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Infrared (IR) sensors are widely used in various applications due to their ability to detect infrared radiation. Currently, infrared detector technology is in its third generation and faces enormous challenges. IR radiation propagation is categorized into distinct transmission windows with the most intriguing aspects of thermal imaging being mid-wave infrared (MWIR) and long-wave infrared (LWIR). Infrared detectors for thermal imaging have many uses in industrial applications, security, search and rescue, surveillance, medical, research, meteorology, climatology, and astronomy. Presently, high-performance infrared imaging technology mostly relies on epitaxially grown structures of the small-bandgap bulk alloy mercury–cadmium–telluride (MCT), indium antimonide (InSb), and GaAs-based quantum well infrared photodetectors (QWIPs), contingent upon the application and wavelength range. Nanostructures and nanomaterials exhibiting appropriate electrical and mechanical properties including two-dimensional materials, graphene, quantum dots (QDs), quantum dot in well (DWELL), and colloidal quantum dot (CQD) will significantly enhance the electronic characteristics of infrared photodetectors, transition metal dichalcogenides, and metal oxides, which are garnering heightened interest. The present manuscript gives an overview of IR sensors, their types, materials commonly used in them, and examples of related applications. Finally, a summary of the manuscript and an outlook on prospects are given. [ABSTRACT FROM AUTHOR]

The current study investigates the structure and electronic properties of the FeO-decorated polythiophene-poly 3-methylpyrrole composite copolymer using a density functional theory-based ab initio approach. The synthesized composite copolymer has a high stability with a binding energy of -5.2 eV compared to polythiophene-poly 3-methylpyrrole copolymer. The synthesized copolymer exhibits a 58.5% reduction in bandgap due to the formation of composite with FeO, and increased conductivity. The structural and electronic properties of FeO decorated polythiophene-poly 3-methylpyrrole composite copolymer are analysed via electron density, density of states, HOMO-LUMO gap, and Mulliken population. The enhanced conductivity and stability of the FeO-decorated composite copolymer make it a promising candidate for various applications, including polymeric sensors, energy storage, photovoltaic and electronics devices. [ABSTRACT FROM AUTHOR]

Wireless Body Sensor Network (BSN) comprises wearables with different sensing, processing, storing, and broadcast abilities. Once several devices acquire the data, multi-sensor fusion was needed for transforming erroneous sensor information into maximum quality fused data. Deep learning (DL) approaches are utilized in different application domains comprising e-health for applications like activity detection, and disease forecast. In recent times, it can be demonstrated that the accuracy of classification techniques is enhanced by the combination of feature selection (FS) approaches. This article develops a Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning (MDFMDC-GTODL) algorithm. The proposed MDFMDC-GTODL method enables collection of various daily activity data using different sensors, which are then fused to produce highquality activity data. In addition, the MDFMDC-GTODL technique applies optimal attention based bidirectional long short term memory (ABLSTM) for heart disease prediction. In this study, Gorilla Troops Optimization Algorithm based FS (GTOA-FS) technique is involved to improve the classification performance. The simulation outcome of the MDFMDC-GTODL technique are validated and the results are investigated in different prospects. A wide-ranging simulation analysis stated the better performance of the MDFMDC-GTODL method over other compared approaches. [ABSTRACT FROM AUTHOR]

The development and integration of the Internet of Things (IoT) sensor technology across various domains have significantly transformed our work and daily lives, enriching society. However, the increase in the number of IoT devices leads to electronic waste (e-waste), which is a growing global concern. The continued development of sustainable IoT sensors utilizing biodegradable and renewable materials will not only help with reducing e-waste but also ensure wider adaptation of sensing applications thereby benefitting the global community. This review article examines the use of biodegradable and renewable materials in developing antennas for various sensing applications, emphasizing their sustainability, biodegradability, and recyclability. The main contributions of our work are six-fold. First, we review common bio-based materials used in microwave components, detailing the selection process for biodegradable and renewable materials, as well as their comparative advantages and limitations. Second, we examine biodegradable and renewable materials in antenna technologies for sensing applications, providing a comparative analysis based on microwave component type, material properties, dielectric constant, measurement method, relative permittivity, and relevant applications. Third, we analyze design requirements for antennas utilizing these materials, comparing antenna design type/technique, substrate and conductive materials, operating frequency band, size, and gain/directivity. Fourth, we evaluate antenna fabrication techniques, discussing their advantages and challenges. Fifth, we comprehensively review applications of biodegradable and renewable antennas in green IoT sensors, with focus areas including agriculture, environmental monitoring, healthcare, wearable electronics, logistics, and food processing. Finally, we address key research challenges, future prospects, and the potential of these technologies moving forward.

