture recognition and achieve the human–machine interaction. Finally, the challenge of environment perception system for mobile robot based on comparison between wheeled and legged robots is discussed. This research provides an insight for the environment perception of legged robots.

Upconversion Luminescence Response of a Single YVO4:Yb, Er Particle.

We present the results of the luminescence response studies of a single YVO4:Yb, Er particle of 1-µm size. Yttrium vanadate nanoparticles are well-known for their low sensitivity to surface quenchers in water solutions which makes them of special interest for biological applications. First, YVO4:Yb, Er nanoparticles (in the size range from 0.05 µm up to 2 µm), using the hydrothermal method, were synthesized. Nanoparticles deposited and dried on a glass surface exhibited bright green upconversion luminescence. By means of an atomic-force microscope, a 60 × 60 µm2 square of a glass surface was cleaned from any noticeable contaminants (more than 10 nm in size) and a single particle of 1-µm size was selected and placed in the middle. Confocal microscopy revealed a significant difference between the collective luminescent response of an ensemble of synthesized nanoparticles (in the form of a dry powder) and that of a single particle. In particular, a pronounced polarization of the upconversion luminescence from a single particle was observed. Luminescence dependences on the laser power are quite different for the single particle and the large ensemble of nanoparticles as well. These facts attest to the notion that upconversion properties of single particles are highly individual. This implies that to use an upconversion particle as a single sensor of the local parameters of a medium, the additional studying and calibration of its individual photophysical properties are essential.

Multi-sensor InSAR time series fusion for long-term land subsidence monitoring

ABSTRACT Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors. This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets, to address cases when partial overlaps and/or temporal gaps exist. A new Power Exponential Knothe Model (PEKM) fits and fuses overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses any temporal gaps in the series. Taking the city of Wuhan (China) as experiment area, COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019) and Sentinel-1 (2019–2021) SAR datasets were fused to map long-term surface deformation over the last decade. An independent 2011–2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison. The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The correlation coefficient of the overall results is 0.78, which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve. The experimental results are consistent with existing studies of surface deformation at Wuhan, demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.

Micropyramidal Flexible Ion Gel Sensor for Multianalyte Discrimination and Strain Compensation.

A highly sensitive and flexible gas sensor that can detect a wide range of chemicals is crucial for wearable applications. However, conventional single resistance-based flexible sensors face challenges in maintaining chemical sensitivity under mechanical stress and can be affected by interfering gases. This study presents a versatile approach for fabricating a micropyramidal flexible ion gel sensor, which accomplishes sub-ppm sensitivity (<80 ppb) at room temperature and discrimination capability between various analytes, including toluene, isobutylene, ammonia, ethanol, and humidity. The discrimination accuracy of our flexible sensor is as high as 95.86%, enhanced by using machine learning-based algorithms. Moreover, its sensing capability remains stable with only a 2.09% change from the flat state to a 6.5 mm bending radius, further amplifying its universal usage for wearable chemical sensing. Therefore, we envision that a micropyramidal flexible ion gel sensor platform assisted by machine learning-based algorithms will provide a new strategy toward next-generation wearable sensing technology.

FusionPID: A PID control system for the fusion of infrared and visible light images

The purpose of infrared and visible image fusion is to compensate for the incomplete imaging of a single sensor. Although there are existing fusion algorithms, they tend to overlook the guidance that infrared and visible light images can provide in formulating fusion strategies. Consequently, the resulting fused image lacks a genuine reference, and the designed fusion strategy lacks adaptive ability. To overcome this challenge, this study proposes a novel fusion framework based on cybernetics, referred to as FusionPID, that utilizes a proportional integral differential (PID) control system to fuse infrared and visible images. The framework first extracts source map features through an improved Mean Shift feature clustering algorithm in the field of machine vision. This information is then used to construct the transfer function of the control system, enhancing the potential of the fused images for downstream target detection tasks. Secondly, a comprehensive measurement function is designed to judge the difference between the fused image and the source image. The target of the fused process is guided by the source image through the measurement value and the feedback function of the control system. Finally, a PID controller is designed, which can adjust the output adaptively according to the difference between the source image and the fusion result. FusionPID can rely on the feedback ability of the control system for different fusion tasks, so that the fusion image can maintain both the thermal radiation in the infrared image and the texture in the visible image. Experiments show that our FusionPID has advantages over the most advanced methods in maintaining significant contrast and rich texture. In addition, the fused image generated by this method can be applied to downstream target detection tasks to improve the detection performance.

