This paper presents a review of FSS technology. The operation principle and main characteristics of specklegram are presented and the applications of FSSs are thoroughly discussed. In addition, advances in microelectronics, machine learning, material processing for new optical fibers and their relation with FSS are also discussed. These new developments on correlated areas can give rise for a new generation of sensors with advantageous features such as lower cost, easy connectivity, higher degree of customization and better performance.

This paper proposes two novel airgap profile designs and distributed airgap arrangement scheme for reduction of winding and thermal losses in high frequency current transformer (HFCT) sensors. The novel airgap profiles namely, zigzag and stepped airgap profiles are proposed to reduce fringing flux around the winding conductors. Moreover, to decrease winding and thermal losses, distributed airgap arrangement scheme is proposed. In distributed airgap arrangement, a single airgap is distributed to multiple equidistant smaller airgaps around the magnetic core. The analysis is performed using 3D finite element analysis (FEA) method. The 3D-finite element analysis results confirm that the HFCT sensors with the proposed airgap profiles and arrangement scheme can reduce winding and thermal losses ensuring high performance for HFCT sensors.

Presents the front cover for this issue of the publication.

Provides a listing of current staff, committee members and society officers.

Presents the table of contents for this issue of this publication.

Bio-signal recording is vital for human condition monitoring and early disease diagnosis. An electrode is a biosensor for bio-signal collection. The conventional wet electrode is not suitable for long-term bio-signal recording owing to the gradual drying of the electrolytic gel and increase in the electrode-skin interface impedance. Microneedle array electrode is a dry electrode that can painlessly pierce skin, form a contact interface between the microneedles and skin tissue, eliminate the high impedance of stratum corneum, weaken the motion artifact, and accurately record bio-signals without skin preparation and application of electrolytic gel. This review focuses on the recently developed fabrication techniques, the mechanical performance, and bio-signal recording performance of microneedle array electrode. The microneedle array electrode is a potential electrode for long-term bio-signal recording, although progress is required in terms of safety and scaled-up manufacture.

This paper is to demonstrate an efficient cell capture assay based on employing patterned highly vertical zinc oxide (ZnO) nanorods. Hydrophilic properties of ZnO nanorods, as well as nano topographic nature of the outer surface of cells were exploited as separation mechanism. MCF-7 and MDA-MB-231 breast cancer cell lines were captured on the patterned or not patterned ZnO nanorods. For these substrates, significantly improved capture efficiency was achieved by patterning the nanorods surfaces. The capture efficiency of MCF-7 cell species increased from 0 to over 84 % through the conversion of flat substrates to patterned nanorods surfaces. Additionally, efficiency promotion was observed by increasing the incubation time from 1 hour to 4 hours for the MCF-7 cells, resulting in maximum capture efficiency of 98.4% in the case of patterned nanorods. However, MDA-MB-231 cancer cells, as invasive late-stage cell species, were expectantly found to show lower efficiencies, maximum of 86%. Intensively and strongly adhered breast cancer cells to nanorods with formed lamellipodia have been reported for proposed ZnO nanostructured substrates. Interestingly, both types of breast cell species depicted different cell areas, offering various focal adhesions. This allows for selective separation of not only the breast cancer cells from the normal blood species but also these two cell types while retaining viability.

A stamp-sized, flexible, wireless, generic electrochemical sensor platform was demonstrated for use as a potentiometric pH and amperometric Cl − sensor. The platform combines flexible printed circuit technology with screen-printed electrodes, which enables cheap and easy quantification of biorelevant analytes in many different environments. pH sensors with graphene oxide as the recognition element was constructed on a ferrocyanide modified carbon electrode, to maintain a well-defined redox potential even at high alkalinity. A solid-state reference electrode based on a NaCl-doped polyvinyl butyral membrane ensured applicability towards sample matrices of unknown composition by remining unsensitive (±7.1 mV) to external Cl − in the range pCl 0–3. The platform was able to linearly (−26.3 mV pH −1 , r 2 = 0.99) quantify pH in the range of 2–9 with a maximum inter-sensor standard deviation of 0.43 pH units. Secondly, a Cl − sensor combining three unmodified Ag/AgCl electrodes with squarewave amperometry was designed, simulated and tested. A sensitivity of (−147.7 $\mu \text{A}$ pCl −1 , r 2 = 0.97) was obtained, with a maximum inter-sensor standard deviation of 0.27 pCl units in the range pCl 0–3.

