Integration Of Multiple Sensors Research Articles

Improving SLAM Techniques with Integrated Multi-Sensor Fusion for 3D Reconstruction.

Simultaneous Localization and Mapping (SLAM) poses distinct challenges, especially in settings with variable elements, which demand the integration of multiple sensors to ensure robustness. This study addresses these issues by integrating advanced technologies like LiDAR-inertial odometry (LIO), visual-inertial odometry (VIO), and sophisticated Inertial Measurement Unit (IMU) preintegration methods. These integrations enhance the robustness and reliability of the SLAM process for precise mapping of complex environments. Additionally, incorporating an object-detection network aids in identifying and excluding transient objects such as pedestrians and vehicles, essential for maintaining the integrity and accuracy of environmental mapping. The object-detection network features a lightweight design and swift performance, enabling real-time analysis without significant resource utilization. Our approach focuses on harmoniously blending these techniques to yield superior mapping outcomes in complex scenarios. The effectiveness of our proposed methods is substantiated through experimental evaluation, demonstrating their capability to produce more reliable and precise maps in environments with variable elements. The results indicate improvements in autonomous navigation and mapping, providing a practical solution for SLAM in challenging and dynamic settings.

Integration of Multiple Sensors into an ROV for Remote Measurement in the Fukushima Daiichi Nuclear Power Station

To measure and analyze the distribution and criticality of submerged fuel debris “in situ” in the decommissioning process of the Fukushima Daiichi Nuclear Power Station (1F), we developed a system for remote measurement using multiple sensors consisting of the neutron sensor, the sonar, and the sub-bottom profiler (SBP) mounted on a small remotely operated underwater vehicle (ROV). First, the locations presumed to contain fuel debris were explored based on the information of the water bottom shape obtained using the sonar and the bottom surface substructure obtained using the SBP. Second, the system identified the location of fuel debris by detecting neutrons emitted by spontaneous fissions of fuel debris using the neutron sensor with diamond elements. Simulated fuel debris was placed at the bottom of a test water tank that simulated the pedestal. In the primary containment vessel (PCV) of 1F, and a verification test of remote measurement was conducted using the ROV mounted on the multiple sensors. The desired results were obtained.

Weighted Multiple-sensor Combining Algorithm for Long-range Underwater Acoustic Communications and Experimental Results

In very long-range underwater acoustic communications, enhancing communication performance often involves augmenting the received signal-to-noise ratio (SNR) through the integration of multiple receiver sensors. However, in cases where sensors are linearly combined, those with inferior performance can significantly impact overall communication efficacy. Consequently, when employing linear combinations, it becomes essential to assign lower weighting factors to sensors exhibiting higher error rates, thereby enhancing overall system performance. This paper introduces a novel approach: a weighted multiple-sensor combination algorithm that leverages the preamble error rate of individual sensors. Through the utilization of turbo coding with a rate of 1/3 and 4/8-ary frequency-shift keying method, extensive experiments spanning very long distances of over 160 km in the East Sea were conducted. The results of these experiments demonstrate that the proposed weighted multiple-sensor combination algorithm markedly improves BER performance compared to conventional linear combination algorithms.

An Enhanced Multi-Sensor Simultaneous Localization and Mapping (SLAM) Framework with Coarse-to-Fine Loop Closure Detection Based on a Tightly Coupled Error State Iterative Kalman Filter

In order to attain precise and robust transformation estimation in simultaneous localization and mapping (SLAM) tasks, the integration of multiple sensors has demonstrated effectiveness and significant potential in robotics applications. Our work emerges as a rapid tightly coupled LIDAR-inertial-visual SLAM system, comprising three tightly coupled components: the LIO module, the VIO module, and the loop closure detection module. The LIO module directly constructs raw scanning point increments into a point cloud map for matching. The VIO component performs image alignment by aligning the observed points and the loop closure detection module imparts real-time cumulative error correction through factor graph optimization using the iSAM2 optimizer. The three components are integrated via an error state iterative Kalman filter (ESIKF). To alleviate computational efforts in loop closure detection, a coarse-to-fine point cloud matching approach is employed, leverging Quatro for deriving a priori state for keyframe point clouds and NanoGICP for detailed transformation computation. Experimental evaluations conducted on both open and private datasets substantiate the superior performance of the proposed method compared to similar approaches. The results indicate the adaptability of this method to various challenging situations.

