Design Of Sensor Array Research Articles

Design and Optimization of Fiber Optic Grating Pressure Sensing Array

Aiming at the haptic problem of robot hand pressure, this paper studies a fiber grating tactile sensor array. Using polydimethylsiloxane (PDMS) package, and the finite element simulation of the package body to obtain internal strain. Aiming at the strain field distribution when the robot hand is gripping, the design of preliminary sensor array and finite element analysis are carried out, and the improved particle swarm algorithm is used to further optimize the measurement points. It provides a theoretical basis for the subsequent construction of the measurement points of the FBG tactile sensor array detection system.

The Complexity of Analytical Tasks: A Driving Force for Sensor Array Design

Introduction Analytical tasks, the detection goals and scoping of quantitative measurements, are a foundational consideration for the design of chemical sensor arrays. Often sensor arrays are expected to support a variety of possible tasks which are only vaguely defined by both the end user and array designer. Nonetheless, the expedient choices made by or forced upon array designers and fabricators can significantly impact the analytical tasks that could be supported by an array. Analytical tasks may be defined as boundaries in an abstract space defining chemically and practically relevant quantities that are to be measured; chemical sensor and instrument responses may be defined in related spaces specific to their physics. Intelligently finding a mapping between these two abstract spaces defines our problem and informs the impact of analytical tasks upon sensor array design while avoiding the “black box” problems of popular machine learning approaches. This presentation details recent work at NRL that probes the meaning and impact of analytical tasks on chemical sensor array design through general, device agnostic figures of merit developed via techniques originating in statistical mechanics, information theory, information retrieval, and theoretical computer science. Approaches Multiple theoretical approaches are explored to determine and measure the impact of analytical task on chemical sensor array design. To begin, we use Ising model-like descriptions of binary sensor responses first described in a biological context by Murugan et. al. [1-3] and subsequently developed by us for chemical sensing with environmental interferents. Analytical tasks are then scoped as molecular and concentration identification against an unknown chemical background. On this framework, we explore the capabilities of a variety of approaches towards assessing the impact of analytical task on sensor array design.The first set of approaches originate in information theory and we consider the mutual information and Kullback-Leibler divergences between different sensor array configurations considered over probability distributions defining the likelihood of that sensor array needing to perform different analytical tasks. Platform-agnostic figures of merit are derived and used to show the impact of analytical tasks on sensor array design.Next, Van Riijsbergen and Melucci [4,5] have developed and elaborated upon an approach to representing and searching information in Hilbert spaces using operator theoretic methods reminiscent to those used in quantum mechanical calculations. By transferring our probabilistic sensor array models to a Hilbert space, we are able to perform similar searches against sensor arrays and by operator methods to ask logical questions of the sensor array using operators that encode analytical tasks and specific exigencies like comparisons to the effectiveness of the array for other analytical tasks.Finally, Almagor et. al. [6-9] have recently developed a finite state machine approach for formally describing the complexity of sensing tasks within formal languages. We attempt to map our binary chemical sensor array model to this methodology and to describe the formal complexity of this toy model for chemical sensing. Analytical tasks are input as restrictions on available formal words in the finite state language. Conclusions Using a simple statistical mechanical model for a chemical sensor array with background interferents, we have been able to probe multiple approaches to quantifying the impact of analytical tasks on the design and relevance of a sensor array for a set of chemical detection problems. Importantly, these approaches avoid drawbacks associated with more empirical, machine learning-based approaches, enabling a more clear understanding of the chemical landscape and how chemical sensor array design interacts with it. Our investigation has explored ways to construct figures of merit for specific families of analytical tasks using methodologies as diverse as information theory, Hilbert space search methods, and complexity techniques for automata-defined sensing.