A high-sensitive surface plasmon resonance (SPR) sensor was proposed using a self-assembly optical glucose-sensitive membrane (OGSM) and an osmotic-protection membrane on a glass/gold sheet. A prism and an OGSM were assembled to form a multilayer structure such as glass/gold/(PDDA/PSS) 2 /(PDDA/ImGODs) n /(PVA+PEG), where the ImGODs layer refers to immobilized glucose oxidases (ImGODs) on mixed nanoparticles consisting of SiO 2 nanoparticles (SiNPs) and mesocellular foams (SiMCFs) and where polyvinyl alcohol and polyethylene glycol were used to form an osmotic-protection film. Many glucose-sensing experiments were performed to determine the optimal parameters of the OGSM proposed. For the OGSM with a mass ratio of SiMCFs:SiNPs = 7:3, the plasmon resonance angle of the glucose sensor reduced 2.60° and 0.26°/(mg/dL) of average sensitivity within the glucose concentration range from 0 to 10 mg/dL. The relationships among the resonance angle shift of the sensor, refractive index of the OGSM, and adsorption isotherm of the OGSM were established. The relationships showed that the Langmuir isotherm model was suitable for describing the adsorption process of the OGSM for glucose molecules in the concentration range of 0–80 mg/dL. For the SPR glucose sensor with three sensitive bilayers (PDDA/ImGODs) 3 , the dependence relationships of resonance angle shift and sensor sensitivity on glucose concentration, $\Delta \theta _{\text {res}}=-{2.076} {C}$ / ( ${1}+{0.707} {C}$ ) and ${S} = {2.076}$ /( ${1}+{0.707} {C}$ ) 2 , were obtained.

A 4-cyano-4’-pentylbiphenyl(5CB) microdroplet-based sensing method for myricetin (MY) detection, which is produced by a syringe pump connected tapered capillary microtube and functionalized by dodecyltrimethylammonium bromide (DTAB) and deoxyribonucleic acid (DNA) at the aqueous/liquid crystal (LC) interface is proposed and demonstrated. As the concentration of MY increases, the 5CB microdroplet exhibits a structural transition from a bipolar configuration to a radial configuration. We also exploit the 5CB microdroplet to act as an optical microcavity of the whispering gallery mode (WGM) and obtain typical WGM lasing spectra under a pulsed laser pump. The WGM spectrum, which is related to the orientation of LC molecules at the aqueous/LC interface, presents a spectral blue shift with the increase of the MY addition. The sensitivity within the detection range is 0.04 nm/ $\mu \text{M}$ . The experimental results demonstrate the feasibility of this simple and small size system for MY detection.

Acyclovir is a synthetic deoxyguanosine analog that is used for the treatment of herpes simplex as well as varicella zoster virus infections. In this work, by incorporation of different deep eutectic solvents (DESs), ZnO nanoparticles, and multi-walled carbon nanotubes (MWCNTs) into the carbon paste matrix, and electropolymerization of arginine at the resulted electrode, new sensor was designed for improved sensing of acyclovir. The effect of influential factors on the electrode quality was investigated. As a result, the carbon paste electrode modified with 5% ZnO, 5% MWCNTs, 5% DES (choline chloride-urea) and applying10 cycles in polymeric condition exerted remarkable enhancement for acyclovir in physiological media (pH = 7). Two linear ranges have been obtained for the acyclovir concentration within the ranges of 0.01–0.33 and 0.33- $1.0~\mu \text{M}$ , with a detection limit of $0.007~\mu \text{M}$ . The excellent selectivity of sensor for the acyclovir determination was confirmed by discrimination of peaks for interferences (e.g. ascorbic acid, and guanine). Suitability of the fabricated sensor for acyclovir determination in pharmaceutical formulations and human fluid was confirmed with satisfactory results.