Electrospinning (ES) methods that can produce piezoelectricity in polymer nanofibers have attracted tremendous research attention. These electrospun polymer nanofibers can be employed for sensors, energy harvesting, tissue engineering, and filtration applications. This paper reviews the performance of a variety of electrospun piezoelectric polymer nanofibers produced by different ES methods, including near‐field electrospinning and conventional far‐field electrospinning methods. Herein, it is described how the ES method can affect the piezoelectric properties of various polymer nanofibers, including poly(vinylidene difluorine), poly(vinylidene fluoride‐trifluoroethylene), nylon 11, poly(l‐lactic acid), and poly(α‐benzyl‐l‐glutamate). Due to the varied matrix structures of piezoelectric polymer nanofibers, the ES method may conduct variable effects on the piezoelectric properties of polymer nanofibers. After characterizations by X‐ray diffraction, Fourier transform infrared spectrum, dielectric spectra, and piezoelectric coefficient measurements, it is found that the piezoelectric properties of the polymer nanofibers can be significantly affected by the ES parameters. Most of previous review articles focus on the output performance of electrospun polymer nanofibers. A detailed description of how different ES methods affect the piezoelectricity of polymer nanofibers is still lacking. In this review paper, the basic principle behind ES methods and the way in which different ES methods affect the properties of polymer nanofibers are examined. [ABSTRACT FROM AUTHOR]

Recently, flexible pressure and strain sensors have attracted the attention of researchers because of their high sensitivity, broad strain-sensing ability, and various forms. Flexible sensors have essential applications and broad market prospects in fields such as wearable electronics, intelligent machines, and structural health monitoring. At the same time, these emerging fields also require more significant performance requirements for flexible sensors. Conductive rubber composite materials have high tensile strength, high electromechanical sensitivity, and high stability, making them ideal for fabricating of high-performance flexible pressure sensors. Therefore, further improving the performance of conductive flexible rubber composite pressure sensors is developmental focus. In this review, the preparation and electromechanical response mechanisms of conductive polymer composites are summarized, and methods for improving the performance of flexible sensors through structural design are introduced, including conductive network structural design, substrate structural design, and conductive polymer composite structural design. In addition, the main applications of flexible pressure sensors are introduced. Finally, problems in developing flexible sensors are summarized, and future development directions are discussed. [ABSTRACT FROM AUTHOR]

In the realm of sensing technologies, the appeal of sensors lies in their exceptional detection ability, high selectivity, sensitivity, cost-effectiveness, and minimal sample usage. Notably, molecularly imprinted polymer (MIP)-based sensors have emerged as focal points of interest spanning from clinical to environmental applications. These sensors offer a promising avenue for rapid, selective, reusable, and real-time screening of diverse molecules. The preparation technologies employed in crafting various polymer formats, ranging from microparticles to nanomaterials, wield a profound influence. These techniques significantly impact the assembly of simplified sensing systems, showcasing remarkable compatibility with other technologies. Moreover, they are poised to play a pivotal role in the realization of next-generation platforms, streamlining the fabrication of sensing systems tailored for diverse objectives. This review serves as a comprehensive exploration, offering concise insights into sensors, the molecular imprinting method, and the burgeoning domain of MIP-based sensors along with their applications. Delving into recent progress, this review provides a detailed summary of advances in imprinted-particle- and gel-based sensors, illuminating the creation of novel sensing systems. Additionally, a thorough examination of the distinctive properties of various types of MIP-based sensors across different applications enriches the understanding of their versatility. In the concluding sections, this review highlights the most recent experiments from cutting-edge studies on MIP-based sensors targeting various molecules. By encapsulating the current state of research, this review acts as a valuable resource, offering a snapshot of the dynamic landscape of MIP-based sensor development and its potential impact on diverse scientific and technological domains. [ABSTRACT FROM AUTHOR]

Herein, we report for the first time cost-effective synthesis of lanthanum doped cobalt-iron ferrite composite sensors towards the detection of liquified petroleum gas (LPG) at room temperature. The synthesized ferrite composites were characterized by different analytical techniques such as scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and ultraviolet–visible (UV–Vis) spectroscopy methods to analyse the surface morphology, elemental composition, functional groups, structural and optical properties. The synthesized ferrite composite exhibit improved ac conductivity and dielectric attributes due to the doping of rare earth (RE) lanthanum NPs into the cobalt-iron ferrites. Doping of lanthanum nanoparticle (NPs) into cobalt-iron ferrites result into porous surface with interconnected network which support for easy transportation of charge carriers into neighbouring sites. The gas sensing response of the lanthanum doped cobalt-iron ferrites was carried out at room temperature. The gas sensing properties of the synthesized CoFe 2-x La x O 4 samples were studied for LPG, ethane, propane, butane, methane, and petrol vapours of variable hydrocarbon gas species. The CoFe 2-x La x O 4 composite shows superior gas response towards LPG gas at room temperature. The gas response of CoFe 2-x La x O 4 (x -0.1) composite sample was measured to be nearly 98% for LPG with enhanced response and recovery times. Furthermore, the long-term stability of these gas sensor towards LPG indicates that, that among the synthesized samples x - 0.1 composite samples show stable performance even after 60 days of operation. Owing to improved electrical conductivity, gas sensing behaviour and quick response and recovery times at room temperature, these CoFe 2-x La x O 4 composites could be employed as suitable candidate towards fabrication of room temperature gas sensors especially for the detection of LPG at low concentration (ppb level). [ABSTRACT FROM AUTHOR]