Sensor Array Complexity and Analytical Capability

Introduction Chemical sensors are generally associated with tasks involving detection of chemical stimuli while conventional analytical laboratory instruments are associated with analysis of chemical mixtures. While sensing tasks may not necessarily be perceived as involving chemical mixture analysis and evaluated as such, there is always an underlying analytical context in any sensing problem. This context becomes progressively more important as the complexity of chemical environment in which the sensing task is to be conducted increases, through both the number of potential intereferents as well as uncertainty regarding the composition of the chemical background.Design of individual sensors can be reasonably predicated on maximizing the sensitivity and selectivity of a narrowly-scoped chemical stimulus against expected backgrounds. However, as one may expect, design considerations can become significantly more complicated for systems based on sensor arrays. Arrays consisting of sensors that exhibit diverse, partially-selective response to a wide range of chemical stimuli allow, in theory, selective detection of a range of chemical stimuli against more complex backgrounds. [1] This is generally supported by analogy to biological olfaction as well as experimental work over the past few decades. Intuitively, a clear consequence of moving towards applications involving more complex and uncertain sensing tasks is a requirement that the system become less “sensor-like” and more “instrument-like.” A significant challenge in sensor array design has been that these concepts are not well-understood in a quantitative and device-agnostic fashion, making it difficult appropriately match sensor array and sensing task complexity in a predictable and systematic fashion. This presentation will discuss how analytical capabilities can be characterized on a continuum between simple sensor arrays and classical analytical instruments and the implications for sensor array design and selection. Approaches Sensor array capability for determining composition of fixed, finite domains of independent chemical stimuli can be expressed and predicted using information-theoretic measures and knowledge of individual chemical response functions for each chemical stimulus/sensor pair. [2-4] Moving towards expression of more general-purpose analytical capability (i.e. dynamic, high-dimensional domains of independent chemical stimuli) requires a shift from consideration of the span of specific chemical stimuli to one of the span of molecular parameters probed by the sensor technology. Sensor array capability for addressing generalized chemical analysis problems can then be expressed in terms of the adjacency in molecular parameter space induced by the underlying response mechanism of a sensor technology and the manner in which specific sensor array configurations induce particular array sensitivities across this same domain of molecular parameters. Hypothetical sensor systems based on optical detection and chemisorption are used to illustrate these concepts. In particular, this framework allows consideration of the manner in which sensor arrays and laboratory instruments occupy a continuum of complexity with which they interact with the chemical environment. Conclusions This work demonstrates the value in developing improved first-principles models of sensor response grounded in molecular parameters rather than specific chemical stimuli. Such an understanding allows expression and prediction of the extent to which a sensor array offers “instrument like” general purpose capability as opposed to narrowly scoped sensing tasks. The complexity of a given sensor technology’s interaction with the chemical environment ultimately provides a limit to the complexity of chemical sensing tasks that are addressable by sensor arrays based on this technology. Considering these aspects of chemical sensor array design allows more reliable selection and design of sensor arrays for complex, real-world applications.

Integration of Porphyrinoids Based Gas Sensor Arrays with Direct Injection Mass Spectrometry

