Dynamic Sensor Research Articles

Abraded optical fibre-based dynamic range force sensor for tissue palpation

Tactile information acquired through palpation plays a crucial role in relation to surface characterisation and tissue differentiation - an essential clinical requirement during surgery. In the case of Minimally Invasive Surgery, access is restricted, and tactile feedback available to surgeons is therefore reduced. This paper presents a novel stiffness controllable, dynamic force range sensor that can provide remote haptic feedback. The sensor has an abraded optical fibre integrated into a silicone dome. Forces applied to the dome change the curvature of the optical fibres, resulting in light attenuation. By changing the pressure within the dome and thereby adjusting the sensor’s stiffness, we are able to modify the force measurement range. Results from our experimental study demonstrate that increasing the pressure inside the dome increases the force range whilst decreasing force sensitivity. We show that the maximum force measured by our sensor prototype at 20 mm/min was 5.02 N, 6.70 N and 8.83 N for the applied pressures of 0 psi (0 kPa), 0.5 psi (3.45 kPa) and 1 psi (6.9 kPa), respectively. The sensor has also been tested to estimate the stiffness of 13 phantoms of different elastic moduli. Results show the elastic modulus sensing range of the proposed sensor to be from 8.58 to 165.32 kPa.

DC characteristics of a dynamic vision sensor's photoreceptor circuit evaluated for event-based sensing in the mid-wave infrared

Forthcoming infrared event-based sensors will have utility in the space-based surveillance domain where they could potentially perform traditional sensing and tracking functions with significantly enhanced temporal resolution and reduced downstream datalink demands and power consumption. As a first step toward extending event-based sensing technology into the mid-wave infrared, a DC simulation of the conventional event-based sensor unit cell's photoreceptor circuit is performed, and the results are compared with measurements of a printed circuit board implementation of the same circuit to assess what design freedom is available to interface the photoreceptor with a mid-wave infrared photodetector. Detailed analysis of the circuit and measurements provides insight into which fundamental properties of the transistors drive the photoreceptor's dynamic range and demonstrates several characteristics that are relevant to mid-wave infrared sensing. These characteristics include the capability to produce a stable, low voltage bias on the infrared photodetector to maximize sensitivity, and that operating the circuit below room temperature increases the photoreceptor's dynamic range. Measurements show that even the simplest implementation of the photoreceptor circuit exhibits a dynamic range of logarithmic compression of 150 dB at room temperature. However, the dynamic range is ultimately limited by the mid-wave infrared photodetector's dark current activation energy, which is significantly lower than the threshold voltage (energy) of the photoreceptor's feedback transistor and, thus, there is an incentive for the photodetector's dark current to be diffusion-limited.

When the past becomes future-like: A phenomenological study of memory, time, and self-familiarity

AbstractThis paper sets out a phenomenological account of how the autobiographical past can, on occasion, assume certain future-like qualities. I begin by reflecting on the analogy of a bore wave, as employed in a novel by Julian Barnes. Building on this, I turn to Simone de Beauvoir and Jean-Paul Sartre in order to address how our memories are revised in light of our current concerns and vice versa. Then, by adapting Edmund Husserl’s conception of temporal “protention,” I show how acts of remembering are integral to a process of ongoing reconciliation between our current orientation towards the future and the autobiographical past. They sustain, disrupt, and reconsolidate a non-localized, dynamic sense of who we are, in ways that are inseparable from how we experience time.

Optimization Algorithms for Dynamic Environmental Sensing and Motion Planning of Quadruped Robots in Complex Environments on Unmanned Offshore Platforms

Optimization Algorithms for Dynamic Environmental Sensing and Motion Planning of Quadruped Robots in Complex Environments on Unmanned Offshore Platforms

Aristotle and the Stoics on the Notion of ἐνέργεια

Abstract The Stoic theory of movement has never been the object of a deep investigation despite the considerable number of sources in Neoplatonist commentators. This paper explores for the first time the Stoic notion of ἐνέργεια, which plays a fundamental role in the Stoic conception of movement and generally in the characterization of interaction between bodies. I will show that the Stoics identified movement and activity, so that everything that is active is necessarily moved. This implies that the Stoics merely characterized ἐνέργεια in a dynamic or kinetic sense, namely by connecting it with movement. In order to prove the coherence of this philosophical position to the Stoic doctrine, I will display the compatibility of Neoplatonists’ reports with other sources. Accordingly, I will claim that the Stoics conceived this peculiar sense of ἐνέργεια in reaction to Aristotle, who clearly distinguished between ἐνέργεια and κίνησις in his physical theory.