Organic triboluminescent (TL) crystals have attracted significant attention in recent years for their potential applications in various fields such as damage sensing in aerospace structures, cyanide detection in water, and radiation sensing. Europium tetrakis dibenzoylmethide triethylammonium (EuD4TEA) is a class of organic TL crystals that have shown promising results in terms of sensitivity due to their high TL intensity, however, they exhibit thermal instability and fragility concerns. In this review, recent research progress on the development, characterizations, TL properties, and potential applications of EuD4TEA crystals in various fields, are summarized. The review will conclude with the current challenges of EuD4TEA crystals, such as fragility nature, thermal instability, and increasing cost of europium. Despite having challenges, EuD4TEA crystals have the potential for damage sensing in the structural composite. [ABSTRACT FROM AUTHOR]

This work proposes a new paradigm for contactless vital sign sensing that adopts an integrated ultrawideband (UWB) radar and hybrid optical wireless communication (OWC) technologies. The proposed system is composed of a sensor node and a receiver node. The sensor node is responsible for detecting continuous vital sign signals and then transmitting these signals via the hybrid OWC transmitters, which are composed of a visible light communication transmitter and an infrared transmitter. The contactless UWB radar adopts low intermedium frequency demodulation rather than the conventional direct down conversion topology to avoid severe direct current (DC) offset issue. The advantages of this merged configuration do not only help reduce radar complexity but also provide the required modulated signal with vital signs for the subsequent OWC system. Moreover, the receiver node has an OWC receiver module for receiving vital signs that utilizes a photodiode-based receiver with an analog filter and digital signal processing mechanisms. The proposed architecture exhibits the potential to reduce radar complexity and provide new solutions to replace the conventional contact sensing system used in hospitals or nursing homes. [ABSTRACT FROM AUTHOR]

Dielectric elastomer sensors (DESs) have been known as highly stretchable strain sensors for about two decades. They are composite films consisting of alternating dielectric and electrode layers. Their electrical capacitance between the electrodes is enhanced upon stretching. In this paper, a variety of advanced designs of DESs is introduced. An explanation of how these sensors work and how they perform in terms of capacitance versus deformation or load force is provided. Moreover, the paper describes how the sensor design affects the sensor characteristics in order to achieve a high measuring sensitivity. The most relevant quantities to be measured are distance variations or elongations, forces and pressure loads. It is demonstrated that the sensor design can be supported by Finite Element Method (FEM) simulations. In the second part of the paper, possible applications of the advanced DESs are outlined. Pure sensor applications to detect or monitor pressure or deformation are distinguished from other applications, where sensors form a part of a human–machine interface (HMI). DESs are predestined to be used in contact with the human body due to their softness and flexibility. In the case of an HMI, a dosed load on a sensor by the user's hand enables the remote control of arbitrary technical functions. This can preferably be realized with an operating glove, which exhibits different categories of DESs. Possible applications of DESs are described with the support of functional demonstrators. [ABSTRACT FROM AUTHOR]

Polyethylene terephthalate (PET) thermoplastic polyester is durable, formable material that is widely used to manufacture consumer products like sailcloth, sailing spinnakers, food-grade containers, etc. for commercial and engineering applications. The recycling of PET is still a challenge because of its abundance, especially in low-income/developing countries. The present study reports the recycling of PET by utilizing the primary (1°) recycled PET (R-PET) for 3D printing-based sensor applications with the idea of converting waste to wealth. The investigations were performed on PET-based waste collected from institute campus canteens (in form of used food containers/soft drink bottles) after ascertaining their rheological, mechanical, morphological, bonding, and sensing capabilities. The sensing capabilities of R-PET were explored by performing a ring resonator test of a 3D-printed substrate using a vector network analyzer (VNA). The result of the study outlined that R-PET-based sensors may be used in sailcloth, and sailing spinnakers to monitor the location of boats in a shipyard/dock. [ABSTRACT FROM AUTHOR]

The resolution of a quartz crystal microbalance (QCM) is particularly crucial for gas sensor applications where low concentrations are detected. This resolution can be improved by increasing the effective surface of QCM electrodes and, thereby, enhancing their sensitivity. For this purpose, various researchers have investigated the use of micro-structured materials with promising results. Herein, we propose the use of easy-to-manufacture metal blacks that are highly structured even on a nanoscale level and thus provide more bonding sites for gas analytes. Two different black metals with thicknesses of 280 nm, black aluminum (B-Al) and black gold (B-Au), were deposited onto the sensor surface to improve the sensitivity following the Sauerbrey equation. Both layers present a high surface roughness due to their cauliflower morphology structure. A high response (i.e., resonant frequency shift) of these QCM sensors coated with a black metal layer was obtained. Two gaseous analytes, H2O vapor and EtOH vapor, at different concentrations, are tested, and a distinct improvement of sensitivity is observed for the QCM sensors coated with a black metal layer compared to the blank ones, without strong side effects on resonance frequency stability or mechanical quality factor. An approximately 10 times higher sensitivity to EtOH gas is reported for the QCM coated with a black gold layer compared to the blank QCM sensor. [ABSTRACT FROM AUTHOR]