Porphyrins have been used in the last two decades to prepare sensor arrays for a variety of applications [1]. The wide chemical interactivity of porphyrins sustains these applications making possible the measurement of complex patterns of volatile compounds. The interplay between the metal ion, the aromatic ring and the peripheral compounds establishes unique selectivity patterns which are fundamental elements for sensor array design and development. Porphyrin films can be adequately applied to inorganic surfaces making possible the preparation of different kinds of chemical sensors. Among them mass transducers, such as Quartz Microbalances (QMBs), have been found particularly suitable for several applications. QMBs do not select the different interaction mechanisms, thus this transducer offer the unique chance to investigate the whole bouquet of interactions established between the volatile compounds and the sensitive layers.The combination of the intrinsic porphyrins sensitivity and the properties of QMB transducer enables these sensor array to discriminate among subtle changes in patterns of volatile compounds produced in living organisms. In-vivo, these sensors have been applied to the measure of volatile compounds released by various body compartments such as skin, breath, and urines while in vitro they have been used to detect the volatile compounds of cell cultures.However, in spite of the positive results, gas sensor arrays are always characterized by the lack of information about the nature of the sensed compounds. So, without this information it may be complicated to provide convincing explanations about the origin of the achieved results. Furthermore, gas sensors results do not shed light into the physiological mechanisms that link the volatile compounds to the disease and its progression.This problem is well known in the field, and gas chromatography-mass spectrometry is the typical companion of sensor arrays investigation. However, these instruments cannot work on exactly the same samples. Consequently, due the lability of volatile compounds the comparison between instrument is far to be straightforward. On the other hand, several mass spectrometer techniques may work in real-time, and then they are virtually compatible to be operated in series to a gas sensor array enabling the simultaneous measurement of the very same samples. Among these techniques, Proton Transfer Reaction - Mass Spectrometer (PTR-MS) is the most suitable in terms of rapidity of measurements. In addition, the moderate molecular fragmentation results in a more friendly interpretation of the results [2]Recently, we applied the combination of porphyrins coated QMB sensor array and PTR-MS to the analysis of volatile compounds released by red blood cells infected by Plasmodium falciparum at different stage of development [3]. Plasmodium falciparum is the most lethal parasite related to malaria, a diseases that kill about half millions of people per year in the world. The gas sensor array identifies the cells infected with the transmissible stage of the parasite, as well the PTR-MS. The analysis of PTR-MS data shown that an exceptional emission of hexanal is observed in these cells. So at the same time sensors demonstrated the identification of infected cells, PTR-MS provided the explanation of the detection, and it provides the direction for the further optimization of sensors for the identification of malaria during the stages of maximum infectiveness.This study introduces a general methodology for sensors selection and optimization in order to improve the performance of gas sensor array in specific applications.

"Optical tentacle" of suspended polymer micro-rings on a multicore fiber facet for vapor sensing.

We designed a new type of gas sensor, an optical tentacle, made of highly integrated polymer micro-ring resonators in three-dimensional space on the tiny end-facet of a multicore optical fiber. Two pairs of three polymer micro-ring resonators were hung symmetrically on both sides of three suspended micro-waveguides as the sensing units. The micro-waveguides interlace to form a three-layer nested configuration, which makes the multicore optical fiber a "tentacle" for vapors of volatile organic compounds. Both experiments and theoretical simulation confirmed that the symmetrical coupling of multiple pairs of rings with the micro-waveguide had better resonance than the single ring setup. This is because the symmetrical light modes in the waveguides couple with the rings separately. All the optical micro-components were fabricated by the two-photon lithography technology on the end facet of multicore optical fiber. The optical tentacle shows good sensitivity and reversibility. This approach can also be adopted for sensor array design on a chip. Furthermore, optical sensors that can sense vapors with multiple constituents may be achieved in the future by adding selective sensitive materials to or on the surface of the rings.

Test-System for Bacteria Sensing Based on Peroxidase-Like Activity of Inkjet-Printed Magnetite Nanoparticles.

Rapid detection of bacterial contamination is an essential task in numerous medical and technical processes and one of the most rapidly developing areas of nano-based analytics. Here, we present a simple-to-use and special-equipment-free test-system for bacteria detection based on magnetite nanoparticle arrays. The system is based on peroxide oxidation of chromogenic substrate catalyzed by magnetite nanoparticles, and the process undergoes computer-aided visual analysis. The nanoparticles used had a pristine surface free of adsorbed molecules and demonstrated high catalytic activities up to 6585 U/mg. The catalytic process showed the Michaelis–Menten kinetic with Km valued 1.22 mmol/L and Vmax of 4.39 µmol/s. The nanoparticles synthesized were used for the creation of inkjet printing inks and the design of sensor arrays by soft lithography. The printed sensors require no special equipment for data reading and showed a linear response for the detection of model bacteria in the range of 104–108 colony-forming units (CFU) per milliliter with the detection limit of 3.2 × 103 CFU/mL.

Genetic Algorithm Design of MOF-based Gas Sensor Arrays for CO2-in-Air Sensing.

Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through over 1.125 × 1015 unique array combinations to identify optimal arrays for the detection of CO2 in air. We found that despite individual MOFs having lower selectivity for O2 or N2 relative to CO2, intelligently selecting the right combinations of MOFs enables accurate prediction of the concentrations of all components in the mixture (i.e., CO2, O2, N2). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we found that an array whose MOFs have diversity in their volumetric surface areas has improved sensing. Consistent with this observation, we found that the best arrays consistently had greater structural diversity (e.g., pore sizes, void fractions, and surface areas) than the worst arrays.

Wearable Sensor Array Design for Spine Posture Monitoring During Exercise Incorporating Biofeedback.

Physical therapy (PT) exercise is an evidence-based intervention for non-specific chronic low back pain, spinal deformities and poor posture. Home based PTprograms are aimed at strengthening core muscle groups, improving mobility and flexibility, and promoting proper posture. However, assessing unsupervised home-based PT outcomes is a generally difficult problem due to lack of reliable methods to monitor execution correctness and compliance. We propose a monitoring method consisting of a wearable sensor array to monitor three geodesic distances between two points on the surface of the shoulders and one point on the lower back. The sensor array may be built into a custom garment or a light weight harness wirelessly linked to a pattern recognition algorithm implemented in a mobile app. We use a new type of triangular stretch sensor array design which can generate a unique signature for a correct spine therapy exercise when performed by a specific subject. We conducted a pilot test consisting of three experiments: (i) two exercise patterns simulated by a mechanical device, (ii) one PT case of a scoliosis therapy exercise including spinal flexion, extension, and rotation performed by one volunteer patient, and (iii) a set of three lower back flexibility exercises performed by six subjects. Overall, the results of correctness recognition show 70-100% sensitivity and 100% specificity. The pilot test provides key data for further development including clinical trials. The significance of the method includes simplicity of design and training method, ability to test with simulated signals, and potential to provide real time biofeedback.

Low-Cost Subarrayed Sensor Array Design Strategy for IoT and Future 6G Applications

The low-cost sensor array, an effective way to provide sufficient degrees of freedom (DoFs) and affordable costs, is one of the most important foundation devices for large-scale applications of the Internet of Things (IoT) and sixth-generation (6G) spaceborne communication systems. In this article, we propose an irregular subarray-based low-cost sensor array design strategy, which decomposes the practical application problem into two subproblems according to the scene scale. For the small-size cases, a user-defined DoF control strategy is presented, which is flexibly defined and establishes an effective tradeoff between the sensor array radiation performance and manufacturing cost. Moreover, for large-size cases, a hierarchical subarray design strategy is presented. A module panel structure composed of subarray tiles is proposed to avoid both the convergence problem in high-dimensional optimization and engineering complexity. Both design strategies are able to ensure good sensor radiation performance while maintaining engineering convenience. The effectiveness and potential of the proposed method are verified by various numerical examples.

Design and optimization of sensor array for magnetic gradient tensor system

PurposeThe differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array. While the effects of measurement error, sensor performance and baseline distance on localization performance of such systems have been widely reported, the research about the effect of spatial design of sensor array is less presented. This paper aims to provide a spatial design method of sensor array and corresponding optimization strategy to localization based on magnetic tensor gradient to get the optimum design of the sensor array. Based on the results of simulation, magnetic localization systems constructed from the proposed array and the traditional array have been built to carry out a localization experiment. The results of experiment have verified the effectiveness of magnetic localization based on the proposed array.Design/methodology/approachThe authors focus on the localization of the magnetic target based on magnetic gradient by using three-axis magnetic sensor array and combine a design method with corresponding optimization strategy to get the optimum design of the sensor array.FindingsThis paper provides an array design and optimization method for magnetic target localization based on magnetic gradient to improve the localization performance.Originality/valueIn this paper, the authors focus on the magnetic localization based on magnetic gradient by using three-axis magnetic sensors and study the effect of the spatial design of sensor array on localization performance.