Application and Analysis of Array Production Logging Technology for Multiphase Flow in Horizontal Wells

Production logging (PL) instruments play a pivotal role in the comprehensive management and monitoring of oil and gas reservoirs. These devices facilitate the resolution of complex flow diagnosis challenges throughout the life cycle of hydrocarbon field exploitation. However, the advent of highly deviated well drilling technology has exposed certain limitations inherent in conventional centralized logging sensing techniques. When fluid flow within horizontal wells becomes segregated or even laminar, these traditional methods struggle to accurately decipher the zonal productions of oil, gas, and water. To address this challenge, multi-array production logging tools were developed in the late 1990s. Historically, these tools were characterized by considerable lengths, reaching up to 30 feet for an entire suite incorporating flow speed and holdup sensors that were not always collocated. Despite the integration of multiple sensors, uncertainties in determining flow profiles persisted. In this paper, we propose a novel integrated multi-parameter evaluation method based on measurements from a recently developed ultracompact flow array sensing tool, aimed at enhancing the accuracy of reservoir evaluation. The validity of the multi-parameter method is substantiated through a comparison of the new tool with an industry benchmark array PL tool on the same well. By combining the monitoring results, an optimization strategy for oil and gas extraction is presented, which is expected to improve the oil and gas recovery rate, thereby providing guidance for subsequent extraction endeavors. Moreover, we demonstrate how this innovative integrated workflow significantly enhances energy savings and efficiency, further underlining its value in modern oil and gas field management.

Sensor Fusion-Based Anthropomorphic Control of a Robotic Arm.

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

Development of Photonic Multi-Sensing Systems Based on Molecular Gates Biorecognition and Plasmonic Sensors: The PHOTONGATE Project.

This paper presents the concept of a novel adaptable sensing solution currently being developed under the EU Commission-founded PHOTONGATE project. This concept will allow for the quantification of multiple analytes of the same or different nature (chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current solutions. PHOTONGATE relies on two core technologies: a biochemical technology (molecular gates), which will confer the specificity and, therefore, the capability to be adaptable to the analyte of interest, and which, combined with porous substrates, will increase the sensitivity, and a photonic technology based on localized surface plasmonic resonance (LSPR) structures that serve as transducers for light interaction. Both technologies are in the micron range, facilitating the integration of multiple sensors within a small area (mm2). The concept will be developed for its application in health diagnosis and food safety sectors. It is thought of as an easy-to-use modular concept, which will consist of the sensing module, mainly of a microfluidics cartridge that will house the photonic sensor, and a platform for fluidic handling, optical interrogation, and signal processing. The platform will include a new optical concept, which is fully European Union Made, avoiding optical fibers and expensive optical components.

Intelligent fiber optic integrated sensing system for human motion monitoring

In recent years, the development of flexible bend sensors and their detection devices has attracted great interest. In this paper, an intelligent wearable plastic optical fiber (POF) integrated sensing system for human motion detection is presented. The system consists of POF bend sensors and a wearable fiber bend detection meter (WFBDM). By capturing the POF array image from the video stream, it realizes a sampling frequency of 91.7 times/s. It also greatly increases the integration of multiple sensors, with an integrated sensing area of only 0.35*0.35 mm2 for a single sensor. POF bending sensors with a diameter of 0.25 mm were tested using this system with a linear range of −40° to 90°, an accuracy of up to 0.4°, and a response time of 26.2 ms. The system has been successfully applied to human joint and muscle motion monitoring, and combined with machine learning to realize the intelligent recognition of human motion state. The sensing system is small in size, large in sensing range, high in sensitivity and good in stability, and has the ability of intelligent recognition and highly integrated multi-channel bend signals sensing.