Differential design in homogenous sensors for classification and decoupling kinesthetic information through machine learning

To accurately reflect the movement of humans or robots, multi-sensor integration strategy is necessary to decouple complex deformations caused by motion in the wearable artificial kinesthetic perception system. The multi-sensor integration strategy could collect multi-dimension information, making up for the deficiency of robustness and accuracy of single sensor in complex motion scenes and extending the sensing capability of the system. However, the inevitable inconsistency between multiple sensors causes difficulties in fabrication, integration, and perception, limiting the development of artificial kinesthetic perception systems toward the direction of compact integration, large-area sensing, and high-resolution decoupling. Here, we propose a strategy to build an artificial kinesthetic perception system based on the differential design of homogeneous sensors. The strategy aims at guiding system design to avoid the inconsistency in multi-sensor integration by revealing the explicit relationship among structure, signal, and movements from the perspective of the physics model. According to the strategy, we presented a simple fabrication method of the kinesthetic perception prototype. The prototype with two homogenous sensors (0.019 56 residual on average) shows three differential signal modes to three deformations. With the help of machine learning, it realized the decoupling task to 25 kinds of complex deformations. The accuracy remains at 95% even though the decoupling resolution is up to 0.2 mm. With more than one prototype arrayed, complex deformation composed with more kinds of basic deformation (shear and twist) could be further decoupled. We believe that the strategy described in this paper will contribute to the development of a compact and programmable kinesthetic perception system.

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

Traditional Japanese orchards control the growth height of fruit trees for the convenience of farmers, which is unfavorable to the operation of medium- and large-sized machinery. A compact, safe, and stable spraying system could offer a solution for orchard automation. Due to the complex orchard environment, the dense tree canopy not only obstructs the GNSS signal but also has effects due to low light, which may impact the recognition of objects by ordinary RGB cameras. To overcome these disadvantages, this study selected LiDAR as a single sensor to achieve a prototype robot navigation system. In this study, density-based spatial clustering of applications with noise (DBSCAN) and K-means and random sample consensus (RANSAC) machine learning algorithms were used to plan the robot navigation path in a facilitated artificial-tree-based orchard system. Pure pursuit tracking and an incremental proportional–integral–derivative (PID) strategy were used to calculate the vehicle steering angle. In field tests on a concrete road, grass field, and a facilitated artificial-tree-based orchard, as indicated by the test data results for several formations of left turns and right turns separately, the position root mean square error (RMSE) of this vehicle was as follows: on the concrete road, the right turn was 12.0 cm and the left turn was 11.6 cm, on grass, the right turn was 12.6 cm and the left turn was 15.5 cm, and in the facilitated artificial-tree-based orchard, the right turn was 13.8 cm and the left turn was 11.4 cm. The vehicle was able to calculate the path in real time based on the position of the objects, operate safely, and complete the task of pesticide spraying.