The digital replication of smell – so-called olfactory displays, remains one of the least advanced sensory technologies. Recent developments have focused heavily on virtual reality. However, there remains a need for olfactory displays targeted towards aroma training. In this paper, we present a thermal-based olfactory display, using Peltier heating elements. The unit is fully computer-controlled and holds up to 12 ‘aroma capsules’. Each capsule contains an individual aroma that can either be activated on its own or with others to create richer experiences. The unit has been designed as part of a wine aroma game to promote sensory training. The portable device has been tested with 15 volunteers to test the detection and recognition times of 6 aromas associated with red or white wines.

Cell viability is an important indicator while screening drugs. An inaccurate evaluation of cell viability can cause large errors in anti-tumor dose experiments, and this becomes very unfavorable for cancer treatment. Generally, cell viability refers to the ratio of the number of live cells to the total number of cells. However, this evaluation method does not consider the effects of differences in metabolic abilities between different living cells. In this paper, there is a new cell viability evaluation method based on respiratory thermodynamic feature that includes the respiratory intensity, proliferation rate, and heat released by cells. These three parameters can be directly measured by a microscopic infrared thermal imaging sensor, which is fast and non-invasive and does not require consumables. The three parameters were simultaneously measured based on a micro-infrared thermal imaging sensor and fitted to the mathematical model. Finally, the method was verified by comparing it with traditional c ounting method and comparing the amount of omethoate with traditional counting method. The results indicated that omethoate is 12.36% lower than in the traditional counting method with the same level of complete cell inactivation. Therefore, this method is more accurate than the conventional cell viability assessment method, and the dosage is more precise when the uniform effect is achieved, which provides a basis for precise doses in tumor treatment and can reduce side effects in the human body. This method has a significant effect on the manufacture of cell activity detecting sensors.

Dissolved gas analysis (DGA) is vital to status evaluation of power transformers. In this study, a solid oxide fuel cell (SOFC) gas sensor was fabricated and applied to DGA. Then, a gas chromatographic system was developed on basis of the SOFC detector. From Nernst function of SOFC, a mathematical model based on oxygen consumption accumulation (OCA) has been proposed to quantify trace gases dissolved in oil. Based on the OCA of SOFC, the gas concentration can be calculated though the chromatogram directly, no calibrating gas is needed, thereby greatly simplifying the quantification process. Following peak detection based on the improved filter matching method, tests were conducted to verify the validity of the OCA method. The experimental results demonstrated that the repeatability represented by relative standard deviation is less than 0.5%; high precision can be achieved, measurement error for five feature gases are less than 10% at given concentrations, providing a competitive performance against other gas sensors with curve fitting method.

An inexpensive methanol sensor lid fabricated from a composite conductive polymer comprised of cellulose nanocrystals, carbon nanotubes and polyanilines (PANI@CNC/CNT) has been demonstrated. The sample holder composed of an inexpensive tube with Kanthal resistive heating for preferential methanol headspace sampling. The PANI@CNC/CNT nanoporous aerogel film has inherent ultra-high surface area, with the CNT/PANI free valence electrons affording a high electrical conductive material. The PANI@CNC/CNT methanol sensor performance was tested for its response to methanol, ethanol standards and methanol/ethanol mixtures. The sensor was 46 times more sensitive and selective to methanol compared to ethanol, and afforded a linear response in the 0-7% methanol concentration range, deviating from linearity above 10%. The sensor was also used to test methanol standards spiked in wine, yielding a linear (R 2 = 0.9842) resistance change in response with methanol concentrations in the range, 0-7%. The sensor precision for triplicate standards for different concentrations (0.5-5.0%) averaged ~4.5%, indicating overall reliability of the device. With sample heating and detection time fixed at 10 mins, the sensor demonstrated optimal performance with a limit of detection of 0.30% for methanol. The sensor was tested for over 24 runs over a one-month period without significant loss in performance due to degradation. multiple uses without degradation in performance. The PANI@CNC/CNT has been found effective for as a dosimetric sensor for use in indoor air quality monitoring and beverage adulteration analysis.