Available wearable dosimeters suffer from spectral mismatch during the measurement of broadband UV and visible radiation in environments that receive radiation from multiple sources emitting differing spectra. We observed this type of multi-spectra environment in all five Washington State cannabis farms visited during a field study investigating worker exposure to ultraviolet radiation in 2018. Spectroradiometers do not suffer from spectral mismatch in these environments, however, an extensive literature review conducted at the time of writing did not identify any spectroradiometers that were directly deployable as wearable dosimetry devices. To close this research gap, we developed a microcontroller system and platform that allows for researchers to mount and deploy the Ocean Insight Flame-S Spectroradiometer as a wearable device for measurement of UV and visible wavelengths (300 to 700 nm). The platform validation consisted of comparing measurements taken under platform control with measurements taken with the spectrometer controlled by a personal computer running the software provided by the spectroradiometer manufacturer. Three Mann–Whitney U-Tests (two-tailed, 95% CI), one for each intensity condition, compared the central tendency between the total spectral power (TSP), the integral of a spectrum measurement, measured under both control schemas. An additional analysis of per pixel agreement and overall platform stability was performed. The three Mann–Whitney tests returned no significant difference between the set of TSPs for each filter condition. These results suggest that the spectroradiometer takes measurements of equivalent accuracy under both control schemas, and can be deployed as a wearable device for the measurement of wavelength resolved UV and visible radiation. [ABSTRACT FROM AUTHOR]

Electronics and soft robotics demand the development of a new generation of hybrid materials featuring novel properties. Among these, remarkable mechanical properties are required to sustain mechanical stresses, and electrical and magnetic properties are essential to design the devices' interface. In this study, a hybrid material is presented, consisting of a spider silk thread, providing mechanical robustness, coated with a layer of a magnetostrictive FeCo alloy, which ensures both electrical conductivity and stress‐sensitive magnetic properties. The durability and the homogeneity of the composite are validated, as well as its ability to respond to magnetic and mechanical stimuli. Despite the coating, the soft nature of the silk and its mechanical performances are preserved. The magnetic study reveals that the magnetic behavior of the film is strongly affected by the silk thread–FeCo layer interaction, especially under mechanical stresses. Indeed, when the composite is subjected to tensile strain, the magnetic signal changes accordingly, indicating that the layer–silk interaction is maintained and can be exploited to reveal the tensional state of the sample even under severe cycles. Therefore, the presented hybrid material is a flexible fiber with properties that are suitable for magneto‐electronics applications, e.g., magnetic actuators as well as strain/stress sensors. [ABSTRACT FROM AUTHOR]

Nanocrystalline graphite (NCG) layers represent a good alternative to graphene for the development of various applications, using large area, complementary metal-oxide semiconductor (CMOS) compatible technologies. A comprehensive analysis of the physical properties of NCG layers—grown for different time periods via plasma-enhanced chemical vapour deposition (PECVD)—was conducted. The correlation between measured properties (thickness, optical constants, Raman response, electrical performance, and surface morphology) and growth time was established to further develop various functional structures. All thin films show an increased grain size and improved crystalline structure, with better electrical properties, as the plasma growth time is increased. Moreover, the spectroscopic ellipsometry investigations of their thickness and optical constants, together with the surface roughness extracted from the atomic force microscopy examinations and the electrical properties resulting from Hall measurements, point out the transition from nucleation to three-dimensional growth in the PECVD process around the five-minute mark. [ABSTRACT FROM AUTHOR]

This article presents a highly digital robust voltage-controlled oscillator (VCO)-based front end for multiplexed single-ended resistive sensor readout applications. The architecture features a modified digital phase-locked loop (DPLL) structure that enables second-order noise shaping without any operational transconductance amplifiers (OTAs) and a single feedback digital-to-analog converter (DAC). The direct conversion of the sensor input resistance to time-domain information obviates the need for any conditioning circuit, thus resulting in a highly digital and area-compact architecture. The closed loop substantially reduces the amplitude of the signal at the VCO input, thereby achieving high linearity. To ensure high robustness against supply and temperature variations, a double measurement approach is employed. Fabricated in a 180-nm CMOS process with a 0.1-mm2 active area, the sensor readout consumes 109 $\mu \text{W}$ of power and achieves a resolution of 96.9 dB for 0.5-ms conversion time, resulting in a Schreier figure of merit (FoM) of 166.5 dB. The circuit only requires a cheap and efficient single-point trimming calibration at room temperature. The low conversion time enables multiplexing among multiple sensor readouts. The supply and temperature sensitivities are 0.4%/V and 36 ppm/°C, respectively. [ABSTRACT FROM AUTHOR]

Highly stable zinc-based luminescent metal-organic framework (LMOF) [(Zn2-(NDC)2(bpy)·Gx) where NDC-2,6-naphthalene dicarboxylic acid, bpy - 4,4' bipyridine, and G - guest solvent molecules] is synthesized via solvothermal method at different synthesizing temperatures (120, 150 and 180∘C) for the detection of nitro-aromatics. Sample structure and presence of ligands is confirmed by X-ray diffraction and FTIR measurements, respectively. The photoluminescence properties of synthesized LMOF samples show the strong emission intensity in ethanol at 376 nm when it is excited at 240 nm. The detection limit is calculated using fluorescence quenching for the various nitro-analytes (2,4-DNP, 2-Nitrotoulene, 4-Nitrophenol, and Nitrobenzene) revealed that it is least for the sample synthesized at 150∘C with the maximum percentage in fluorescence decay. The thermal stability of as-synthesized samples is analysed using thermogravimetry analysis (TGA) which reveals that sample synthesized at 150∘C is the most stable sample. The TGA analysis also shows that on further heating from 526∘C to 980∘C decomposition of zinc metal and formation of zinc oxide takes place. FE-SEM confirms the cuboidal morphology with an average particle size of 12.62 nm, corroborated by the TEM analysis. The fluorescence quenching mechanism is explained on the basis of dipole-dipole and π-stacked interactions between the MOF and nitro-aromatics that play a prominent role in the sensing of nitro-analytes. Synthesized MOF samples show excellent recyclability in fluorescence quenching even after the fourth cycle, i.e. more than 95% and is sensitive to detect nitro-aromatics even upto 0.284 µM, and the results can generalize the explosive sensor technology and sensing various nitro-based pollutants.