Using transient energy release measurements for the in‐line characterization of non‐Newtonian fluids and fluid state in pipe flow

AbstractAlberini et al. have developed a new technology based on a passive acoustic emission (AE) sensing system that uses only a single sensor, with the goal of providing live and in‐situ measurement of rheology. For this study, three different types of fluids were selected to represent common rheological behaviours: Newtonian behaviour, non‐Newtonian behaviour with power law, and non‐Newtonian behaviour with Herschel–Bulkley relationship. By analyzing the transient energy released during the interaction between the probe and the fluid, distinct acoustic fingerprints were identified in the frequency domain. These acoustic fingerprints were found to be characteristic of the different fluids and their rheology, and were validated in triplicate. Furthermore, the results showed that the intensity of the acoustic emissions increased with higher flow rates (30 to 50 L/min). To test the correlation between flow rate and acoustic response, a neural network regression test was conducted, which demonstrated a direct correlation between AE peaks and flow rate. The neural network used was nonlinear autoregressive network with exogenous inputs (NARX), and the test involved a stepwise regression with 70% training and 30% network validation. The study also introduced the Rheology‐AE quotient, which maps fluid constituents against the acoustic signal. Results showed that this was a reliable means of deriving live rheology from a fluid's frequency domain. Finally, the results obtained from this study were validated using an offline rotational rheometer.

Development of a Gait Feature-Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor.

Gait changes are potential markers of cognitive disorders (CDs). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertial sensor and compared its diagnostic performance for CD with that of the model using the Mini-Mental State Examination (MMSE). We enrolled community-dwelling older adults with normal gait from the Korean Longitudinal Study on Cognitive Aging and Dementia and measured their gait features using a wearable inertial sensor placed at the center of body mass while they walked on a 14-m long walkway thrice at comfortable paces. We randomly split our entire dataset into the development (80%) and validation (20%) datasets. We developed a model for classifying CD using logistic regression analysis from the development dataset and validated it in the validation dataset. In both datasets, we compared the diagnostic performance of the model with that using the MMSE. We estimated optimal cutoff score of our model using receiver operator characteristic analysis. In total, 595 participants were enrolled, of which 101 of them experienced CD. Our model included both gait speed and temporal gait variability and exhibited good diagnostic performance for classifying CD from normal cognition in both the development (area under the receiver operator characteristic curve [AUC] = 0.788, 95% CI 0.748-0.823, p < 0.001) and validation datasets (AUC = 0.811, 95% CI 0.729-0.877, p < 0.001). Our model showed comparable diagnostic performance for CD with that of the model using the MMSE in both the development (difference in AUC = 0.026, standard error [SE] = 0.043, z statistic = 0.610, p = 0.542) and validation datasets (difference in AUC = 0.070, SE = 0.073, z statistic = 0.956, p = 0.330). The optimal cutoff score of the gait-based model was >-1.56. Our gait-based model using a wearable inertial sensor may be a promising diagnostic marker of CD in older adults. This study provides Class III evidence that gait analysis can accurately distinguish older adults with CDs from healthy controls.

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB.

With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between the filter-output quantities, owing to the use of the same state equation in each of the local sensors, a new kinematic and static multi-source fusion-filtering algorithm based on the error-state Kalman filter (ESKF) is proposed in this paper for the positioning-state estimation of UGVs. The algorithm is based on INS/GNSS/UWB multi-source sensors, and the ESKF replaces the traditional Kalman filter in kinematic and static filtering. After constructing the kinematic EKSF based on GNSS/INS and the static ESKF based on UWB/INS, the error-state vector solved by the kinematic ESKF was injected and set to zero. On this basis, the kinematic ESKF filter solution was used as the state vector of the static ESKF for the rest of the static filtering in a sequential form. Finally, the last static ESKF filtering solution was used as the integral filtering solution. Through mathematical simulations and comparative experiments, it is demonstrated that the proposed method converges quickly, and the positioning accuracy of the method was improved by 21.98% and 13.03% compared to the loosely coupled GNSS/INS and the loosely coupled UWB/INS navigation methods, respectively. Furthermore, as shown by the error-variation curves, the main performance of the proposed fusion-filtering method was largely influenced by the accuracy and robustness of the sensors in the kinematic ESKF. Furthermore, the algorithm proposed in this paper demonstrated good generalizability, plug-and-play, and robustness through comparative analysis experiments.