Analysis of Multivariable Sensor Responses to Multi-Analyte Gas Samples in the Presence of Interferents and Humidity.

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg-Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the mixtures, with reliable identification and accurate quantification, even in the presence of various interferents. Concentration errors of approximately ±5% are obtained for mixtures containing up to 6 BTEX compounds (including chemical isomers) and up to 4 interferents. Additionally, the study investigates the impact of humidity on the polymer/plasticizer-coated shear-horizontal surface acoustic wave (SH-SAW) sensors, demonstrating accurate concentration estimation in a relative humidity range from dry nitrogen to 65%. This sensing-and-multivariate-signal-processing approach is a promising candidate for reliable environmental monitoring in real-world applications.

Resistive Heating and Thermal Decomposition of an Additively Manufacturable Electrically Conductive Explosive

AbstractThe ability to transmit electrical signals through high explosive charges without using dense metal conductors could enable continuous monitoring of explosive charges with internally embedded sensors. With the sensor materials themselves being energetic, the impact on the intended detonation performance of the charge can be minimized. Embedded sensors enable real time, in‐situ measurements of conditions relevant to ageing assessments. Additionally, dynamic material property sensing can provide detonation performance data, enabling measurement of dynamic shock position. One proposed method to allow for electronic signal transfer through high explosives is to use electrically conductive explosive composites consisting of an electrically conductive polymer binder mixed with high explosive crystals. These conductive energetic pathways also serve as independently reactive elements and can provide additional functionality via dynamic sensor removal through resistive heating and subsequent decomposition. This work discusses the resistive heating of a previously developed, poly (3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) based, electrically conductive extrudable explosive composite. Experimental samples of the conductive explosive material were subjected to a range of direct current applications and the resulting heating and decomposition of the material is discussed.

Secondary measurement standard for calibration of dynamic pressure sensor to bridge the gap between existing static and dynamic standards

Secondary measurement standard for calibration of dynamic pressure sensor to bridge the gap between existing static and dynamic standards

Spiking Neural Networks for Real-Time Pedestrian Street-Crossing Detection Using Dynamic Vision Sensors in Simulated Adverse Weather Conditions

This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in dynamic, low-light, and high-contrast environments, this research evaluates the effectiveness of SNNs compared to traditional Convolutional Neural Networks (CNNs). The experimental setup involved a custom dataset from the CARLA simulator, designed to mimic real-world variability, including rain, fog, and varying lighting conditions. Additionally, the JAAD dataset was adopted to allow for evaluations using real-world data. The SNN models were optimized using Temporally Effective Batch Normalization (TEBN) and benchmarked against well-established deep learning models, concerning their accuracy, computational efficiency, and energy efficiency in complex weather conditions. This study also conducted a comprehensive analysis of energy consumption, highlighting the significant reduction in energy usage achieved by SNNs when processing DVS data. The results indicate that SNNs, when integrated with DVSs, not only reduce computational overhead but also dramatically lower energy consumption, making them a highly efficient choice for real-time applications in autonomous vehicles (AVs).

A New Type of Dynamic Vibration Fiber Sensor.

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc.

Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach

This paper presents a robust position estimation technique for autonomous vehicles navigating urban and highway environments. It employs a probabilistic framework, integrating Monte Carlo simulation and Bayesian filtering via particle filters to represent position estimates beyond Gaussian distributions. To improve accuracy, Unscented Kalman Filtering (UKF) is used to fuse radar and lidar data, facilitating the detection of static, pole-like objects. These objects are matched with landmarks from a detailed reference map generated through 3D lidar scans. The proposed method addresses both lateral and longitudinal localisation challenges, ensuring precise vehicle positioning. Comprehensive simulation tests validate the system's effectiveness, demonstrating reliable performance across various driving conditions.