Diagonal loading is one of the most widely used and effective methods to improve the robustness of both adaptive beamformers and Direction of Arrival (DOA) estimation due to the involvement of the sensor received covariance matrix. In addition, subspace-based DOA estimation techniques rely on multiple snapshots to achieve high estimation accuracies. This paper presents the study of a modified diagonally loaded sample covariance matrix for accurate DOA estimation in adverse scenarios. The proposed and novel technique deciphers poor DOA estimation in a low SNR environment by computationally changing the received sample covariance matrix. Our method is computationally simple as it does not require peak searching and does not depend on the coherency of the signal. The efficacy of the proposed method is examined via computer simulation for various sensor array sizes and the number of snapshot samples. Based on our numerical simulation results, our proposed method generally outperforms most state-of-the-art DOA estimators. In a finite number of snapshots and a single signal source, our proposed method performs 9.5% better than the state-of-the-art DOA estimation technique, 2.8% in multiple signal sources, and 8.5% in a single snapshot, single signal source environment of gained DOA estimation performance. [ABSTRACT FROM AUTHOR]

Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors. This raises many challenges including how to estimate the location of events sensed by the smart dusts. Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces. The study proposes a novel machine learning based localization algorithm for estimating the location of events. The approach utilizes timestamps (time of arrival) of sensed events received at base stations by assembling them into a multi-dimensional vector and input to a machine learning classifier for estimating the location. Due to the unavailability of real smart dusts, we built a simulator for analysing the accuracy of the proposed approach for monitoring forest fire. The experiments on the simulator show reasonable accuracy of the approach. [ABSTRACT FROM AUTHOR]

Recently there has been a significant increase in demand for wireless sensor networks, as their field of application is rapidly expanding, driven mostly by the growing importance and prevalence of the Internet of Things concept. To enable practical application of wireless sensor networks, sensor nodes must be inexpensive, small, light-weight, intelligent and autonomous. This paper presents a review of low-power always-on wake-up detectors used to reduce a sensor node’s power consumption and enable continuous detection of sparse events. We describe the wake-up concept, discuss its advantages and present the wake-up detector’s power consumption, detection accuracy and false positive rate as parameters of interest. We present a state-of-the-art analysis of wake-up detectors, grouping them based on their power consumption into zero-power and near-zero-power detectors and active detectors, which we further separate based on their implementation into digital detectors and mixed-signal and analog detectors. This analysis shows state-of-the-art wake-up detectors operate with detection accuracy over 90% and a wide range of power consumptions, spanning from a few nW to few tens of $\mu \text{W}$ , because of their diverse applications and sensor modalities. Additionally, it shows that active mixed-signal wake-up detectors are currently the most utilized implementation, with emphasis on acoustic transducer modality. It also shows potential trends for future detector design utilizing more MEMS and NEMS to further lower detector power consumption. We also show techniques and ideas not yet integrated in the wake-up concept, to potentially improve the wake-up detector concept and present the wake-up detectors’ common applications and their specifics. [ABSTRACT FROM AUTHOR]

The use of capacitive sensors has advantages in different industrial applications due to their low cost and low-temperature dependence. In this sense, the current-mode approach by means of second-generation current conveyors (CCIIs) allows for improvements in key features, such as sensitivity and resolution. In this paper, a novel architecture of CCII for differential capacitive sensor interfaces is presented. The proposed topology shows a closed-loop configuration for both the voltage and the current buffer, thus leading to better interface impedances at terminals X and Z. Moreover, a low power consumption of 600 µW was obtained due to class-AB biasing of both buffers, and the inherent drawbacks in terms of linearity under the mismatch of class-AB buffering are overcome by its closed-loop configuration. The advantages of the novel architecture are demonstrated by circuit analysis and simulations; in particular, very good robustness under process, supply voltage and temperature variations and mismatches were obtained due to the closed-loop approach. The CCII was also used to design a capacitive sensor interface in integrated CMOS technology, where it was possible to achieve a sensitivity of 2.34 nA/fF, with a full-scale sensor variation of 8 pF and a minimum detectable capacitance difference of 40 fF. [ABSTRACT FROM AUTHOR]