Spatio-Temporal Patterns of NDVI and Its Influencing Factors Based on the ESTARFM in the Loess Plateau of China

The Loess Plateau is ecologically vulnerable. Vegetation is the key factor in ecological improvement. The study of the distribution patterns of vegetation and its impact factors has important guiding meaning for ecological construction in the region. The existing single sensor cannot provide long-term and high-resolution data. We established data of NDVI with a great spatial resolution by fusing the GIMMS NDVI and the MODIS NDVI based on the ESTARFM. Furthermore, we analyzed the variation in NDVI under different topographies and its response to climatic factors and human activities in the Loess Plateau. The results manifested that: (1) The fused NDVI by the ESTARFM had a high correlation with the MODIS NDVI and can be used in subsequent studies. (2) The multi-year average NDVI of this region ranged from 0.027 to 0.973, which is specifically low in the northwest and high southeast. The NDVI manifested an upward trend in the last 31 years. Its growth rate was 0.0036/a (p &lt; 0.01). Spatially, the area with an upward trend of NDVI accounted for 89.48% of the plateau. (3) For topography, the larger area with the extremely significant upward of NDVI was found at elevations of 500–1500 m, with slopes of 6–15°. The larger area with the extremely significant downward trend of NDVI was found at an elevation of higher than 3000 m, with a slope of greater than 35°. (4) The response of the NDVI to the climatic factors manifested a significant spatial heterogeneity. The temperature had a more significant impact on NDVI than precipitation. (5) Human activities contributed more to NDVI than climatic factors (65.22% for human activities and 34.78% for climatic factors). Among them, the area with a high contribution of human activities to NDVI increase was consistent with the area where the GGP was implemented. The distribution of areas with high contribution of human activities to NDVI decrease was in line with that of the provincial capital cities. The results served as the theoretical foundation for assessing the efficacy of environmental stewardship and for optimizing ecological restoration measures.

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion.

For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species. This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR). Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials. For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model. Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods. This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

Correlation between Sensing Accuracy and Read Margin of a Memristor-Based NO Gas Sensor Array Estimated by Neural Network Analysis.

Memristor-based gas sensors (gasistors) have been considered as the most promising candidate for detecting NO gas suitable for neural network (NN) analysis. In this work, in order to solve an overfitting issue arising from the training data when using a single gasistor, which degrades the accuracy of NN, we here propose a metal-insulator-silicon (MIS)-structured Zr3N4-based gasistor array that results in an improvement in both the accuracy of the NN analysis and the efficiency of the operating power. As a result, the proposed gasistor array showed a decrease of epoch and a 2.5% improvement of prediction accuracy at room temperature compared to single cells with metal/insulator/metal (MIM) and MIS structures. These results imply that an array structure based on MIS can efficiently solve the overfitting issue by receiving multiple responses at once, compared to a single gas sensor that obtains one response per sensing.

Building performance simulations can inform IoT privacy leaks in buildings

As IoT devices become cheaper, smaller, and more ubiquitously deployed, they can reveal more information than their intended design and threaten user privacy. Indoor Environmental Quality (IEQ) sensors previously installed for energy savings and indoor health monitoring have emerged as an avenue to infer sensitive occupant information. For example, light sensors are a known conduit for inspecting room occupancy status with motion-sensitive lights. Light signals can also infer sensitive data such as occupant identity and digital screen information. To limit sensor overreach, we explore the selection of sensor placements as a methodology. Specifically, in this proof-of-concept exploration, we demonstrate the potential of physics-based simulation models to quantify the minimal number of positions necessary to capture sensitive inferences. We show how a single well-placed sensor can be sufficient in specific building contexts to holistically capture its environmental states and how additional well-placed sensors can contribute to more granular inferences. We contribute a device-agnostic and building-adaptive workflow to respectfully capture inferable occupant activity and elaborate on the implications of incorporating building simulations into sensing schemes in the real world.