Superconducting receiver arrays for magnetic resonance imaging

Superconducting QUantum-Interference Devices (SQUIDs) make magnetic resonance imaging (MRI) possible in ultra-low microtesla-range magnetic fields. In this work, we investigate the design parameters affecting the signal and noise performance of SQUID-based sensors and multichannel magnetometers for MRI of the brain. Besides sensor intrinsics, various noise sources along with the size, geometry and number of superconducting detector coils are important factors affecting the image quality. We derive figures of merit based on optimal combination of multichannel data, analyze different sensor array designs, and provide tools for understanding the signal detection and the different noise mechanisms. The work forms a guide to making design decisions for both imaging- and sensor-oriented readers.

Development of a Column-Shaped Fluorometric Sensor Array and Its Application in Visual Discrimination of Alcohols from Vapor Phase.

Portable, miniaturized, and inexpensive detectors are in high demand for detecting and discriminating volatile organic compounds (VOCs). Sensor array design and exploitation are two key issues for new detector development. In contrast to the most reported plane-shaped sensor array for gaseous analyte sensing, here we report a column-shaped fluorometric sensor array by using fluorophore-loaded silica particles (∼40 μm) filled capillary. In the design, the capillary serves as test chamber and facilitates visualization. The orifices of the capillary were used as inlet and outlet for gaseous analyte. Sensing modules are installed in series, which lays foundation for their even and effective contact with the gaseous analyte. Meanwhile, further capsulation could be avoided. Silica particles were chosen as carries due to their preferred adsorption behavior to VOCs. By choosing four typical fluorophores (PBI-CB, Py-CB-Ph, Py-At, and NA-Ch) as sensing units, a 4-element fluorometric sensor array was achieved. Fluorescence of the array varied when different alcohol vapors were pumped in. The six tested alcohols could not only be distinguished as primary, secondary, or tertiary, but also be identified individually. The array had good reproducibility in visualization of the six alcohols. In addition, the orders of the fluorophores can be changed as desired. It is believed that the proofed concept provides not only a totally new design of sensor array but also contributes a new strategy for the discrimination of the alcohols as examined.

Urea-Functionalized Poly(ionic liquid) Photonic Spheres for Visual Identification of Explosives with a Smartphone.

Current effort merging rational design of colorimetric sensor array with portable and easy-to-use hand-held readers delivers an effective and convenient method for on-site detection and discrimination of explosives. However, on the one hand, there are rare relevant reports; on the other hand, some limitations regarding direct sensing, color retention, and array extendibility still remain. Herein, urea-functionalized poly(ionic liquid) photonic spheres were employed to construct a brand-new colorimetric sensor array for directly identifying five nitroaromatic explosives with a smartphone. It is found that the strong hydrogen bonding between the urea motifs and the nitro groups offers the spheres high affinity for binding the targets, whereas the existence of other abundant intermolecular interactions in poly(ionic liquid) units renders one single sphere eligible for prominent cross-responses to a broad range of analytes. Besides, in our case, opal-like photonic crystal structures other than chemical dyes are used to fabricate a new style of colorimetric array. Such structural colors can be vivid and unchanged over a long period even in hazard environments. Importantly, through simply altering the preparation conditions of our PIL spheres, a pool of sensing elements could be added to the developed array for discrimination of extended target systems such as more explosives and even their mixtures in real-world context.

Design, data analysis and measurement uncertainty evaluation of an eddy-current sensor array for in-process metrology of carbon fiber reinforced plastics

Composite lightweight components of carbon fiber reinforced plastics (CFRP) provide great potential for the automotive industry. Yet, due to their complexity, there is a high amount of quality defects in their production processes. Thus, this article deals with an approach for in-process metrology of CFRP components by means of an eddy-current sensor array. First, the development of the sensor design and its in-process integration is introduced. Second, a method for the quantitative evaluation of the measurement data as well as the measurement uncertainty is elaborated. Finally, a machine learning approach for the classification of crucial defects is shown.