The internal mechanical motions of a dynamic system can serve as the basis for developing “free” sensors that probe operating state and potentially diagnostic health. Specifically, the interaction of a moving mechanical structure with an applied, nonintrusive field, e.g., an electric or magnetic field, can produce a signal that creates a sensor with little additional hardware. In this paper, bellows-and-diaphragm natural gas (BDNG) meters provide an illustrative example for three different approaches for transforming a metering mechanism and its consumption totalizer into a high quality flow meter. Detailed flow information provides a data-stream for nonintrusively monitoring the real-time operation of loads – in this example case, loads that consume natural gas such as burners, heaters, and engines. The flow information can also be used for fault detection and diagnostics, e.g., for finding leaks or correlating faulted operation with respect to the operation of electrical actuators or other flows of consumables like water. This paper examines three BDNG meters and presents a methodology for inexpensively adding digital flow-rate measurement capabilities to each meter. The methods developed here can in principle be applied to any dynamic machine for consumption estimation or fault detection. They can also be used with other nonintrusive field stimuli including light and sound. Mechanical retrofit techniques are described, and exemplary signal processing and estimation chains including compensation, flow rate estimation, and post processing are presented. [ABSTRACT FROM AUTHOR]

One‐dimensional sensing materials that are prepared via electrospinning and controlled annealing exhibit intrinsic properties, such as electron transmissivity, magnetic susceptibility, specific heat capacity, as well as optical and mechanical characteristics. Particularly, the electronic transmission characteristics of the ceramic fiber materials, such as the electrical conductivity, photocurrent, magnetoresistance, nanocontact resistance, and dielectric properties, exhibited great potential for applications in the next generation of electronic sensing devices. First, electrospun ceramic materials with different structural and functional characteristics were reviewed here, after which the strategies for improving their properties, as well as the method for assembling the flexible devices, are summarized. Moreover, the electrospun ceramic nanofibers were detailedly discussed regarding applications in device construction and wearable electronics, such as photosensors, gas sensors, mechanical sensors, and other energy storage devices. Finally, the future development direction of the electrospinning technology for multifunctional and wearable electronics skin was proposed. [ABSTRACT FROM AUTHOR]

This paper presents the Flying Lidar (Flydar) aerial robot for simultaneous localization and mapping (SLAM). The Flydar integrates a single laser and capitalizes on the rotating dynamics of a nature-inspired aerial robot for omnidirectional scanning. This paper presents a 2.5D SLAM approach using the Flydar and does not assume that the Flydar’s hovering plane, and thus the laser scan, is parallel to the ground plane, which is a key assumption in our previous work. A quaternion-based filter performs active correction of the Flydar’s laser scan due to its transient attitude. A dual-accelerometer approach was incorporated to estimate the Flydar’s scan rate during high angular rates flight beyond the gyroscope dynamic range. The proposed attitude filter output was experimentally evaluated statically on a bench-top setup and dynamically in flight, with an rms inclination estimate error of up to 1.1° and 0.38° respectively. The attitude-corrected lidar scan was used to estimate the robot pose for 2.5D SLAM. The 2.5D SLAM was experimentally validated on the Flydar and demonstrated to be superior to pure 2D SLAM in loop closure, position estimate drift, and rms error. Significantly, the 2.5D SLAM using the Flydar reports a low rms Euclidean error of up to 0.083 m, which is a 32.0% error reduction compared with our previous work, which uses 2D SLAM. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel end-to-end learning-based LiDAR sensor relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input. Compared to visual sensor-based relocalization, LiDAR sensors can provide rich and robust geometric information about a scene. However, point clouds of LiDAR sensors are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360° LiDAR sensor frames. Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposed method can achieve accurate relocalization performance. [ABSTRACT FROM AUTHOR]

The benefits of the fourth industrial revolution are realised through accurate capture and processing of data relating to product, process, asset and supply chain activities. Although services such as Global Positioning Services (GPS) can be relied on outdoors, indoor positioning remains a challenge due to the characteristics of indoor environments (including metal structures, changing environments and personnel). An accurate Indoor Positioning System (IPS) is required to provide end-to-end asset tracking within a manufacturing supply chain to improve security and process monitoring. Inertial measurement units (IMU) are commonly used for indoor positioning and routing services due to their low cost and ease of implementation. However, IMU accuracy (including heading and orientation detection) is reduced by the effects of indoor environmental conditions (such as motors and metallic structures) and require low-cost reliable solutions to improve accuracy. The current state of the art utilises algorithms to adjust the IMU data and improve accuracy, resulting in error propagation. The research outlined in this paper explores the use of passive RFID tags as a low cost, non-invasive method to reorient an IMU step and heading algorithm. This is achieved by confirming reference location to correct drift in scenarios where magnetometer and zero velocity updates are not available. The RFID tag correction method is demonstrated to map the route taken by an asset carried by personnel in an indoor environment. The test scenario task is representative of warehousing and delivery tasks where asset and personnel tracking are required. [ABSTRACT FROM AUTHOR]

Ubiquitous connection applications have become unprecedentedly attractive recently, and increasing demand for real-time multiple targets localization is proposed. Traditional methods face challenges on efficient localization simultaneously due to signal conflict. In this paper, a real-time multiple-object localization mechanism NL-Locate is proposed. By utilizing the nonlinearity of the microphone modules, we design a modulated ultrasound signal for each target device, which enables the receiver to identify each device simultaneously. Such a method does not require any channel negotiation or preallocation, and we implement the prototype of the localization system and evaluate the system performance in both simulations and real systems. The experiment results demonstrate that NL-Locate can achieve a decimeter-level localization accuracy for multiple targets efficiently. [ABSTRACT FROM AUTHOR]

