Optimized Sensor Geometry Research Articles

Improving Localization Accuracy under Constrained Regions in Wireless Sensor Networks through Geometry Optimization.

In addition to various estimation algorithms, the target localization accuracy in wireless sensor networks (WSNs) can also be improved from the perspective of geometry optimization. Note that existing placement strategies are mainly aimed at unconstrained deployment regions, i.e., the positions of sensors are arbitrary. In this paper, considering factors such as terrain, communication, and security, the optimal range-based sensor geometries under circular deployment region and minimum safety distance constraints are proposed. The geometry optimization problem is modeled as a constrained optimization problem, with a D-optimality-based (maximizing the determinant of FIM matrix) scalar function as the objective function and the irregular feasible deployment regions as the constraints. We transform the constrained optimization problem into an equivalent form using the introduced maximum feasible angle and separation angle, and discuss the optimal geometries based on the relationship between the minimum safety distance and the maximum feasible angle. We first consider optimal geometries for two and three sensors in the localization system, and then use their findings to extend the study to scenarios with arbitrary numbers of sensors and arbitrarily shaped feasible regions. Numerical simulation results are included to verify the theoretical conclusions.

Design and Fabrication of Soft 3D Printed Sensors and Performance Analysis of the Soft Sensors in a C-leg as Sensing Element

In soft robotics, a recent challenge is to decrease the number of rigid components used tocreate entirely soft robots. A common rigid component used in soft robots is the rigid encoder, which should be replaced with a soft counterpart if possible. In this work, we de-sign and manufacture a soft sensor, which is embedded into a C-shaped leg of a soft, legged, miniature robot. Our main goal is to show that we can embed a soft sensor into and receive contact feedback from a soft C-shaped leg of our soft miniature quadruped. We test various sensor parameters using custom test setups to analyze the soft sensor performance. Our soft sensor design is iterated by experimentally investigating several sensor shape options. For the C-leg of the soft miniature quadruped, optimal sensor geometry and position for the sensor implementation are found from a discrete design space as the outcome of this work. We received feedback from the soft sensor and compared commercial encoder data to the soft sensor embedded C-leg data. We managed to detect the rotation speed of the C-leg with the accuracy of 87.5% on a treadmill and with the accuracy of %86.7 under free rotation of the C-leg. However, if connection loss occurs in the miniature slipring mechanism, the error percentage in estimating the rotational speed increases significantly.

Flex-PCB Quench Antenna Developments at FNAL

Flexible printed-circuit-board-based quench antenna arrays have been developed at FNAL since 2017. They proved to be low-noise, low-cost, sensitive, and non-invasive sensors for quench characterization in superconducting magnets, having the potential to extend even to quench detection. Starting with applications of small area sensors of this type, we have explored feasibility for operations at high pressure, and have invested in multi-channel quench antenna arrays covering the complete area of the innermost magnet layer of the conductor. We've also tested and optimized sensor geometry and relative orientations to increase sensitivity and spatial resolution. Beyond developments of warm and cold bore antennas, we've progressed to concepts which attach quench antennas to the conductor surfaces of accelerator magnets. Multiple programs and projects at FNAL and collaborating institutions benefited from this work already and our plans target further extensions of capabilities and applicability of these devices. In this paper we present the logic behind the development steps taken so far, status of our work and direction of near-term research on quench antennas at Fermilab.

Detection Limits of Tetrapolar Impedance Sensors for Tissue Differentiation.

Cancer recurrence is an important issue in bladder tumor resections, because tissue cannot generously be removed from the thin bladder wall without impacting its functionality. Electrical impedance measurements during an operation aim to support the surgeon in making the decision which tissue areas to preserve, because physiological changes in tissue due to cancerous mutations can be detected by their altered electrical characteristics. This work investigates the detection limits of tetrapolar sensors when the impedance of heterogeneous tissue is measured. To do this, a finite element analysis is carried out where the sensors are placed on a dielectric medium with inclusions of different sizes, conductivity, and locations relative to the sensor. It is shown that a sensor with four electrodes in a square performs poorly in comparison to a sensor where the electrodes are symmetrically shaped as rings around one center electrode. This is mainly due to its enlarged regions of negative sensitivity. Based on the results, a third, optimized sensor geometry is proposed that shows superior performance to the other sensors in terms of geometry factor, sensitivities, and tumor detection. In simulation, it can reliably detect tumors with only half the radius of the sensor surface. Smaller tumor fractions cannot be detected by either sensor.

