Sensor Signal Processing Research Articles

Low noise signal processing for electrochemical biosensor using narrow bandwidth filter

Abstract Biosensor has an important role in several domains, including medical and environmental applications. The improvement of biosensor measurement system therefore offers a better measurement quality. The objective of this work is the development of the signal processing of electrochemical biosensor for advanced analysis and use. The noise of electrochemical sensor is considered rather higher than the other sensor due to many factors such as electrode area, solution velocity, and particle size in solution, which affect adsorption-desorption process. Moreover, the sensor signal processing also makes contribution to noise level. Therefore, it must be reduced and suppressed to obtain better measurement result. In this paper, a bandwidth optimisation method is proposed and used to improve measurement quality. The system is realized with a transimpedance circuit and low-pass and high-pass filter, so that it offers low noise characteristics. The developed proposed system has provided better improvement than the standard electrochemical biosensor system. The noise of the system can be suppressed to be lower up to 14.3% of the standard transimpedance circuit. This development is promising to give better biosensor measurement.

Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension.

Blood pressure (BP) is a key contributor to the lifetime risk of preclinical organ damage and cardiovascular disease. Traditional clinic-based BP readings are typically measured infrequently and under standardized/resting conditions and therefore do not capture BP values during normal everyday activity. Therefore, current hypertension guidelines emphasize the importance of incorporating out-of-office BP measurement into strategies for hypertension diagnosis and management. However, conventional home and ambulatory BP monitoring devices use the upper-arm cuff oscillometric method and only provide intermittent BP readings under static conditions or in a limited number of situations. New innovations include technologies for BP estimation based on processing of sensor signals supported by artificial intelligence tools, technologies for remote monitoring, reporting and storage of BP data, and technologies for BP data interpretation and patient interaction designed to improve hypertension management ("digital therapeutics"). The number and volume of data relating to new devices/technologies is increasing rapidly and will continue to grow. This International Society of Hypertension position paper describes the new devices/technologies, presents evidence relating to new BP measurement techniques and related indices, highlights standard for the validation of new devices/technologies, discusses the reliability and utility of novel BP monitoring devices, the association of these metrics with clinical outcomes, and the use of digital therapeutics. It also highlights the challenges and evidence gaps that need to be overcome before these new technologies can be considered as a user-friendly and accurate source of novel BP data to inform clinical hypertension management strategies.

Triboelectric Nanogenerators with Machine Learning for Internet of Things

AbstractThe development of the Internet of Things (IoT) indicates that humankind has entered a new intelligent era of the “Internet of Everything”. Thanks to the characteristics of low‐cost, diverse structure, and high energy conversion efficiency, the self‐powered sensing systems, which are based on the Triboelectric Nanogenerator (TENG), demonstrate great potential in the field of IoT. In order to solve the challenges of TENG in sensing signal processing, such as signal noise and nonlinear relations, Machine Learning (ML), which is an efficient and mature data processing tool, is widely applied for efficiently processing the large and complex output signal data generated by TENG intelligent sensing system. This review summarizes and analyzes the adaptation of different algorithms in TENG and their advantages and disadvantages at the beginning, which provides a reference for the selection of algorithms for TENG. More importantly, the application of TENG is introduced in multiple scenarios, including health monitoring, fault detection, and human‐computer interaction. Finally, the limitations and development trend of the integration of TENG and ML are proposed by classification to promote the future development of the intelligent IoT era.

Pseudo-ISP: Learning pseudo in-camera signal processing pipeline from a color image denoiser