Most electrical sensor and biosensor elements require reliable transducing elements to convert small potential changes into easy to read out current signals. Offering inherent signal magnification and being operable in many relevant environments field‐effect transistors (FETs) are the element of choice in many cases. In particular, using electrolyte gating, numerous sensors and biosensors have been realized in aqueous environments. Over the past years, electrolyte‐gated FETs have been fabricated using a variety of semiconducting materials, including graphene, ZnO, as well as conjugated molecules and polymers. Above all, using conducting polymers top‐performing devices have been achieved. Herein, an approach to use a transition metal dichalcogenide (TMDC)‐based monolayer device as a transducing element is presented. Using MoS2 monolayers, it is shown that such electrolyte‐gated devices may be regarded as very promising transducing elements for sensor and biosensor applications, enabled by their high sensitivity for environmental changes and the possibility of using the naturally occurring sulfur vacancies as grafting points of biorecognition layers. Furthermore, the behavior of such a device under prolonged operation in a dilute biologically relevant electrolyte such as phosphate buffered saline solution (PBS) is reported. [ABSTRACT FROM AUTHOR]

Developments in flexible electronics have adopted various approaches which have enhanced the applicability of human–machine interface fields. Recently, microstructural integration and hybrid functional materials were designed for realizing human somatosensory. Nonetheless, designing tactile sensors with smart structures using facile and low-cost fabrication processes remains challenging. Furthermore, using the sensors for recognizing stimuli and feedback applications remains poorly validated. In this study, a highly flexible piezoresistive tactile sensor was developed by homogeneously dispersing carbon black (CB) in a microstructure porous sugar/PDMS-based sponge. Owning to its high flexibility and softness, the sensor can be mounted on human or robotic systems for different clinical applications. We validated the applicability of the proposed sensor by applying it to recognizing grasp and release forces in an open setting and to classifying hand motions that surgeons apply on the master interface of a robotic system during intravascular catheterization. For this purpose, we implemented the long short-term memory (LSTM)-dense classification model and five traditional machine learning methods, namely, support vector machine, multilayer perceptron, decision tree, and k-nearest neighbor. The models were used to classify the different hand gestures obtained in an open-setting experiment. Amongst all, the LSTM-dense method yielded the highest overall recognition accuracy (87.38%). Nevertheless, the performance of the other models was in a similar range, showing that our sensor structure can be applied in intelligence sensing or tactile feedback systems. Secondly, the sensor prototype was applied to analyze the motions made while manipulating an interventional robot. We analyzed the displacement and velocity of the master interface during typical axial (push/pull) and radial operations with the robot. The results obtained show that the sensor is capable of recording unique patterns during different operations. Thus, a combination of the flexible wearable sensors and machine learning could yield a future generation of flexible materials and artificial intelligence of things (AIoT) devices. [ABSTRACT FROM AUTHOR]

Robotic devices for nursing care are expected to help caregivers work with the elderly. Some robotic devices assist in the physical transfer of the elderly, and these robots come in contact with large surfaces of the human body. The regions of the buttock and the back may be uncomfortable due to the surface material or motions of the robotic devices. Therefore, sensing devices simulating a human body were developed to quantify and evaluate the load of a human body objectively. This simulated buttock consists of simulated bone and soft tissues, which include muscle, fat and skin. These regions have multi-axis force sensors to enable the quantification of the load due to the robotic devices used for nursing care. On measuring the soft exterior, it was found that the stiffness of the buttock dummy was similar to the human buttock. The comfort of a robotic bed was measured using the simulated buttock, and it was found that the shear force increased due to the deformation of the robotic bed. Thus, it was proven that the simulated buttock was capable of measuring the load of the human body when being used with robotic devices for nursing care. [ABSTRACT FROM AUTHOR]

Domestic household appliances (e.g., washing machines) are major factors in the anthropogenic global energy balance, which is why an efficient control of their energy and material consumption are of great importance in the future. At the same time, most of today's washing machines can be considered as “black boxes” which lack dynamic analysis techniques for an automated and optimized resource management. Following a biomimetic approach, rapid prototyping methods are combined with high‐speed videography and 2D particle tracking. In addition, preliminary work on the functional morphology and hydrodynamic characteristics of the olfactory organ of Sphyrna tudes is utilized to generate a flow channel module suitable for the implementation of diverse state‐of‐the‐art sensor technologies into innovative washing machines. The module's fluid dynamics are essentially determined by its optimized channel geometry and the fluid's inflow velocity. Future investigations will reveal whether a precise change of individual hydrodynamic parameters can solely be achieved by a variation of the inflow velocity and whether specific flow profiles can be established to allow for dynamic online analytics of multiple sensors during washing.