A solid-state vapor sensor in parallel electrode configuration was fabricated by employing 1-D TiO 2 nanorods as a sensing layer. Highly ordered and oriented TiO 2 nanorods were synthesized on a Ti substrate by using hydrothermal method. X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) were used to characterize the TiO 2 nanorods/Ti samples. The developed Au/TiO 2 nanorods/Ti type parallel electrodes sensor demonstrated the potential of integrated operations of both resistive and capacitive changes towards various concentrations (50–300 ppm) of volatile organic compounds (VOCs) like methanol, ethanol, 2-propanol, acetone and benzene at 50 °C. The resistive response magnitude of the sensor was found to be increased from 13 % to 87% while the capacitive response magnitude of the sensor was increased from 32 % to 200%, as methanol concentration was increased from 50 ppm to 300 ppm. However, the use of both modes enhances the selectivity performance of the sensor as the resistive mode exhibited better selectivity for a lower concentration of VOCs and the capacitive mode for higher concentration of VOCs. Moreover, the sensor showed a very good stability because of low operating temperature (50°C) as well as rutile (major) phase of TiO 2 nanorods.

In this paper, the structure of Core-Shell Junctionless Nanotube Tunnel Field Effect Transistor is proposed and investigated to minimize the fabrication steps and fabrication complexity of the device. The proposed junctionless device is implemented as a biosensor. The core-source metal and shell gate electrode are created using suitable work function. The p-type source region is created by using metal of work function 5.93eV and the shell gate is created by using a metal of work function 4.5eV, over the silicon surface. For biosensing application, a nano cavity is introduced between core-source metal and source region. The device is vertically aligned which provides stability to the biomolecules within the structure. For the investigation of biosensing application, three biomolecules of different dielectric constants, streptavidin (k = 2.1), 3-aminopropyltriethoxysilane (k = 3.57), and protein (k = 8), are used. The performance of the device for biosensing application is analyzed for both neutral and charged biomolecules. To examine the efficiency of the device, various characteristics are studied based on parametric variations, the effect of charged biomolecules, and sensitivity of the device and so on. The motivation behind our work is to design a device having optimum performance attributes without sacrificing the performance parameters of the device such as the ON-state current, subthreshold slope, I ON /I OFF ratio, etc.

To realize the on-line and in situ pH detection in soilless culture, a new all-solid-stated pH sensor was presented. The pH sensor consisted of alumina ceramic (Al 2 O 3 ) substrate, Sb/Sb 2 O 3 working electrode, Ag/AgCl reference electrode, Ag lead and pad. Sb thin film and Ag thin film were obtained by magnetron sputtering method, and then thermal annealing and Nafion modification was conducted to improve the performance of the sensor. Moreover, the influence of surface area of Sb electrode on the performance of pH sensor was investigated with the area of 4 mm 2 , 9 mm 2 , 16 mm 2 , 25 mm 2 and 36 mm 2 , respectively. Through surface morphology analysis, energy spectrum analysis and electrochemical experiments, the pH sensor, which Sb electrode surface area is 16 mm 2 , shows excellent comprehensive performance, with high sensitivity (67.3 mV/pH), short response time (less than 3.5 s), good stability (standard deviation less than 2 mV in 700 s period), low hysteresis (less than 1.63 mV) and low drift rate (less than 1.62 mV/h). The developed sensor can be applied in pH detection in soilless culture and is promising to popularize in industrial production.