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Development of 3D UDTs for Traffic Monitoring in Unreal 5 Game Engine

Abstract. Digital Twins recently, are a much regarded research topic in geospatial engineering, offering great potential for optimization of a variety of interrelated processes in urban environments. A major component of a Digital Twin use case is real-time sensor data input and analysis, which is used as foundation for simulations that lead to decision-support for authorities or even automatic response into the physical environment. The use of game engines as platform for urban Digital Twin use cases is promising due to superior visualization and animation capabilities, that facilitate diverse monitoring use cases for highly dynamic sensor data inputs. This contribution evaluates the fit for purpose of the Unreal Engine 5 game engine for digital twin use cases, by conducting a case study on urban traffic monitoring in the city of Liverpool Australia. The case study proved, that the necessary connections of static data provided via a geodatabase and dynamic real-time data from public portals can be integrated into the engine to establish an interactive Digital Twin use cases for evaluating traffic user interactions.

Efficient Multi-floor Indoor Mapping with A Versatile Rotating LiDAR System

Abstract. Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D environmental map. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. To this end, an efficient multi-floor indoor mapping system is proposed in this paper utilizing a versatile rotating LiDAR platform. In the front end, measurements from motor, IMU and LiDAR are tightly integrated to track the fast motion state of system, based on iterative Error State Kalman Filter (ESKF). Then linear and planar features are extracted from the point cloud map voxelized with adaptive resolutions. A sliding-window-based batch optimization is performed to simultaneously optimize the states of local frames with reference to the map consistency. In the experiments, we investigated the influence of rotating speed on the mapping performance as well as the superiority of rotating mechanism when compared to standard LiDAR setup. Moreover, comparative studies with one of the SOTA work, FAST-LIO2, have shown the competitive mapping results in multi-floor indoor environments.

Target detection and obstacle avoidance for UAV based on dynamic vision sensor

Aiming at the problem of UAV sensing dynamic targets and avoiding high-speed dynamic obstacles in dynamic environments, a target detection and obstacle avoidance algorithm based on dynamic vision sensor is designed. A filtering method and motion compensation algorithm are designed to filter out background noise and hot spot noise in the event stream as well as redundant events caused by the camera's own motion. A dynamic target fusion detection algorithm that fuses event images and RGB images is designed to ensure the reliability of detection. The target motion trajectory is estimated based on the detection results, and the speed obstacle method is improved to avoid dynamic obstacles by combining the obstacle motion characteristics and UAV dynamic constraints. A large number of simulation tests, handheld tests and flight tests verify the feasibility of the proposed algorithm.

Quantum Sensing of Spin Dynamics Using Boron-Vacancy Centers in Hexagonal Boron Nitride.

Spin defects embedded in solid-state systems are appealing for quantum sensing of materials and for quantum science and engineering. The spin-sensitive photoluminescence of optically active spin defects in Van der Waals based materials, such as the boron-vacancy (V_{B}^{-}) center in hexagonal boron nitride, enables its application as a quantum sensor to detect weak, spatially localized magnetic static and dynamic fields. However, the utility of V_{B}^{-} centers to probe spin dynamics in magnetic systems has yet to be demonstrated; this is essential to establish the V_{B}^{-} as a modular sensing platform that can be seamlessly integrated with emergent quantum materials to probe a wide range of static and dynamic phenomena. Here, we use V_{B}^{-} centers to experimentally probe uniform mode magnon dynamics and optically perform ferromagnetic resonance spectroscopy on a thin magnetic film.

Collaborative Autoethnography of Cancer Patients' Dynamic Sense of Agency.

Through collaborative autoethnography, we studied shifts in cancer patients' sense of agency and the meaning of cancer during the diagnostic and treatment phases. This article contributes to the illness management literature by adopting sense of agency perspective that provides new understanding of retrospective interpretation of cancer patients' agency. The authors' experiences of receiving cancer diagnoses and a related, collectively written story illustrate how relational and contextual elements facilitate rapid shifts in cancer patients' sense of agency and illness management. The findings illustrate shifts in the sense of agency as a collaborative and reflexive process between cognitive, emotional, and bodily constraints and adjustments. We demonstrate how shifts in patients' sense of agency and respective changes in meanings attached to cancer were shaped by near ones, healthcare actors, and other cancer patients, as well as the COVID-19 pandemic and the fear of military conflict due to Finland neighbor Russia's war on Ukraine. Furthermore, the study illustrates how shifts in sense of agency shape and are shaped by changes in the understanding of cancer as either a secondary issue, ambiguous stranger, travel companion, or enemy.

