Large Sensor Research Articles

Large measurement-range and low temperature cross-sensitivity optical fiber curvature sensor based on Michelson interferometer

A large-measurement-range fiber curvature sensor based on Michelson interferometer is proposed and investigated. The sensor is fabricated by sandwiching a bitaper between a single-mode fiber (SMF) and a piece of multimode fiber (MMF), and the MMF end face is multiply discharged to form a fiber sphere. The mode interference is accumulated in the MMF for the bitaper could excite high-order modes and couple them with core mode. The sensor can be defined as a cantilever beam structure, in which the fiber sphere serves as a mass block, while the bitaper and MMF act as cantilever beam. The reflection interference spectrum is sensitive to ambient curvature, so a large-range curvature detection is realized. Experimental results show that the sensor has a sensitivity of −0.6888 nm/m−1 and −1.812 dB/m−1 in the curvature range of 0–12.2894 m−1. The maximum temperature sensitivity is 0.0078 nm/°C and 0.007 dB /°C in the temperature range of 30–150 °C, and the maximum temperature cross-sensitivity is −0.0113 m−1/°C, which could be neglected in the application. The sensor has advantages of low cost, easy fabrication, large curvature detection range, and low temperature cross-sensitivity, making it has broad application prospects in the curvature sensing field.

Screen Violence from Settler Colonialism to Cognitive Capitalism

While the HBO show Westworld (2016–present, created by Lisa Joy and Jonathan Nolan) has gained much critical attention for its byzantine plotting and philosophical conundrums, the present discussion focuses instead on the basic premise on which the titular park operates, namely that the algorithms that govern human behavior can be disclosed by studying how human beings behave toward image beings. Under the guise of a tactile experience of a make-believe past, the park attractions clandestinely function as a large behavioral sensor, extracting actionable data from the guests who reveal their inner drives when interacting with the host environment. Taking its cue from the opening titles of the first season, the argument pivots on the master trope of the series: the machine-readable scroll of perforated paper that commands the automated performance of the player piano. This motif is examined through a double-pronged approach that aligns the anthropology of images developed by Hans Belting, which understands the relation between humans and images as the interactions between “hosts” and “guests,” with the archaeology of media and its dominant concern to uncover the prehistory of the automated control systems of the computer age. While Westworld proffers a timely allegory of biopolitical capture along the digital frontier, the show ultimately testifies to the failure to constructively engage with the precarious relation between hosts and guests that to an equal extent defines our contemporary moment. The initial problem raised by Westworld, the ethics of killing virtual beings, thus gives rise to a broader historical inquiry that concerns the inability of human societies to face the past and deal with the images they inherit.

High-speed hydrogen sensor fabricated using a platinum/titanium oxide nanocontact

Titanium dioxide (TiO2) thin films have been extensively studied for use in solid-state hydrogen (H2) gas sensors. In this work, TiOx/platinum (Pt) nanocontacts which constitute a Schottky-diode sensor were developed using shadow evaporation. Large resistance changes and short reaction times were achieved through measurement of the resistance of the contacts. The sensing was quite depended on the thickness of the TiOx layer. The results indicate that the large sensor response over 105 was achieved just neighboring outermost surfaces of TiOx layer. As this sensor can be fabricated at temperatures less than 400 K, it is expected to be applicable to on-tip sensors.

Wearable OPM-MEG: A changing landscape for epilepsy.

Magnetoencephalography with optically pumped magnometers(OPM-MEG) is an emerging and novel, cost-effective wearable system that can simultaneously record neuronal activity with high temporal resolution ("when" neuronal activity occurs) and spatial resolution ("where" neuronal activity occurs). This paper will first outline recent methodological advances in OPM-MEG compared to conventional superconducting quantum interference device (SQUID)-MEG before discussing how OPM-MEG can become a valuable and noninvasive clinical support tool in epilepsy surgery evaluation. Although OPM-MEG and SQUID-MEG share similar data features, OPM-MEG is a wearable design that fits children and adults, and it is also robust to head motion within a magnetically shielded room. This means that OPM-MEG can potentially extend the application of MEG into the neurobiology of severe childhood epilepsies with intellectual disabilities (e.g., epileptic encephalopathies) without sedation. It is worth noting that most OPM-MEG sensors are heated, which may become an issue with large OPM sensor arrays (OPM-MEG currently has fewer sensors than SQUID-MEG). Future implementation of triaxial sensors may alleviate the need for large OPM sensor arrays. OPM-MEG designs allowing both awake and sleep recording are essential for potential long-term epilepsy monitoring.