A new 3D process is proposed for inertial MEMS sensors where a thin transduction layer based on high-density surface-varying comb fingers, with nano-metric gaps, is patterned below a thick seismic mass layer. The objective is to achieve high-performance in a reduced footprint. An in-plane accelerometer is designed, fabricated with this new 3D process, and tested under acceleration. Initial fabricated devices demonstrate a full-scale range of ±160 g, $7~\mu {g}/ \sqrt {\textit {Hz}}$ Brownian noise floor and more than 4 kHz bandwidth. These figures translate into a dynamic range of more than 150 dB (normalized to 1-Hz bandwidth) with an overall footprint of only ${400}\times {600}\,\,\mu {m}^{{2}}$ .

With the increasing demand for total knee arthroplasty (TKA) surgery, more attention has been paid to the accurate measurement of pressure and adjustment of knee prosthesis. The tightness between artificial meniscus and articular head and the thickness of artificial meniscus will directly affect the matching between knee joint and human body. At present, the installation and adjustment of knee joint during operation largely depends on the personal experience of doctors. In this work, a flexible pressure sensing device with bendable and distortable sensor arrays for force detection in knee arthroplasty is fabricated and characterized to provide quantitative data guidance and reference for knee arthroplasty. The internal pressure of prosthesis knee joint can be effectively detected through real-time obtaining for output voltages of sensor array. The densely distributed measuring points feedback abundant pressure distribution information. The pressure device shows good repeatability, consistency and small hysteresis in large detection range and stability in dynamic response which indicates promising potential in applications of knee arthroplasty.

In this study we present a novel dual-resonator temperature sensor which can be embedded in other MEMS sensors for improved thermal compensation and on-the-run calibration. For accurate temperature measurements, the proposed method mitigates time base errors in frequency counting, eliminates the need for a highly accurate reference clock and can cancel out the effects of aging of the time base without using a calibration process. The sensor structure is composed of a strain amplifying beam and two Double Ended Tuning Fork (DETF) resonators with different temperature sensitivities. The DETFs are kept at resonance simultaneously with a dual PLL circuit. Experiments reveal that at the expense of decreasing sensitivity, one can suppress the measurement errors which can be as high as 0.164 °C for the long resonator and 0.240 °C for the short resonator when a time base of 50 ppm accuracy is used. Moreover, while the frequency stability characteristics of the single sensing elements deteriorate drastically as the accuracy of the time base decreases, the frequency stability of the proposed frequency ratio remains unaffected and it is superior compared to both of the resonators.

A class of angular-displacement microwave sensor based on a microwave signal-interference transversal filtering section (TFS) with unequal-electrical-length paths is reported for the first time. It consists of a bi-path TFS composed of two in-parallel transmission-line segments with distinct lengths, which is modified by adding a rotational open-circuit-ended stub as the rotor to a curved section as the stator of one of its line segments. In this manner, the spectral positions of the transmission zeros, which are produced in its filtering transfer function through destructive signal-energy interactions, are modified with the rotation of this additional stub. This allows single/multi-band angular-displacement microwave-sensing capabilities in terms of transmission-zero inter-spacing variation for passbands/stopbands and stopband-peak-level modification to be attained by means of the mechanical rotation of the rotor. The theoretical operational principles of this type of bi-path-TFS-based angular-displacement microwave sensor and design curves for specific values of its electrical parameters are provided. Moreover, for experimental validation purposes, a proof-of-concept prototype of the devised TFS-based microwave-sensor approach operating at 961 MHz with 180° dynamic range is prototyped in microstrip technology and measured. A comparative analysis of the developed angular-displacement microwave sensor with the state-of-the-art is also presented.