Existing deep denoisers usually ignore the problem of noise discrepancy between training and test images caused by the different noise modeling of sensor and in-camera signal processing (ISP) pipeline, which inevitably degrades the denoising performance. In this paper, we present an unpaired learning scheme to adapt the color image denoisers for handling test images with noise discrepancy. To this end, we consider a practical training setting equipped with a pre-trained color denoiser, a set of test noisy sRGB images, and an unpaired set of clean sRGB images. Then, we propose to alternate between two modules, i.e., Pseudo-ISP training for learning noise modeling of sensor and in-camera signal processing (ISP) pipeline and denoiser adaption. Instead of modeling the complex noise in sRGB images, we assume the signal-dependent and spatially independent noise in rawRGB space, and specifically design the Pseudo-ISP to learn the pseudo ISP pipeline and pseudo rawRGB noise model jointly. Applying the learned Pseudo-ISP to the unpaired set of clean sRGB images, the corresponding realistic noisy sRGB observations can be easily obtained. Conversely, using the generated paired data for model fine-tuning, the color denoiser can be well adapted for test images. Through iterating between Pseudo-ISP training and denoiser adaption, denoising performance of deep denoisers can be further improved. Experiments show that our Pseudo-ISP not only can boost simple Gaussian blurring-based denoiser to achieve competitive performance against CBDNet, but also is effective in improving state-of-the-art deep denoisers, e.g., CBDNet and RIDNet.

Robustness of Structural Health Monitoring Systems for Offshore Energy Structures based on Damage Characteristics

Offshore energy structures such as fixed/floating wind turbines, tidal turbines and wave energy converters are exposed to loads coming from different sources. Recent advances in SHM offer many unique opportunities to assess the structural integrity of offshore energy infrastructure. The process of SHM generally involves the use of a system of sensors mounted on a structure; the processing of signals received from the sensors to attain damage sensitive features; and the detection of damage in the structure. Over the past decades, most of the research in SHM has been focused on improving and developing new sensor technology, signal processing, and damage detection algorithms. However, there are very few studies assessing the performance of SHM systems with respect to damage detection, localization, and quantification. Moreover, most of the performance analysis models for SHM systems rely only on 'probability of detection (POD)', that is an assessment index used in NDT to evaluate the probability of detecting a damage as a function of its size. For SHM systems, the replicability of POD is influenced by the environment, measurement noise, and the deterioration of sensors and instruments. Therefore, the distribution of POD is often determined by performing an extensive number of test sets with existing damage of varying magnitude. This paper aims to reduce the volume of experimental data required, as well as the uncertainties associated with POD prediction by using a model-assisted probability of detection (MAPOD) approach. We develop a Bayesian network (BN) method to evaluate the performance of SHM systems in terms of damage detection, localization, and assessment. In addition to POD, the probability of accurate localization (POAL) and probability of accurate assessment (POAA) are also incorporated into the analysis. The BN and limit state functions are applied to a fixed offshore energy structure equipped with a vibration based SHM system.

A review of SLAM techniques and applications in unmanned aerial vehicles

Abstract Simultaneous Localisation and Mapping (SLAM) is a foundational idea in the field of robotics. It involves the processing of sensor signals and the optimisation of pose-graphs. SLAM has found several applications in various domains, including but not limited to courier services, agriculture, environmental monitoring, and military operations, particularly with the use of Unmanned Aerial Vehicles (UAVs). There exist several applications. This work aims to provide a comprehensive analysis of three Simultaneous Localization and Mapping (SLAM) algorithms, namely CNN-SLAM, Linearized Kalman Filter (LKF), and Extended Kalman Filter (EKF). Additionally, it will explore the utilisation of SLAM algorithms in Unmanned Aerial Vehicles (UAVs) by examining its use in precision agriculture, geological surveys, and Emergency Scenarios. This section will outline certain issues that SLAM algorithms may encounter in relation to wide area applications, real-time processing and efficiency, robustness, and dynamic objects within the environment. Ultimately, this study will undertake a comparative analysis of the merits and drawbacks associated with the three algorithms, while also putting up potential remedies for the aforementioned issues.

Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction

Abstract Accurately predicting the remaining useful life of aerospace engines is crucial for enhancing the reliability of aviation equipment. While some methods have taken note of the challenges posed by vast sensor data and complex signal interrelationships, there is still room for improvement in performance. This paper proposes a novel deep learning model that utilizes a parallel structure to independently process inputs from various sensor signals. Each branch in this parallel structure employs a combination of an improved Inception module and a novel feature filtering module as a feature extractor. The improved Inception module boasts a larger perceptual field to ensure the integrity of feature information. The feature filtering module calculates the importance weights of feature information through convenient computation, allowing the network to focus more on feature information without significantly increasing computational complexity. Finally, the feature extractor is combined with a gated recurrent unit module to learn features from sensor signals. Extensive experiments were conducted on the C-MAPSS standard dataset, comparing the proposed method with other state-of-the-art methods. Ablation experiments were performed on the new generation N-CMAPSS standard dataset. The results of the experiments confirm the superiority and rationality of the proposed prediction method.