Weave-pattern-dependent fabric piezoelectric pressure sensors based on polyvinylidene fluoride nanofibers electrospun with 50 nozzles

Wearable pressure sensors having versatile device structures have been extensively investigated to achieve high sensitivity under mechanical stimuli. Here, we introduce piezoelectric pressure sensors based on fabrics woven using polyvinylidene fluoride (PVDF) weft and polyethylene terephthalate (PET) warp yarns with different weave structures: 1/1 (plain), 2/2, and 3/3 weft rib patterns. The dependence of the pressure-sensing performance on the weave pattern is demonstrated with an actual large-scale fabric up to the ~2 m scale. An optimized pressure sensor with a 2/2 weft rib pattern produced a high sensitivity of 83 mV N−1, which was 245% higher than that of the 1/1 pattern. The detection performance of the optimal fabric was extensively evaluated with a variety of ambient input sources, such as pressing, bending, twisting, and crumpling, as well as various human motions. Further, a large all-fabric pressure sensor with arrayed touch pixel units demonstrated highly sensitive and stable sensing performance.

Performance Analysis of Wireless Sensor Network by Variation in Cluster Head Selection through mLEACH-CS Algorithm

Abstract: The energy depletion at the nodes due to large volume of data transmission and data reception for large scale Wireless Sensor Networks (WSN) is one of the important areas of concern. Unbalanced energy consumption due to data traffic overhead at the nodes near the base station (BS) results in early node death and reduction in network lifetime. To balance the load at the wireless nodes during clustering, a novel hybrid clustering and routing protocol has been proposed based on mLEACH (modified Low-Energy Adaptive Clustering Hierarchy) and Cuckoo Search (CS) algorithm. Initially, mLEACH protocol is utilized to solve a novel equation designed to select the cluster heads by rotation at every round of data transmission based on the maximum permissible cluster heads in the network. In the second phase, CS algorithm is implemented for uniform distribution of the cluster heads in the network for efficient multi-hop data routing in the network based on several weighted factors. The performance of the proposed algorithm is compared with other algorithms in literature in terms of energy consumption and network lifetime for different positions of the base station and varied number of cluster heads in the model application area. The proposed mLEACH-CS algorithm shows a significant improvement by 60% considering 300 nodes in the WSN architecture while it decreased to 40% for denser network architecture. Optimal choice of the number and position of the cluster heads in certain network architecture has proved beneficial for designing an energy efficient wireless network with improved network lifetime.

High sensitivity and large measurement range magnetic field micro-nano fiber sensor based on Mach-Zehnder interference

High sensitivity and large measurement range magnetic field micro-nano fiber (MFM-NF) sensor based on Mach-Zehnder interference has been proposed and demonstrated. This sensor is composed of micro-nano optical fiber (MNF) and magnetic fluid (MF). MNF is fabricated with rapidly changed cone region and small diameter micro-nano region, the core mode will be coupled to the cladding duo to the small enough diameter of the MNF, and the core mode and the cladding mode will be form Mach-Zehnder interference. The change of magnetic field leads to the change of MF refractive index, and the refractive index near the micro-nano region strongly affects the mode interference. Under the magnetic field increased, the magnetic field sensitivity of the sensor is up to 69 pm/Gs in the range of 50 ∼ 200 Gs. The linearity was 0.9952. Under the magnetic field decreased, the linear correlation is 0.9942 in the range of 200 ∼ 0 Gs, and the magnetic field sensitivity is 29 pm/Gs. The sensor has the advantages of easy fabrication, low cost and simple structure, and it would be have a wide application prospect in the field of magnetic field sensing.