Simultaneous detection of Alzheimer's biomarkers using a visual electrochemiluminescence bipolar array

Alzheimer's disease (AD), a progressive neurological disorder, presents challenges in early detection due to the limitations and costs of neuropsychological tests and neuroimaging. This study introduces a visual electrochemiluminescence (ECL) sensing device employing red and green signal lights on dual-channel closed bipolar electrodes (BPEs) for simultaneous Aβ oligomers and Tau proteins detection. To mitigate cross-chemical reactions, we physically separated target detection and ECL production within different chambers connected by BPEs. Utilizing efficient gold clusters (Au NCs) and Ru(bpy)32+ as green and red emitters, respectively, along with methylene blue (MB) as the recognition probe on the cathode sensing platform, we achieved a detection limit of 2.6 pM for Aβ oligomers and 0.3 pM for Tau proteins by measuring visual ECL intensity at two anodic poles of BPEs. This approach minimizes cross-chemical reactions, ensures robust ECL signal sensors, and allows integration of multiple sensors on a single chip. The proposed portable bipolar electrode chips employing ECL technology show promise for early and precise Alzheimer's disease diagnosis and personal healthcare.

(Invited) multi-Modal Nanoarray Sensors for Chemical Differentiation and Quantification

Integration of multiple sensors in a so-called sensor array platform allows multi-analyte detection and enhanced selectivity. However, the demanding requirement of device integration comes along with less than desired sophistication of electrical interconnects with increasing circuit complexity. In the past decade, we have been focused on establishing a multi-modal sensing platform based on a single nanostructured device. Such a single device platform can be operated with different modes such as photocurrent, resistance, potential, and impedance. These nanoarray sensors have been fabricated with a good scalability using vapor to solution phase depositions. The array composition could be rationally designed from pristine to heterogeneous across metals, ceramics, and polymers. Depending on the comprised material composition, decoration of nanoparticle sensitizers such as perovskite, metal oxide, or noble metal may enable drastic boosting of sensor performance under various gaseous mixtures such as nitrogen oxide (NOx), hydrocarbons (HCs), carbon mono-oxide (CO), and hydrogen in atmospheres. Toward mixed multi-analyte conditions, such a new chemical sensor allows differentiated detection as well as low-concentration quantification of multiple species in a single-device platform. Machine learning as driven by the multimodal sensor datasets is utilized to add and enhance the single sensor platform performance in terms of accuracy, detection range, and speciation differentiation.

A Systematic Review on Advancements in Remote Sensing for Assessing and Monitoring Land Use and Land Cover Changes Impacts on Surface Water Resources in Semi-Arid Tropical Environments

This study aimed to provide a systematic overview of the progress made in utilizing remote sensing for assessing the impacts of land use and land cover (LULC) changes on water resources (quality and quantity). This review also addresses research gaps, challenges, and opportunities associated with the use of remotely sensed data in assessment and monitoring. The progress of remote sensing applications in the assessment and monitoring of LULC, along with their impacts on water quality and quantity, has advanced significantly. The availability of high-resolution satellite imagery, the integration of multiple sensors, and advanced classification techniques have improved the accuracy of land cover mapping and change detection. Furthermore, the study highlights the vast potential for providing detailed information on the monitoring and assessment of the relationship between LULC and water resources through advancements in data science analytics, drones, web-based platforms, and balloons. It emphasizes the importance of promoting research efforts, and the integration of remote sensing data with spatial patterns, ecosystem services, and hydrological models enables a more comprehensive evaluation of water quantity and quality changes. Continued advancements in remote sensing technology and methodologies will further improve our ability to assess and monitor the impacts of LULC changes on water quality and quantity, ultimately leading to more informed decision making and effective water resource management. Such research endeavors are crucial for achieving the effective and sustainable management of water quality and quantity.

Real-time depth completion based on LiDAR-stereo for autonomous driving.

The integration of multiple sensors is a crucial and emerging trend in the development of autonomous driving technology. The depth image obtained by stereo matching of the binocular camera is easily influenced by environment and distance. The point cloud of LiDAR has strong penetrability. However, it is much sparser than binocular images. LiDAR-stereo fusion can neutralize the advantages of the two sensors and maximize the acquisition of reliable three-dimensional information to improve the safety of automatic driving. Cross-sensor fusion is a key issue in the development of autonomous driving technology. This study proposed a real-time LiDAR-stereo depth completion network without 3D convolution to fuse point clouds and binocular images using injection guidance. At the same time, a kernel-connected spatial propagation network was utilized to refine the depth. The output of dense 3D information is more accurate for autonomous driving. Experimental results on the KITTI dataset showed that our method used real-time techniques effectively. Further, we demonstrated our solution's ability to address sensor defects and challenging environmental conditions using the p-KITTI dataset.