Mathematical modeling of different diaphragm geometries in MEMS pressure sensor

Abstract Micro pressure sensors generally employ diaphragm as the sensing element which converts the applied pressure into appropriate displacement. The displacement can be further used for sensing the actual physical phenomena viz., pressure or force with different transduction techniques. The transduction mechanism could be piezoresistive, capacitive, piezoelectric or resonant, etc. It is utmost essential to comprehend the behaviour of the structural element before its design with respect to transduction technique. The parameter optimization of diaphragm is the prime challenge in order to achieve the unsurpassed performance of the device. In this work, three of the diaphragm geometries namely a square diaphragm, a rectangular diaphragm and a circular diaphragm are considered for structural analysis. The displacement and stress analysis of the structures are carried out and investigated with the help of finite element-based simulation. The performed analysis will be useful in the selection of pressure sensor which provides optimized sensor geometry for required sensitivity, linearity, output range and area.

Optimal and fast sensor geometry design method for TDOA localisation systemswith placement constraints

The sensor geometry design problem of time difference of arrival (TDOA) localisation systems based on Cramer-Rao bound is studied. Sensor placement constraints are considered, which means the available placement area is limited. This makes sensor geometry design a sensor selection problem. In two-dimensional (2D) TDOA localisation or 3D TDOA localisation on the earth surface, sensor selection can be implemented through solving a fractional integer programming problem. However, traditional fractional integer programming methods are either suboptimal or too time consuming. For this reason, a new method named path-varying sphere decoding is proposed in two steps. In step one, the programming problem is relaxed into two sphere decoding (SD) subproblems. Solving these subproblems leads to the optimal solution, and the required computational complexity is much less than those of traditional optimal methods. In step two, the structure of the cost function is explored. This makes it possible to calculate the path-varying upper-bound of a quadratic function. Thus the quadratic function constraint used in one SD subproblem becomes tighter and the calculation speed is enhanced. Theory analyses and simulation results show that the proposed method is not only optimal but also much faster than traditional optimal methods when solving large-scale programming problems.

Optimal deployment of sensor–emitter geometries for hybrid localisation using TDOA and AOA measurements

The relative sensor–emitter geometry has a great impact on the localisation accuracy. In this study, optimal deployments of hybrid time-difference-of-arrival (TDOA) and angle-of-arrival (AOA) localisation are studied. The explicit expression of the determinant of the Fisher information matrix is acquired and chosen as the criterion to determine optimal sensor deployments with the assumption of Gaussian measurement noise. The analytic solutions of the optimal sensor geometries are derived in both general and co-located scenarios. Finally, the theoretical developments are corroborated by simulation examples of unmanned aerial vehicles path planning problem.

Optimal sensor placement for multi‐source AOA localisation with distance‐dependent noise model

In this study, the optimal sensor placement problem for multi-source angle of arrival localisation is investigated. The authors adopt the A-optimality criterion, maximising the trace of Fisher information matrix, to determine the optimal sensor-target geometry under distance-dependent Gaussian noise model. A recursive representation of the Cramer-Rao lower bound is derived to recast the sensor placement problem into a sequential method, obtaining the optimal sensor geometries in a step by step manner. Note that the state-of-the-art methods are highly sensitive to the source location changes such that they should be relocated by any later changes in target geometries, which is practically impossible in systems with stationary receivers (such as radars). The proposed sequential method can circumvent this drawback by changing only one flexible sensor position rather than the whole geometry of the sensors. Numerical simulations are included to corroborate the theoretical results and determine the cost of such flexibility and robustness on the localisation performance.

Multiobjective optimization for a plasmonic nanoslit array sensor using Kriging models.

We propose an efficient multiobjective optimization approach for a plasmonic nanoslit array sensor using Kriging surrogate models. The universal Kriging models whose regression functions are zeroth-, first-, and second-order polynomials are adopted to estimate objective functions. The multiobjective extension of the genetic algorithm is used for Pareto optimal sensor geometry. The objective functions are the figure of merit defined as a ratio of peak wavelength shift at molecular adsorption and 3 dB bandwidth of transmission spectrum, and peak transmission power, respectively. The optical properties of a plasmonic slit sensor are investigated, such as transmission power, bandwidth, and peak shift, using the finite element method.