High Spatial Resolution 2C-2D PIV Measurements Using A 47 MPx Sensor Of High Reynolds Number Turbulent Boundary Layer Flow

In the past decade, advances in electronics technology have made larger imaging sensors available to the experimental fluid mechanics community. These advancements have enabled the measurement of 2-component 2-dimensional (2C-2D) velocity fields using particle image velocimetry (PIV) with much higher spatial resolution than previously possible using a single camera. Although previously reported experiments have incorporated multiple-camera arrays to acquire high spatial resolution PIV, using a single large camera can greatly reduce the complexity of the experimental setup as well as the error introduced by the calibration between the cameras. In this paper, the ability of a single large sensor for high spatial resolution PIV is demonstrated by performing the measurement of a zero-pressure-gradient turbulent boundary layer (ZPG-TBL). In post-processing the PIV images, the lens distortion error is of particular importance, as the lens distortion error increases with the size of the imaging sensor. The third-order polynomial functions are used to model the lens distortion in this study, and the correction is performed on the PIV vectors to save computational cost. The first- and second-order statistics are calculated and compared with the profiles captured by small camera arrays, and the result shows that the corrected profiles agree well with the previously acquired data, therefore, the lens distortion error can be corrected.

Skin-inspired large area iontronic pressure sensor with ultra-broad range and high sensitivity

The development of flexible electronics and intelligent wearable devices requires flexible electronic skin with broad-range high sensitivity and sufficient spatial resolution through high-level sensor integration. Inspired by both the ion migration behaviors and the high-density distribution of tactile mechanical receptors in human skin, microstructured iontronic force-sensitive films and in-plane interdigital electrodes are integrated to form skin-inspired large area iontronic pressure sensors. Furthermore, by introducing the pressure-dependent unit area capacitance (UAC), the theoretical model of the in-plane iontronic sensor is successfully built and all parameters of the device are systematically analyzed from theoretical and experimental perspectives to achieve high-performance sensors. Such skin-inspired iontronic sensors exhibit an unexpected high sensitivity (365 kPa−1), ultra-broad range (1.7 Pa-1000 kPa), and remarkable reproducibility (10000 cycles), together with a wonderful capability of large array sensors (32 ×32). Benefitting from the outstanding comprehensive performance, the potential application in health monitoring has been demonstrated and an intelligent footpad based on iontronic sensors has been fabricated to detect falling danger of humans. The skin-inspired in-plane iontronic pressure sensor provides a new approach to achieve high-performance large-scale array of E-skin, which delivers great potential applications in next-generation health monitoring and wearable electronics.

Evaluation of passive CMOS strip sensors

Silicon sensors will continue to be the central tracking elements for upcoming particle physics detectors. They will have to cover large areas and thus be a main cost driver. The silicon sensors currently used are available only from very few manufacturers, thus detector technologies and designs that can be realized through established commercial industrial production processes and are cost-effective are becoming increasingly relevant. The CMOS technology is one of the important candidates. Since typically CMOS foundries are equipped for producing much smaller sizes than the currently used wafer-scale strip sensors, several neighbouring reticles have to be connected via a stitching process to obtain large sensors. In this study, strip sensors were designed and developed with the passive p-CMOS 150 nm process including stitching of up to five reticles. After initial electrical characterizations the sensors were tested in the laboratory with a 90Sr source and infrared lasers. The key investigation was to evaluate the impact of stitching on the sensor performance. The results presented will demonstrate that the stitching does not show any negative effect on the sensor performance before and after irradiation, and the stitching process is successful.

Energy efficient grid based k‐means clustering algorithm for large scale wireless sensor networks

SummaryHierarchical based routing is a kind of group‐based routing that consists in creating of a virtual hierarchy among network sensor nodes. This class of routing technique is generally designed for large‐scale networks. It aims to efficiently increase network lifetime by cutting the whole network into clusters. However, traditional clustering techniques show some limitations and do not take into consideration the self‐organized with dynamic topology inherent in wireless sensor networks (WSNs). These limitations can lead to an unbalanced cluster head distribution that affects the whole network energy consumption. In this paper, we propose a grid‐based k‐means clustering protocol (named GBK), which combines grid‐based routing with k‐means algorithm in order to overcome the above mentioned weaknesses. From the supervised zone area size parameter, the base station determines the optimal grid size based on our optimization study. Afterwards, the k‐means algorithm is executed in every grid cell generating a cluster head per cell grid where the nearest node to the grid cell centroid is elected. An enhancement of this proposed GBK algorithm named GBK‐R is also proposed to extend network stability of the GBK algorithm by node scoring calculation that take as parameters the node remaining energy in addition to the distance to centroid. Our proposed GBK and GBK‐R algorithms allow for an enhanced network stability and increase the network lifetime as demonstrated by our performance evaluation study. In addition, this GBK clustering algorithm provides a better network topology control and a better control of the random nature of node distribution by generating cluster heads with bounded localization.