In the present study, a Schiff base (E)-2-[(3,5-bis(trifluoromethyl)phenylimino)methyl]-4,6-dichlorophenol has been synthesized. Its spectroscopic and X-ray single-crystal data have been compared with the results of theoretical calculations based on the DFT method, as well as molecular electrostatic potential (MEP), HOMO and LUMO, and nonlinear optical (NLO) effects. Antimicrobial activity of the compound has been tested against some bacteria and fungi. According to UV-Vis and agarose gel electrophoresis methods, the compound interacts with DNA electrostatically and breaks it oxidatively. Antioxidant activity of the compound measured by the DPPH method, is lower than that of BHT. [ABSTRACT FROM AUTHOR]

Positive displacement water meters are found throughout the world’s water distribution networks to totalize water consumption. Containing internal permanent magnets, the water meters are underutilized sensors. Flow data from these meter locations can be used to non-intrusively identify the operation of individual water loads from an aggregate data stream at the meter. This article models these meters as nonideal magnetic encoders and presents a methodology for inexpensively retrofitting these meters with digital flow rate measurement capabilities, effectively bringing them into the growing body of smart metering devices and non-intrusive load monitoring without any contact with the water passing through them. We present a model of the magnetic field produced by these meters, discuss associated magnetic sensing hardware, and describe compensation, flow rate estimation, and post-processing algorithms for flow rate extraction. Verification is provided with experimental results. [ABSTRACT FROM AUTHOR]

Numerous time synchronization methods have been proposed during the last decades targeted at devices transmitting in general-topology networks, e.g. wireless sensor networks. Interestingly enough, there are still sensorics applications that, from the data flow point of view, just consist of pairs of devices —typically, being one of them a central controller shared among all pairs—; this is common in embedded systems, mobile robots, factory cells, domotic installations, etc. In these cases, time synchronization takes the form of the estimation of the relative drifts and offsets of these pairs of clocks, thus the quality, guarantees and computational cost of the methods become crucial. Under that specific perspective, it is still possible to propose novel approaches that are computationally efficient while providing guarantees on the resulting estimates for the relative clocks relation. In particular, solving the synchronization problem through a geometrical interpretation is specially suitable, since it provides both efficiency and sound estimates with hard bounds. In this article we analyze a direct geometrical approach for pairwise systems that, through a more direct formulation of the common geometrical setting, improves efficiency and adapts better to diverse stochastic transmissions, while providing good estimates. We demonstrate its advantages with a real low-cost, embedded test bed that can be appropriately instrumented for measuring times, comparing its performance against previous synchronization approaches suitable for pairwise systems. [ABSTRACT FROM AUTHOR]

A novel direct resistive-sensor-to-digital readout circuit is presented, which achieves 16.1-bit ENOB while being very compact and robust. The highly digital time-based architecture employs a single voltage-controlled oscillator (VCO), counter, and digital feedback loop for the readout of an external single-ended highly nonlinear resistive sensor, such as an NTC thermistor. In addition to the inherent first-order noise shaping due to the oscillator, the second loop in SMASH configuration creates second-order noise shaping. Fabricated in 180-nm CMOS, the readout circuit achieves 16.1 bit of resolution for 1-ms conversion time and consumes only 171 μW, resulting in an excellent 2.4-pJ/c.s. FOMW for a resistive sensor interface while occupying only 0.064 mm2. The specific closed-loop architecture tackles the VCO nonlinearity, achieving more than 14 bits of linearity. Multiple prototype chip samples have been measured in a temperature-controlled environment from −40 °C to 125 °C for the readout of commercial external NTC thermistors. A maximum temperature inaccuracy of 0.3 °C is achieved with only one-point trimming at room temperature. Since the circuit architecture decouples the sensor excitation from the feedback, high electromagnetic interference (EMI) immunity at the sensor node is demonstrated as well. [ABSTRACT FROM AUTHOR]

A dual-feedback K-band low-noise amplifier (LNA) is presented. This circuit consists of two common-source configurations in series. Its input matching network uses the shunt inductive transformer technique to reduce the chip area and improve noise matching, and uses the transformer technique to improve the gain and isolation. The measured input and output return losses are less than -10 dB at 23 GHz, the isolation is -50 dB, and the gain at 23 GHz is 10.6 dB. This chip is implemented by 0.18 µm CMOS technology and its dimensions are 0.22 mm2. The DC power consumption is 5.4 mW with a supply voltage of 1 V. This amplifier can improve a complete RF front-end for sensor applications. [ABSTRACT FROM AUTHOR]

Domestic household appliances (e.g., washing machines) are major factors in the anthropogenic global energy balance, which is why an efficient control of their energy and material consumption are of great importance in the future. At the same time, most of today's washing machines can be considered as "black boxes" which lack dynamic analysis techniques for an automated and optimized resource management. Following a biomimetic approach, rapid prototyping methods are combined with high‐speed videography and 2D particle tracking. In addition, preliminary work on the functional morphology and hydrodynamic characteristics of the olfactory organ of Sphyrna tudes is utilized to generate a flow channel module suitable for the implementation of diverse state‐of‐the‐art sensor technologies into innovative washing machines. The module's fluid dynamics are essentially determined by its optimized channel geometry and the fluid's inflow velocity. Future investigations will reveal whether a precise change of individual hydrodynamic parameters can solely be achieved by a variation of the inflow velocity and whether specific flow profiles can be established to allow for dynamic online analytics of multiple sensors during washing. [ABSTRACT FROM AUTHOR]