Advancements in Filter Technology: Evolution and Applications

This paper explores the historical trajectory and contemporary applications of signal processing filters. From the early days of analog filters to the current digital era, the paper traces their evolution, emphasizing key developments in active RC filters, monolithic integrated op amps, and the transition to switched-capacitor and continuous-time filters. Fundamentals of analog and digital filters are outlined, distinguishing their processing of continuous-time and discrete-time signals. The classification of filters, including low-pass, high-pass, band-pass, and band-stop, is discussed with insights into their specific applications. The paper delves into crucial applications in audio processing, video processing, communication systems, power management, and sensor signal processing, showcasing filters' pivotal role in enhancing signal quality across diverse fields. Addressing emerging technologies, the paper highlights adaptive and deep learning filters, projecting the future trends in filter technology. It anticipates developments in analog integrated filters, emphasizing the growing significance of integrated circuits technology. This comprehensive review aims to deepen readers' understanding and inspire continued innovation in signal processing.

Data-Driven Prediction Model for Analysis of Sensor Data

In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used extensively in Industry 4.0 to process sensor signals and improve the accuracy of predictions. Therefore, this paper proposes and validates the data-driven prediction model to analyze sensor data, including in the data transformation phase Principal Component Analysis tested by Fourier Transformation and Wavelet Transformation, and the modeling phase based on machine and deep learning algorithms. The machine learning algorithms used for tests in this research are Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Decision Tree Regression (DTR). For the deep learning comparison, the algorithms are Deep Learning Regression and Convolutional network with LeNet-5 Architecture. The experimental results indicate that the models show promising results in predicting wear values and open the problem to further research, reaching peak values of 92.3% accuracy for the first dataset and 62.4% accuracy for the second dataset.

PSII-10 Development of an interdisciplinary undergraduate and graduate course in precision livestock farming systems

Abstract The modern livestock industry is increasingly adopting data, technologies, and automation into daily husbandry and management routines and decisions. These advances in precision livestock farming (PLF) enable improved financial margins, sustainability, efficiency, and animal productivity and welfare. However, there is currently a gap between the engineers and data scientists designing the technologies and the animal scientists and caretakers using them. At North Carolina State University a novel course on PLF Systems for undergraduate and graduate students was developed in the Department of Animal Science. Through lecture delivery, livestock production scenarios, scientific literature, and data-based homework assignments students explored data collection, management, analysis, and data-based decision making. Students gained hands-on experience with a variety of sensor types and signal processing through lab-based activities. Basic hardware and software development concepts were explored during an Arduino-based microcontroller activity. The course was developed and refined using student feedback to include additional sensor and lab-based days and additional in-class supported activities utilizing Excel for data manipulation, processing, and presentation. Self-reported student learning results were collected across two semesters (19 students; 18 Animal Science, 1 Biological Engineering) with84% of students indicated the course was effective in increasing their ability to evaluate and implement technology in livestock production systems and 74% of students reported increased knowledge of coding and software, as well as their ability to clean, analyze, and make sense of raw data. The topics and delivery methods implemented in this course could inform the basis for development of similar interdisciplinary PLF courses at other academic institutions.

SQUID magnetoneurography: an old-fashioned yet new tool for noninvasive functional imaging of spinal cords and peripheral nerves.