Highly sensitive large strain cellulose/multiwalled carbon nanotubes (MWCNTs)/thermoplastic polyurethane (TPU) nanocomposite foams: From design to performance evaluation

Aerogel-based polymer composite foams are promising for large strain piezoresistive sensors, but their aerogel skeleton is partially destroyed during the foaming process, limiting their sensitivity. Herein, the thermoplastic polyurethane was synthesized on the aerogel skeleton to obtain cellulose/multiwalled carbon nanotubes (MWCNTs)/thermoplastic polyurethane (TPU) nanocomposite materials foamed with the aid of supercritical carbon dioxide (ScCO2). A series of lightweight, high strength, and high sensitivity nanocomposite foams were developed, possessing an average diameter of ~9.11 µm and a cell density of 6.84 × 108 cells/cm3. The mechanical properties, including compressive strength and compressive modulus, were promisingly 5.56 and 28.90 MPa, respectively. Under 50 % strain, apparent high repeatability of force-to-electricity conversion behavior was detected in 100 compression cycles.

An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements.

The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO2 concentrations as a proxy for exhaled air can help to shed light on potential aerosol pathways. While the former typically lack accurate boundary conditions as well as spatially and temporally resolved validation data, currently existing measurement systems often probe rooms in non-ideal, single locations. Addressing both of these issues, a large and flexible wireless array of 50 embedded sensor units is presented that provides indoor climate metrics with configurable spatial and temporal resolutions at a sensor response time of 20 s. Augmented by an anchorless self-localization capability, three-dimensional air quality maps are reconstructed up to a mean 3D Euclidean error of 0.21 m. Driven by resolution, ease of use, and fault tolerance requirements, the system has proven itself in day-to-day use at ETH Zurich, where topologically differing auditoria (at-grade, sloped) were investigated under real occupancy conditions. The corresponding results indicate significant spatial and temporal variations in the indoor climate rendering large sensor arrays essential for accurate room assessments. Even in well-ventilated auditoria, cleanout time constants exceeded 30 min.

Selective Translocation of Cyclic Sugars through Dynamic Bacterial Transporter.

The selective translocation of molecules through membrane pores is an integral process in cells. We present a bacterial sugar transporter, CymA of unusual structural conformation due to a dynamic N terminus segment in the pore, reducing its diameter. We quantified the translocation kinetics of various cyclic sugars of different charge, size, and symmetry across native and truncated CymA devoid of the N terminus using single-channel recordings. The chemically divergent cyclic hexasaccharides bind to the native and truncated pore with high affinity and translocate effectively. Specifically, these sugars bind and translocate rapidly through truncated CymA compared to native CymA. In contrast, larger cyclic heptasaccharides and octasaccharides do not translocate but bind to native and truncated CymA with distinct binding kinetics highlighting the importance of molecular charge, size and symmetry in translocation consistent with liposome assays. Based on the sugar-binding kinetics, we suggest that the N terminus most likely resides inside the native CymA barrel, regulating the transport rate of cyclic sugars. Finally, we present native CymA as a large nanopore sensor for the simultaneous single-molecule detection of various sugars at high resolution, establishing its functional versatility. This natural pore is expected to have several applications in nanobiotechnology and will help further our understanding of the fundamental mechanism of molecular transport.

Eigen-Spectrum Estimation and Source Detection in a Massive Sensor Array Based on Quantum Assisted Hamiltonian Simulation Framework

In this work, we propose quantum assisted eigenvalue estimation and target detection algorithms for a large sensor array via Hamiltonian simulation. Quantum algorithms provide complexity advantage of a certain class of problems on a quantum computer with fewer physical resources as compared to their classical counterparts. The proposed algorithms make use of the quantum phase estimation (QPE) as its core computing component. We have introduced an analytical quantum framework to map from classical to quantum in the context of target detection. Target detection involves an appropriate choice of threshold based on the probability of detection or false alarm. We exploited the massive sensor array structure and invoked the random matrix theory to propose an optimal threshold. It also takes into account the quantum measurement noise in the framework. Numerical simulations are performed to ascertain the efficacy of the proposed framework. The results suggest near term applications of the quantum algorithm for large-scale linear systems.