Optimal TDOA Sensor-Pair Placement With Uncertainty in Source Location

Source localization based on time-difference-of-arrival (TDOA) measurements from spatially separated sensors is an important problem in sensor networks. In this paper, we study the optimal geometries of a team of networked sensors for the TDOA-based localization problem, taking into account the a priori uncertainty of the source location. Two types of TDOA sensor pairing methods, namely, centralized and decentralized , are considered. Analytic solutions to the optimal sensor geometries in the ideal case are derived in both static and movable source cases. In the movable source case, sensor geometry design is investigated under an extended Kalman filter (EKF) framework. Furthermore, under sensor motion and communication constraints, we extend this paper to an optimal sensor path planning problem. Simulations demonstrate its enhancement to the source localization and tracking performance.

Evaluation of accuracy and reliability of PulseOn optical heart rate monitoring device.

PulseOn is a wrist-worn optical heart rate (HR) monitor based on photoplethysmography. It utilizes multi-wavelength technology and optimized sensor geometry to monitor blood flow at different depths of skin tissue, and it dynamically adapts to an optimal measurement depth in different conditions. Movement artefacts are reduced by adaptive movement-cancellation algorithms and optimized mechanics, which stabilize the sensor-to-skin contact. In this paper, we evaluated the accuracy and reliability of PulseOn technology against ECG-derived HR in laboratory conditions during a wide range of physical activities and also during outdoor sports. In addition, we compared the performance to another on-the-shelf wrist-worn consumer product Mio LINK(®). The results showed PulseOn reliability (% of time with error <;10bpm) of 94.5% with accuracy (100% - mean absolute percentage error) 96.6% as compared to ECG (vs 86.6% and 94.4% for Mio LINK(®), correspondingly) during laboratory protocol. Similar or better reliability and accuracy was seen during normal outdoor sports activities. The results show that PulseOn provides reliability and accuracy similar to traditional chest strap ECG HR monitors during cardiovascular exercise.

Performance analysis based on least squares and extended Kalman filter for localization of static target in wireless sensor networks

Wireless sensor network localization is an essential problem that has attracted increasing attention due to wide demands such as in-door navigation, autonomous vehicle, intrusion detection, and so on. With the a priori knowledge of the positions of sensor nodes and their measurements to targets in the wireless sensor networks (WSNs), i.e. posterior knowledge, such as distance and angle measurements, it is possible to estimate the position of targets through different algorithms. In this contribution, two commonly-used approaches based on least-squares and Kalman filter are described and analyzed for localization of one static target in the WSNs with distance, angle, or both distance and angle measurements, respectively. Noting that the measurements of these sensors are generally noisy of certain degree, it is crucial and interesting to analyze how the accuracy of localization is affected by the sensor errors and the sensor network, which may help to provide guideline on choosing the specification of sensors and designing the sensor network. In addition, the problem of optimal sensor placement is also addressed to minimize the localization error. To this end, theoretical analysis have been made for the different methods based on three typical types of measurement noise: bounded noise, uniformly distributed noise, and Gaussian white noise. Simulation results illustrate the performance comparison of these different methods, the theoretical analysis and simulations and the optimal sensor geometry which may be meaningful and guideful in practice.

Pushing the Limits of Electrical Detection of Ultralow Flows in Nanofluidic Channels

This paper presents improvements in flow detection by electrical cross-correlation spectroscopy. This new technique detects molecular number fluctuations of electrochemically active analyte molecules as they are transported by liquid flow through a nanochannel. The fluctuations are used as a marker of liquid flow as their time of flight in between two consecutive transducers is determined, thereby allowing for the measurement of liquid flow rates in the picoliter-per-minute regime. Here we show an enhanced record-low sensitivity below 1 pL/min by capitalizing on improved electrical instrumentation, an optimized sensor geometry and a smaller channel cross section. We further discuss the impact of sensor geometry on the cross-correlation functions.