High-Sensitivity Triple-Channel Optical Fiber Surface Plasmon Resonance Sensor

Optical fiber surface plasmon resonance (SPR) sensors are widely used for refractive index (RI) sensing, thanks to their high sensitivity. However, with the increasing demand for multiple analytes detection and sensor integration, a traditional single-channel optical fiber SPR sensor is outdated. We propose and demonstrate a novel triple-channel optical fiber SPR sensor with high sensitivity. The sensor is fabricated with side-polished single-mode fiber (SMF), where the sensing film consists of a layer of gold nanofilm and a layer of TiO2 nanofilm. The resonance wavelength range of the SPR sensing zone can be adjusted significantly by changing the TiO2 nanofilm thickness. Experimentally, by cascading three sensing zones whose TiO2 nanofilm thicknesses are 10, 30, and 60 nm, respectively, a triple-channel SPR sensor is realized. The average sensitivities of three sensing channels reach up to 2637.8, 3962.2, and 6097.3 nm/RIU in the RI range of 1.333–1.370 RIU, which is superior to most current multichannel SPR sensors. Besides, three sensing channels are independent without crosstalk. The proposed triple-channel SPR sensor has great potential in biological and chemical sensing fields, especially in multiple analytes detection applications.

Open‐Chip Droplet Splitting in Electrowetting

AbstractElectrowetting‐on‐dielectric (EWOD) has emerged as a powerful technique to perform on‐chip droplet operations like transportation, dispensing, splitting, and mixing in sandwiched droplets. In contrast, open‐chip droplet manipulation using electrowetting enables micro‐total‐analysis systems with multiple sensor integration and re‐routing capabilities. Droplet splitting has been the bottleneck in developing open‐chip platforms. Droplet splitting on an open‐chip platform using electrowetting‐on‐dielectric is presented. An energy‐based simulation model has been developed. It shows that splitting a sessile water droplet is impossible on an open‐chip configuration because of the low pad contact angle requirement. Low contact angles cannot be achieved due to contact angle saturation in electrowetting. It is experimentally shown that splitting is possible if the droplet is engulfed in an oil shell (i.e., in compound droplets). The planar electrode configurations and regime of electrowetting numbers for which splitting can be achieved are identified. It is observed that larger gaps and higher electrowetting numbers favor symmetrical splitting because the electrostatic force driving the actuation is significantly higher than the retarding interfacial forces. Conversely, asymmetrical splitting has been obtained when the actuation force is barely sufficient. Further the splitting of surfactant‐loaded single‐phase sessile droplets is demonstrated and a preferential surface charging phenomenon is explained.

Opto-ultrasound biosensor for wearable and mobile devices: realization with a transparent ultrasound transducer.

Mobile and wearable healthcare electronics are widely used for measuring bio-signals using various fusion sensors that employ photoplethysmograms, cameras, microphones, ultrasound (US) sensors, and accelerometers. However, the consumer demand for small form factors has significantly increased as the integration of multiple sensors is difficult in small mobile or wearable devices. This study proposes two novel opto-US sensors, namely (1) a wearable photoplethysmography (PPG)-US device and (2) a PPG sensor built-in mobile smartphone with a US sensor, seamlessly integrated using a transparent ultrasound transducer (TUT). The TUT exhibits a center frequency of 6 MHz with a 50% bandwidth and 82% optical transparency in visible and near-infrared regions. We developed an integrated wearable PPG-US device to demonstrate its feasibility and coupled the TUT sensor with a smartphone. We measured the heart rates optically and acoustically in human subjects and quantified the oxygen saturation optically by passing light through the TUT. The proposed proof-of-concept is a novel sensor fusion for mobile and wearable devices that require a small form factor and aim to improve digital healthcare. The results of this study can form the basis for innovative developments in sensor-based high-tech industrial applications, such as automobiles, robots, and drones, in addition to healthcare applications.