Nonlinear scattering and mode conversion of Lamb waves at breathing cracks: An efficient numerical approach

This article presents an efficient numerical approach to the investigation of nonlinear scattering and mode conversion phenomena of Lamb waves as they interact with breathing cracks. A Local Interaction Simulation Approach (LISA) is adopted, which possesses the versatility to capture arbitrary damage profiles. The stick-slip contact dynamics is implemented in the LISA model via the penalty method, which captures the nonlinear interactions between Lamb waves and breathing cracks. The LISA framework achieves remarkable computational efficiency with its parallel implementation using Compute Unified Device Architecture (CUDA) executed on powerful GPUs. A small-size LISA model with absorbing boundaries is tailored for the purpose of extracting the Lamb wave scattering and mode conversion features. Due to the explicit parallel CUDA implementation and the small-size model setup, the computation is highly efficient. Numerical case studies on nonlinear scattering of Lamb waves from breathing cracks are given. Distinctive higher harmonic generation and selective mode conversion phenomena are presented using the complex-valued Wave Damage Interaction Coefficients (WDICs) containing both amplitude and phase information of the scattered wave field. The effect of oblique incident angle on nonlinear scattering phenomenon is investigated. The rough crack surface feature with initial openings and closures is also considered to better approximate fatigue cracks in practical engineering scenarios. In addition, the wave amplitude effect on the nonlinear scattering and mode conversion is studied. This research may provide guidelines for the effective design of sensor arrays utilizing nonlinear Lamb waves for fatigue crack detection.

The Optoelectronic Nose: Colorimetric and Fluorometric Sensor Arrays.

A comprehensive review on the development and state of the art of colorimetric and fluorometric sensor arrays is presented. Chemical sensing aims to detect subtle changes in the chemical environment by transforming relevant chemical or physical properties of molecular or ionic species (i.e., analytes) into an analytically useful output. Optical arrays based on chemoresponsive colorants (dyes and nanoporous pigments) probe the chemical reactivity of analytes, rather than their physical properties (e.g., mass). The chemical specificity of the olfactory system does not come from specific receptors for specific analytes (e.g., the traditional lock-and-key model of substrate-enzyme interactions), but rather olfaction makes use of pattern recognition of the combined response of several hundred olfactory receptors. In a similar fashion, arrays of chemoresponsive colorants provide high-dimensional data from the color or fluorescence changes of the dyes in these arrays as they are exposed to analytes. This provides chemical sensing with high sensitivity (often down to parts per billion levels), impressive discrimination among very similar analytes, and exquisite fingerprinting of extremely similar mixtures over a wide range of analyte types, in both the gas and liquid phases. Design of both sensor arrays and instrumentation for their analysis are discussed. In addition, the various chemometric and statistical analyses of high-dimensional data (including hierarchical cluster analysis (HCA), principal component analysis (PCA), linear discriminant analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs)) are presented and critiqued in reference to their use in chemical sensing. A variety of applications are also discussed, including personal dosimetry of toxic industrial chemical, detection of explosives or accelerants, quality control of foods and beverages, biosensing intracellularly, identification of bacteria and fungi, and detection of cancer and disease biomarkers.

Design of PVDF sensor array for determining airflow direction and velocity

An airflow sensor comprised of an array of piezoelectric polyvinylidene fluoride (PVDF) cantilever sensors mounted on a sensor ring is fabricated. A fluid-solid-electric coupling model based on the finite element method is presented to obtain the mathematical relationship between the normal airflow velocity and the response voltage. According to the response voltages from all pieces of PVDF cantilevers in the array, the values of the airflow direction and the velocity can be calculated. Furthermore, to find a suitable algorithm for error calculations and to achieve high accuracy, a method of reducing the flow angle error ( ) and flow velocity error ( ) by extracting Um of the effective cantilevers can be established. The experimental results show that the maximum value of is 1.2° (at 270° with 11.1 m/s) and the minimum value of is 0.3° (at 135° with 11.1 m/s) based on the PVDF sensor array with eight cantilevers. Meanwhile, the maximum value of is 3% (at 315° with 11.1 m/s), and the minimum value of is 1.5% (at 360° with 11.1 m/s). In addition, under 20 random airflow angles at 8 m/s, the error range in airflow velocity is from 1.27% to 2.67%, the error range in airflow angle is from 0.34° to 1.24°, and the response time is 20 ms. Therefore, the proposed design for an airflow sensory ring array can be used to determine the airflow direction and velocity, and the airflow sensor can be miniaturized as a bionic antennae, which is mounted on the skin of a piezoelectric autonomous mobile robot for sensing and escaping from an attack of the natural enemy.