We are engaged in the development and clinical application of a neural magnetic field measurement system that utilizes biomagnetic measurements to observe the activity of the spinal cord and peripheral nerves. Unlike conventional surface potential measurements, biomagnetic measurements are not affected by the conductivity distribution within the body, making them less influenced by the anatomical structure of body tissues. Consequently, functional testing using biomagnetic measurements can achieve higher spatial resolution compared to surface potential measurements. The neural magnetic field measurement, referred to as magnetoneurography, takes advantage of these benefits to enable functional testing of the spinal cord and peripheral nerves, while maintaining high spatial resolution and noninvasiveness. Our magnetoneurograph system is based on superconducting quantum interference devices (SQUIDs) similar to the conventional biomagnetic measurement systems. Various design considerations have been incorporated into the SQUID sensor array structure and signal processing software to make it suitable for detecting neural signal propagation along spinal cord and peripheral nerve. The technical validation of this system began in 1999 with a 3-channel SQUID system. Over the course of more than 20 years, we have continued technological development through medical-engineering collaboration, and in the latest prototype released in 2020, neural function imaging of the spinal cord and peripheral nerves, which could also be applied for the diagnosis of neurological disorders, has become possible. This paper provides an overview of the technical aspects of the magnetoneurograph system, covering the measurement hardware and software perspectives for providing diagnostic information, and its applications. Additionally, we discuss the integration with a helium recondensing system, which is a key factor in reducing running costs and achieving practicality in hospitals.

A novel dust collection system fault diagnosis based on the noise emission of solenoid pulse valve

The solenoid pulse valve (SPV) is a key component of dust collector. The frequent switch of SPV accelerates its damage, subsequently decrease operation efficiency of dust collector. The traditional manual inspection methods lack reliability and efficiency, while vibration-based condition monitoring is costly and sophisticated in acceleration sensor distribution and signal processing. This paper proposes a fault diagnosis approach based on SPV noise. The average coherence coefficient of SPV noise and vibration signals is above 0.8 and close to 1, which demonstrates that there is a close relationship between SPV noise and its structural vibration. Furthermore, the validation experiments indicate that the SPV fault can be clearly distinguished by its noise signal, confirms the effectiveness of proposed approach. By utilizing this noise monitoring method, the number of sensors can be reduced by about 95 %, the work intensity of the staff is greatly reduced, and production efficiency and safety are guaranteed.

Detection and Recognition of Voice Commands by a Distributed Acoustic Sensor Based on Phase-Sensitive OTDR in the Smart Home Concept.

In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results. We also proposed a new setup model for lab tests of φ-OTDR single coordinate sensors, which allows for the quick variation of their parameters. As a result, it was possible to define requirements for the best quality of speech recognition; estimation using the percentage of recognized words yielded a value of 96.3%, and estimation with Levenshtein distance provided a value of 15.

Design recommendations for voluntary blink interactions based on pressure sensors

Voluntary blink control is vital in eye-controlled human–computer interaction (ECHCI). Pressure sensors, known for their low power consumption and strong resistance to interference, are suitable for blink detection. Existing research has predominantly focused on sensor materials and signal processing, and studies on the characteristics and optimized design of voluntary blink actions in ECHCI remain relatively limited. Therefore, this study presents a blink detection system based on pressure sensors and investigates the efficiency of voluntary blink actions. We examined the effects of two independent variables, blink count (single, double, and triple) and blink side (bilateral and unilateral), on recognition accuracy and three time-related variables. Subsequently, the subjective workload and evaluation experiments were conducted for each blink action. The results indicate that the blink count has a more pronounced impact on interaction efficiency and comfort, with continuous blink actions exceeding three blinks not being recommended. Bilateral blinks were perceived as more natural and comfortable than unilateral blinks. Moreover, we discovered that the participants exhibited greater attentiveness and focus when performing double bilateral blink actions, making it suitable for important commands. Based on the experimental results, the top three recommended voluntary blink actions are single bilateral blink (SB), double bilateral blink (DB), and single unilateral blink (SU). Finally, a test involving controlling the movement of a toy car using the recommended blink actions validates the usability of the actions and detection system. These findings provide valuable guidance for the design and optimization of voluntary-blink interaction systems, further enhancing this interaction method’s feasibility.

Exhaled Breath Analysis Detects the Clearance of Staphylococcus aureus from the Airways of Children with Cystic Fibrosis.