DTAR: A Dynamic Threshold Adaptive Ranking-Based Energy-Efficient Routing Algorithm for WSNs

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance ([Formula: see text]) and Residual Energy ([Formula: see text]). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency.

High and temperature-insensitive piezoelectric performance in the lead-free Sm-doped BiFeO3–BaTiO3 ceramics with high Curie temperature

Large sensor piezoelectric constant (d33 = 334 pC/N) and superior actuator piezoelectric constant (d33* = 552 pm/V) as well as a high Curie temperature (TC = 454 °C) were obtained simultaneously in the lead-free 0.67Bi1.03FeO3-0.33Ba1-xSmxTiO3 ceramics. Such an excellent and temperature-insensitive piezoelectric performance with only 10% temperature variation of piezoelectric strain at the range of 25–125 °C is highly desirable for real applications. The structural origin of the enhanced piezoelectric performance is mainly attributed to the morphotropic phase boundary and the highest known tetragonality (cT/aT = 1.02) in such materials. Transmission electron microscopy and electro-mechanical phenomenological theory demonstrate that the superior d33 and d33* are associated with the hybrids nanodomains (60–90 nm) and flattened thermodynamic energy profile owing to the local structure heterogeneity. These results are superior as the piezoelectric properties are temperature-independent and the material has large d33*, and high TC compared to other lead-free piezoelectric ceramics.

A novel time difference of arrival source localization method based on constrained scoring algorithm with a calibration emitter

In this paper, we focus on the radiation source localization problem based on time-difference-of-arrival (time difference of arrival (TDOA)) measurements in the presence of sensor position errors. In order to alleviate the estimation degradation in locating a radiation source caused by the sensor position uncertainty, we propose to use a single calibration source and develop a novel two-stage localization algorithm based on the constrained scoring algorithm (CSA). In the first stage, we use the information of the calibration source to reduce the sensor position deviation, and here the CSA method is utilised to solve a quadratic constrained maximum likelihood (ML) objective function which is formulated from a set of underdetermined pseudo-linear equations. In the second stage, using the updated sensor positions and the TDOA measurements of the radiation source, another ML estimation problem with quadratic constraints is formulated, and we again invoke the CSA method to jointly estimate the source and sensor positions. Moreover, the asymptotically optimal performance of the proposed algorithm is mathematically analysed and proved based on the first-order error analysis. The simulation results verify the excellent performance of our algorithm and also demonstrate its robustness of resisting large measurement noise and sensor position errors.

Performance of a complementary metal-oxide-semiconductor sensor for laser guide star wavefront sensing

The adaptive optics systems of future Extremely Large Telescopes (ELTs) will be assisted with laser guide stars (LGS) which will be created in the sodium layer at a height of ≈90 km above the telescopes. In a Shack–Hartmann wavefront sensor, the long elongation of LGS spots on the sub-pupils far apart from the laser beam axis constraints the design of the wavefront sensor (WFS) which must be able to fully sample the elongated spots without undersampling the non-elongated spots. To fulfill these requirements, a newly released large complementary metal oxide semiconductor sensor with 1100 × 1600 pixels and 9 μm pixel pitch could be employed. Here, we report on the characterization of such a sensor in terms of noise and linearity, and we evaluate its performance for wavefront sensing based on the spot centroid variations. We then illustrate how this new detector can be integrated into a full LGS WFS for both the European Southern Observatory’s ELT and the Thirty Meter Telescope.

Soft and flexible large-strain piezoresistive sensors: On implementing proprioception, object classification and curvature estimation systems in adaptive, human-like robot hands

Soft and flexible large strain piezoresistive sensors can be widely used in robotic applications due to their simplicity and compact and lightweight structure. In this study, combining conductive filler of carbon black with elastomers, a soft elastomer strain sensor that can reach very large elongations is presented. These sensors have good linearity and extensibility. Stress relaxation does affect the accuracy of the sensor, but this can be minimized. In this work, we demonstrate the application of this sensor into the structure of an adaptive, human-like robot hand to perform object classification and object curvature estimation employing hand proprioception (measuring the bending angles of the fingers). Experiments demonstrated that the sensors could assist the hand with the ability to classify the object’s basic shape using a single grasp.