Modeling and Analytical Studies of an Ink-Jet Printed Sensing Framework for Measuring Sub-Soil Nitrate

We have demonstrated ink-jet printed IoT sensor sheets capable of quantifying long-term soil nitrate concentration as shown in Fig. 1a. However, our previous data indicates that long routing lines contribute to lossy signals, noise, and electrode cross-talk within the sensor sheet. Using this current version of the sensor sheets leads to following challenges: 1) unbalanced sensing performance at different locations. Sensors further away from the power source report over 45% signal loss and large sensing current variation due to the unbalanced signal routing as seen in Fig. 1b. 2) Noisy sensing incurred by the signal crosstalk. As Fig.1c shows, the interference from neighboring sensors can largely deviate the sensing of a target sensor. 3) Inaccurate sub-soil nitrate analysis because of imperfect sensing results from failed or noisy sensors. This paper focuses on the development of mathematical models to quantify the sensing crosstalk among targeted sensors and other idle sensors for high-accurate sensing, and use the models as guides for robust VLSI sensor array design and routing architecture. A unique aspect of our approach is to use sensor cross-talk as a technique to identify sensor failures. The simple linear and nonlinear models, as well as advanced 2pi model used to measure the coupling between victim and aggressor nets in VLSI physical design, will be implemented on the ink-jet printed sensor sheets. Figure 1

Developing a graphical package for sensor arrays design, optimization and maintenance

The sensor arrays are now an essential part of any communication, medical and remote sensing system. These arrays should be designed with utmost performance to ensure the maximum link efficiency. The available commercial sensor arrays design packages are expensive, complicated and cannot be easily modified to accommodate the users’ needs. This work suggests a solution that is to design an open source specialized application to serve the ever-changing needs of the users. This package is called Sensor Array Design and Optimization (SADO) and it is developed to allow the unexperienced users and the researchers to design, test and optimize their sensor arrays using effi-cient optimization algorithms. The optimization is trying to reduce the sidelobe levels to reduce the interference. The application is simple and friendly to use, with professional graphical results. The predesigned arrays configurations supplied with this package are uniform and random arrays. The built in optimization algorithms are: Artificial Bee Colony (ABC), Biogeography-Based Optimization (BBO) and Teaching Learning Based Optimization (TLBO). The results for various designs and optimization results are also given and compared to indicate the best settings for the user. Some optimization ratios might reach about 50% that represent -3dB reduction in sidelobe level.

Aerial Drones with Location-Sensitive Ears

Micro aerial vehicles (MAVs), an emerging class of aerial drones, are fast turning into a high value mobile sensing platform for applications across sectors ranging from industrial to humanitarian. While MAVs have a large sensory gamut at their disposal; vision continues to dominate the external sensing scene, which has limited usability in scenarios that offer non-visual clues such as auditory. Therefore, we endeavor to provision a MAV auditory system (i.e., ears); and as part of this goal, our preliminary aim is to develop a robust acoustic localization system for detecting sound sources in the physical space of interest. However, devising this capability is extremely challenging due to strong ego-noise from the MAV propeller units, which is both wideband and non-stationary. It is well known that beamformers with large sensor arrays can overcome high noise levels; but in an attempt to cater to the platform (i.e., space, payload, and computation) constraints of a MAV, we propose DroneEARS: a binaural sensing system for geo-locating sound sources. It combines the benefits of sparse (two elements) sensor array design (for meeting the platform constraints), and our proposed mobility induced beamforming based on intra-band and inter-measurement beam fusion (for overcoming the severe ego-noise and its other complex characteristics) to significantly enhance the received signal-to-noise ratio (SNR). We demonstrate the efficacy of DroneEARS, in terms of SNR improvement obtained over many widely used techniques, by empirical evaluations.