Electronic nose (eNose) technology can be used to characterize volatile organic compound (VOC) mixes in breath. While previous reports have shown that eNose can detect lung infections with pathogens such as Staphylococcus aureus (SA) in people with cystic fibrosis (CF), the clinical utility of eNose for longitudinally monitoring SA infection status is unknown. In this longitudinal study, a cloud-connected eNose, the SpiroNose, was used for the breath profile analysis of children with CF at two stable visits and compared based on changes in SA infection status between visits. Data analysis involved advanced sensor signal processing, ambient correction, and statistics based on the comparison of breath profiles between baseline and follow-up visits. Seventy-two children with CF, with a mean (IQR) age of 13.8 (9.8-16.4) years, were studied. In those with SA-positive airway cultures at baseline but SA-negative cultures at follow-up (n = 19), significant signal differences were detected between Baseline and Follow-up at three distinct eNose sensors, i.e., S4 (p = 0.047), S6 (p = 0.014), and S7 (p = 0.014). Sensor signal changes with the clearance of SA from airways were unrelated to antibiotic treatment. No changes in sensor signals were seen in patients with unchanged infection status between visits. Our results demonstrate the potential applicability of the eNose as a non-invasive clinical tool to longitudinally monitor pulmonary SA infection status in children with CF.

Smart house system for safety of elderly living alone based on camera and PIR sensor

With the improvement of human life quality, life expectancy generally increases. As a result, more and more elderly people living alone appear. Recently, the safety problems of the elderly living alone have attracted more and more attention from the public. Due to living alone, the elderly cannot be found at the first time when an accident occurs indoors or out, and the rescue time is delayed. This article proposes a way to use the speed up module to realize real-time face detection on the Raspberry Pi and optimize the processing of PIR sensor signals and write a logic system based on the camera and PIR signals to record and analyze the life of the elderly living alone and warning system to their family members.

Enabling Product Carbon Footprint Management in the Material Extrusion Process

The environmental impact of new products and production processes is one of the most important indicators of the current time. Many methods exist to estimate ecological metrics, such as the Product Carbon Footprint (PCF) in the individual product development phases with stand-alone software. However, methods to integrate the PCF in the entire process chain with the possibility to interact in the process are missing. This paper presents a new approach to measuring the ecological impact of an Additive Manufacturing production process, exemplified by the Material Extrusion (MEX) process. The novelty of the approach lies in implementing a single software for machine control, processing, and visualization of sensor signals, parameter variation, and reporting functions to enable comprehensive PCF management. The workflow includes a model-based prediction of the PCF based on the G-code, live monitoring during the production phase, and a subsequent automated evaluation at a central interface. Critical points can be identified, and approaches for optimizing the MEX process can be designed. Uncertainty evaluation of the PCF based on the combination of uncertainty of the sensors and the Life Cycle Assessment is included in the method. The developed prototype software can be used in the design phase for prediction and in the process phase for optimization, enabling seamless PCF management for the MEX process.

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence.

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

An Infrared Near-Sensor Reservoir Computing System Based on Large-Dynamic-Space Memristor with Tens of Thousands of States for Dynamic Gesture Perception.

To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near-senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single-crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable-range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single-crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor-based near-sensor computing system.

Design of pressure signal acquisition system based on STM32

In order to realize the function of an automatic target-scoring system for shock wave pressure signal positioning, it is necessary to collect multi-channel shock wave pressure signals. Therefore, this paper designs a sensor signal acquisition and processing system based on STM32. The system can realize the acquisition and processing of 8-channel sensor signals. The hardware module includes the operational amplifier module, the acquisition conversion module, the temperature acquisition module, and the wireless transmission module. In order to solve the positioning coordinates, the program is written based on Keil uVision5 and STM32 firmware library. The PYQT5 is called to design the host computer receiving software. Finally, the acquisition test of the designed acquisition system is carried out. The test results show that the signal reduction degree of the designed acquisition system is more than 90%. The designed signal acquisition system has the advantages of high reliability and small size. It also has a certain universality.