Perspective on the Development and Integration of Hydrogen Sensors for Fuel Cell Control

The measurement of hydrogen concentration in fuel cell systems is an important prerequisite for the development of a control strategy to enhance system performance, reduce purge losses and minimize fuel cell aging effects. In this perspective paper, the working principles of hydrogen sensors are analyzed and their requirements for hydrogen control in fuel cell systems are critically discussed. The wide measurement range, absence of oxygen, high humidity and limited space turn out to be most limiting. A perspective on the development of hydrogen sensors based on palladium as a gas-sensitive metal and based on the organic magnetic field effect in organic light-emitting devices is presented. The design of a test chamber, where the sensor response can easily be analyzed under fuel cell-like conditions is proposed. This allows the generation of practical knowledge for further sensor development. The presented sensors could be integrated into the end plate to measure the hydrogen concentration at the anode in- and outlet. Further miniaturization is necessary to integrate them into the flow field of the fuel cell to avoid fuel starvation in each single cell. Compressed sensing methods are used for more efficient data analysis. By using a dynamical sensor model, control algorithms are applied with high frequency to control the hydrogen concentration, the purge process, and the recirculation pump.

OR-LIM: Observability-aware robust LiDAR-inertial-mapping under high dynamic sensor motion

Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D data. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. In this paper, we present OR-LIM, a novel observability-aware LiDAR-inertial-mapping system. Essentially, it combines a robust real-time LiDAR-inertial-odometry (LIO) module with an efficient surfel-map-smoothing (SMS) module that seamlessly optimizes the sensor poses and scene geometry at the same time. To improve robustness, the planar surfels are hierarchically generated and grown from point cloud maps to provide reliable correspondences for fixed-lag optimization. Moreover, the normals of surfels are analyzed for the observability evaluation of each frame. To maintain global consistency, a factor graph is utilized integrating the information from IMU propagation, LIO as well as the SMS. The system is extensively tested on the datasets collected by a low-cost multi-beam LiDAR (MBL) mounted on a rotating platform. The experiments with various settings of sensor motion, conducted on complex multi-story buildings and large-scale outdoor scenes, demonstrate the superior performance of our system over multiple state-of-the-art methods. The improvement of point accuracy reaches 3.39–13.6 % with an average 8.71 % outdoor and correspondingly 1.89–15.88 % with 9.09 % indoor, with reference to the collected Terrestrial Laser Scanning (TLS) map.

Influence of tip air injection on the unsteady blade force under circumferential pressure distortion

In order to further understand the impact of tip air injection on compressor stability under circumferential pressure distortion, a series of experimental studies on tip air injection under uniform and circumferential distortion were conducted on a low-speed single rotor axial flow compressor. The unsteady measurements were carried out by use of microsensors and strain gauges bonded on the rotor blade surfaces, and a collection of dynamic pressure sensors installed on the casing wall. Results indicate that with the increase in the injected momentum ratio, the load at the leading edge near the rotor blade tip obviously decreases, thereby delaying the stall. By comparing the pressure distribution on the rotor blade surface under different injected momentum ratios, tip air injection can effectively suppress flow separation at the blade tip to enhance flow capability. In addition, it can also weaken the fluctuation of tip leakage flow and reduce the dynamic stress similar to natural vibration near the blade root both in stationary and rotating coordinate systems. Under the condition of inlet circumferential distortion, tip air injection can significantly reduce the load at the distorted region and suppress the low-frequency disturbances measured on the rotor blade surface caused by inlet distortion. In addition, the dynamic stress near the blade root can also be effectively reduced by tip air injection. It implies that tip air injection can both extend the stall margin and reduce blade vibration when suffering from circumferential distortion; thus, the compressor can safely operate.