Hand Motion Analysis during the Execution of the Action Research Arm Test Using Multiple Sensors

The Action Research Arm Test (ARAT) is a standardized outcome measure that can be improved by integrating sensors for hand motion analysis. The purpose of this study is to measure the flexion angle of the finger joints and fingertip forces during the performance of three subscales (Grasp, Grip, and Pinch) of the ARAT, using a data glove (CyberGlove II®) and five force-sensing resistors (FSRs) simultaneously. An experimental study was carried out with 25 healthy subjects (right-handed). The results showed that the mean flexion angles of the finger joints required to perform the 16 activities were Thumb (Carpometacarpal Joint (CMC) 28.56°, Metacarpophalangeal Joint (MCP) 26.84°, and Interphalangeal Joint (IP) 13.23°), Index (MCP 46.18°, Index Proximal Interphalangeal Joint (PIP) 38.89°), Middle (MCP 47.5°, PIP 42.62°), Ring (MCP 44.09°, PIP 39.22°), and Little (MCP 31.50°, PIP 22.10°). The averaged fingertip force exerted in the Grasp Subscale was 8.2 N, in Grip subscale 6.61 N and Pinch subscale 3.89 N. These results suggest that the integration of multiple sensors during the performance of the ARAT has clinical relevance, allowing therapists and other health professionals to perform a more sensitive, objective, and quantitative assessment of the hand function.

Nanodevices for Biological and Medical Applications: Development of Single-Molecule Electrical Measurement Method

A comprehensive detection of a wide variety of diagnostic markers is required for the realization of personalized medicine. As a sensor to realize such personalized medicine, a single molecule electrical measurement method using nanodevices is currently attracting interest for its comprehensive simultaneous detection of various target markers for use in biological and medical application. Single-molecule electrical measurement using nanodevices, such as nanopore, nanogap, or nanopipette devices, has the following features:; high sensitivity, low-cost, high-throughput detection, easy-portability, low-cost availability by mass production technologies, and the possibility of integration of various functions and multiple sensors. In this review, I focus on the medical applications of single- molecule electrical measurement using nanodevices. This review provides information on the current status and future prospects of nanodevice-based single-molecule electrical measurement technology, which is making a full-scale contribution to realizing personalized medicine in the future. Future prospects include some discussion on of the current issues on the expansion of the application requirements for single-mole-cule measurement.

Recent Progress in Cellulose-Based Flexible Sensors

Flexible sensors are attractive due to potential applications in body exercise and ambient gas monitoring systems. Cellulose and its derivatives have combined superiorities such as intrinsic and structural flexibility, ease of chemical functionalization, moisture sensitivity, and mechanical stability, enabling them to be promising candidates as flexible supporting substrates and flexible sensitive materials. Significant progress consequently has been achieved to improve mechanical, electrical, and chemical performance. The latest advance in materials synthesis, structure design, fabrication control, and working mechanism of novel cellulose-based flexible sensors are reviewed and discussed, including strain sensors, humidity sensors, and harmful gas sensors. Various strategies were summarized to enhance sensor performance by surface group modifications, inorganic and organic conducting fillers optimization, multilayer structure design. Newly emerged processing techniques of self-assembly, vacuum filtration, and 3D printing were introduced as well to construct multiscale microstructures. The integration of multiple sensors toward smart and healthy exercise monitoring system is briefly reviewed. The facing challenges and future opportunities of cellulose-based flexible sensors were discussed and proposed at the end. This review provides inspiration and guidelines on how to design and fabricate cellulose-based flexible sensors.

Real-Time Hand Gesture Tracking for Human–Computer Interface Based on Multi-Sensor Data Fusion

Cerebral palsy is one of the main factors leading to children’s disability. A large number of such children have hand motor dysfunction, such as limited range of motion, abnormal gestures, etc. Our goal is to design a prototype of wearable gesture training equipment for such children. For this purpose, this paper presents the development of a wireless smart glove to facilitate the accurate measurement of finger movement through the integration of multiple IMU sensors. Commercially available gaming gloves are not fitted with sufficient sensors for this particular application and most of them can only provide one degree of freedom for finger flexion and extension. In this paper, a data glove integrated with nine degree of freedom inertial sensors in conjunction with complex multi-sensor data fusion is developed. In our method, a single/dual state measurement update switching extended Kalman filter is proposed to estimate the spatial attitude of each finger segment. Through traversing the kinematic chain, a tree type hand dynamic model based on joint constraints is established to realize the real-time gesture tracking. Furthermore, a visual interface is developed to display the tracking effect of gestures, and the reliability of our algorithm is verified by optical contrast experiments, the repeatability of joint angle is also evaluated by kinematic analysis. In general, all the experimental results demonstrated that our proposed framework can accurately track the 3-D hand motion. This glove will help quantify joint stiffness and monitor patient progression during the rehabilitation training process.

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring.

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.