110 Correlation of Measured Gas Concentrations in Four-Gas Alarm Equipment Data in Wastewater Related Work

Abstract Introduction Trondheim municipality uses four-gas alarm equipment as part of their personal protective equipment (PPE) in wastewater related work. Approximately 65 sensors are personal, and 60 of them are included in the study. 15 second recordings and alarm data for hydrogen sulphide (H2S), oxygen (O2), lower explosive limit (LEL) and carbon monoxide (CO) from more than 7000 workdays are presented. We have mainly studied H2S, but here we present data and correlation from all four sensors in the equipment. The sensor setup has been chosen to cover the most common gas exposure of the occupation: H2S and LEL (calibrated as methane, CH4) caused by biological degradation, CO from machinery and O2 deprivation, mainly thought to be caused by use (corrosion or burning) and/ or by displacement by other gases. Many workers still think use of a single sensor gas detector is enough for protection against all gases. Method An algorithm that makes this calculation from measurement data is published. The algorithm was adapted to data export format from logging gas alarm equipment MicroClipX3 from Honeywell Analytics. Data from more than 7000 workdays in sewage related work was collected. Results There were no obvious correlations between the measured gas concentrations. Conclusions All four gases are relevant to measure in wastewater related work. None of them can substitute each other.

Fixed-Point Landing Method for Unmanned Aerial Vehicles Using Multi-Sensor Pattern Detection

Conventional autonomous landing systems for unmanned aerial vehicles (UAVs) mainly rely on global positioning systems (GPS) or a single visual sensor, which identifies specific markers and uses them to guide the UAV landing. However, such methods have low positioning accuracies and weak range recognition abilities, which limit their applicability. This report proposes a fixed-point landing method combining light detection and ranging (LIDAR) technology with a vision camera; the developed approach can be used to locate the landing area and enable a completely autonomous landing, without being affected by distance, weather, or time. First, we establish the mathematical relationship between the color of the mark and the reflection intensity of the laser point cloud. According to this relationship, UAVs can confirm their position relative to the landing point starting from a greater distance without being disturbed by the environmental light intensity. Meanwhile, the method uses the camera to detect the corners of the landing target, even obtaining the relative position at a close distance. Finally, the LIDAR and camera data are combined to detect the corner points of the markers, determine the position of the UAV relative to the center of the landing target in real time, and guide the landing.

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application.

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.

Respiratory Rate Extraction from Neonatal Near-Infrared Spectroscopy Signals.

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain 'noise' from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory rate from cerebral NIRS intensity signals in neonates admitted to a neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR from NIRS signals recorded from critically ill neonates. In total, 19 measurements were recorded from ten neonates admitted to the NICU with a gestational age and birth weight of 38 ± 5 weeks and 3092 ± 990 g, respectively. We synchronously recorded NIRS and reference RR signals sampled at 100 Hz and 0.5 Hz, respectively. The performance of the NRR algorithm is assessed in terms of the agreement and linear correlation between the reference and extracted RRs, and it is compared statistically with that of two existing methods. Results: The NRR algorithm showed a mean error of 1.1 breaths per minute (BPM), a root mean square error of 3.8 BPM, and Bland-Altman limits of agreement of 6.7 BPM averaged over all measurements. In addition, a linear correlation of 84.5% (p < 0.01) was achieved between the reference and extracted RRs. The statistical analyses confirmed the significant (p < 0.05) outperformance of the NRR algorithm with respect to the existing methods. Conclusions: We showed the possibility of extracting RR from neonatal NIRS in an intensive care environment, which showed high correspondence with the reference RR recorded. Adding the NRR algorithm to a NIRS system provides the opportunity to record synchronously different physiological sources of information about cerebral perfusion and respiration by a single monitoring system. This allows for a concurrent integrated analysis of the impact of breathing (including apnea) on cerebral hemodynamics.