Optimizing the geometry of an in vitro tunneling magnetoresistance biosensor using an immobilized ferrimagnetic nanoparticle agent

Spatial interactions of the magnetic field produced by an immobilized ferrimagnetic nanoparticle agent on the sensing layer have been numerically calculated considering the longitudinal and transverse components of the magnetic field. Based on the calculations, the sensor geometry to obtain the maximum sensing performance of an in vitro exchange biased tunneling magnetoresistance (TMR) based biosensor can be determined prior to fabrication. Numerical analysis considering the one-dimensional (longitudinal) component of the magnetic field along the magnetization of the ferrimagnetic nanoparticle agent found that the geometrical parameters of the biosensor can be determined by simply considering the effective distance (δ). The effective distance (δ) is determined by the remnant magnetization and radius of the ferrimagnetic nanoparticle agent and the detectable field limit (BDL) relevant to the exchange bias field of the TMR sensor. The optimized sensor geometry in terms of the critical length (lc) and width (wc) is approximated as wc/lc≈3.14. For a more accurate optimization of the sensor geometry, the two-dimensional magnetic field distributions on the sensing surface are numerically analyzed by employing the astroid curve model (or Stoner–Wohlfarth model). According to the numerical results using this model, the effective sensing area is found to have been extended due to the transverse component of the magnetic field. The extended effective sensing area in the optimized sensor geometry resulting from the spatial field interaction caused by the two-dimensional magnetic field is expected to enhance the output signal of the in vitro TMR based biosensor. However, in considering the two-dimensional magnetic field distribution, the undetectable area formed in the vicinity of the center of the optimized sensing area due to the spatial field interaction is a problem in designing a more accurate sensor geometry. Making a TMR sensor with a larger exchange bias field and introducing an especially designed sensor structure with a magnetic shield with a high permeability are suggested as solutions to this technical problem.

A Time-domain Reflectometry Method for Automated Measurement of Crack Propagation in Composites during Mode I DCB Testing

This article describes a new technique for automated measurement of crack initiation, growth, and propagation in composite materials during mode I double cantilever beam (DCB) testing. The proposed method uses time-domain reflectometry (TDR) to detect changes in geometry and electromagnetic properties (dielectric or magnetic) along a transmission line that can be embedded in or bonded to the surface of the specimen. Two types of transmission line TDR sensors are evaluated (IM7 carbon fiber and ARACON) during DCB tests. A P-SPICE transmission-line simulation model is used to verify the baseline signal response for the DCB sensor and the sensitivity for crack detection, with good agreement. Comparison with standard visual methods in DCB testing showed excellent correlation in crack location, crack propagation (LC), and the interlaminar fracture toughness (GIC) values. The TDR sensor design and model-based parametric studies are carried out to determine optimal sensor geometry and configuration. The results demonstrate that the TDR-based method can measure crack propagation parameters at high resolution and accuracy, in an automated manner using low-cost sensors.

Optimal Cooperative Sensing using a Team of UAVs

The authors investigates the joint optimal estimation of both the position and velocity of a ground moving target (GMT) using pulse Doppler radars on-board unmanned aerial vehicles (UAVs). The problem of cooperative estimation using a UAV team and the optimization of the team's configuration to achieve optimal GMT position and velocity estimates are addressed. Based on the Cramer-Rao bound, the minimum achievable error variance of the GMT position and velocity estimates is derived. The expression of the minimum achievable estimation error variance for unbiased estimation provided by the Cramer-Rao bound is minimized yielding the optimal configuration of the UAV team. Our solution is complete in that it addresses various GMT tracking scenarios and an arbitrary number of UAVs. Optimal sensor geometries for typical applications are illustrated

Test beam results of geometry optimized hybrid pixel detectors

The Multi-Chip-Module-Deposited (MCM-D) technique has been used to build hybrid pixel detector assemblies. This paper summarises the results of an analysis of data obtained in a test beam campaign at CERN. Single-chip hybrids made of ATLAS pixel prototype read-out electronics and special sensor tiles were used. The sensors feature an optimized sensor geometry called “equal sized bricked”. In this document, the geometry of this type of hybrid is described and their performance in terms of spatial resolution and charge collection is shown.

Building pixel detector modules in multichip module deposited technology

The MCM-D technique has been used to build hybrid pixel detector modules and single chip assemblies with optimized sensor geometry. Results of test measurements in both a laboratory and a test beam environment are given. Proton irradiation to the ATLAS pixel lifetime of a MCM-D single chip assembly has been carried out

Improved ultrasonic density sensor with reduced diffraction influence

The density of liquids can be measured with ultrasonic techniques. An important factor limiting the accuracy of the measurement is the effect of diffraction of acoustic waves in the transducer. This paper presents a method to minimize such effects. A simple geometric model is used to analyse the contributions of plane waves and diffraction waves as a function of the sensor geometry. Calculations using the model lead to an optimized sensor geometry where diffraction waves do not interfere with the measurement signals. To demonstrate the fundamental improvement of accuracy, selected measurements of optimized and non-optimized sensors are compared.