Gait disturbance is one of the most pronounced and disabling symptoms of cerebellar disease (CD). Generally, gait studies quantify human gait characteristics under natural walking speeds while mainly considering upper body movements. Therefore, the primary goal of this study was to investigate the influence of different walking speeds on different gait parameters of both the upper and lower body, as a result of disabilities caused by Cerebellar Ataxia (CA). We employed wearable sensor technology to identify the kinematic characteristics which best identify the gait abnormalities seen in CA. Measurements were made at self-selected slow, preferred and fast walking speeds. Velocity irregularity and resonant frequency characteristics were identified as key features of truncal and lower limb movements respectively. Subsequently, the differentiating features for both trunk and lower limb movements were combined to produce an even greater separation between the patients and the normal subjects, as well as better correlation with the expert clinical assessment (ECA) (0.86) and the Scale for the Assessment and Rating of Ataxia (SARA) (0.62). The different speed of walking conditions resulted in varying degrees of the separation and the correlation. Moreover, the contribution of the extracted features was examined using the random forest algorithm. Clinically observable truncal medio-lateral movements express the disability at relatively slow gait speeds while the anterior-posterior movements captured by the sensory mechanisms characterises the disability across all walking speeds. The importance of selected dominant features from the trunk and lower limb suggest that overall clinical assessments are predominantly influenced by the lower body peripheral movements, particularly at higher cadences.

When implementing temporary vibration monitoring of mechanical equipment, it is hoped that the installation and disassembly of the sensor will not change the original structure of the equipment. In that case, non-intrusive vibration sensors are preferable. This paper presents a motion induced eddy current sensor that can non-intrusively acquire vibration signal from the surface of metal objects. The sensor consists of a cylindrical permanent magnet and a pancake coil coaxially placed under it. The permanent magnet is used to generate a static magnetic field and the coil is used to pick up the eddy current signal induced by vibration. The results of principle experiments indicate that the output signal of the sensor has a linear relationship with the vibration acceleration/velocity when the measured object is a non-ferromagnetic/ferromagnetic metallic specimen. Two application measurement experiments are also done. When the sensor is placed on the metallic panel of a weighting scale, the ballistocardiogram (BCG) of the human body can be effectively obtained. Another experiment is carried out on a rotor test bed. When the sensor is placed above the bearing support with a fixed lift distance, satisfying vibration signals can be obtained compared with that from the conventional piezoelectric accelerometer. The present works show that the simple-structure, low-cost, non-intrusive vibration sensor (MIECS) can be applied in various fields.

In many automatic production equipment, it is necessary to simultaneously measure the torsion and tension of the end-effector. However, the existing composite force sensors cannot be used in high-speed rotating equipment such as manipulators. In this work, through the research on the measurement principle, elastomer mechanical structure, installation method, electric signal processing method and decoupling method of the composite force sensor, a magnetic coupling rotary composite force sensor is designed, which can be installed on the high-speed rotating equipment. The internal components of the sensor are powered by a dynamic transformer coupled with infrared pulse signal transmission. The sensor can accurately detect the torsion and tension of the end-effector while the device rotates at high speed. By establishing decoupling matrix, the coupling interference is reduced. The comprehensive measurement accuracy of the sensor ≤0.5% F.S.

Magnetoelectric (ME) composites with ferromagnetic and ferroelectric phases show a strong coupling between the magnetic and electric subsystems that occurs through mechanical strain. Such composites are suitable for applications such as transducers that convert magnetic and electric energies. An important issue that needs to be considered is the magnetic-to-electric energy conversion efficiency. For applications under high power, energy transduction requires a high mechanical stability that minimizes the loss during conversion. Here, the high drive mechanical stability of Mn-doped Pb(Mg 1/3 Nb 2/3 )O 3 -PbTiO 3 (PMN-PT) crystals has been enhanced by external magnetic field when bonded to Fe-rich Metglas foils. External field modulation stabilizes the mechanical quality factor under high power drive. A magnetoelectric gyrator was then made that had an excellent power conversion efficiency of $\eta ={92}$ %, but which also had a dramatically decreased size as compared to similar gyrators made of “hard” Pb(Zr,Ti)O 3 (PZT) ceramics. The findings provided an approach to optimizing the size-weight-power space of ME gyrators for power converters.

The effect of isotope shifts of nuclear magnetic resonance (NMR) frequency in xenon isotopes 129 Xe and 131 Xe polarized by optically-oriented alkali metal atoms is not only of fundamental interest, but also of practical significance, since it is the main factor limiting the accuracy of a whole class of prospective navigation and metrological devices. Our study of the parametric dependences of the isotope shift has shown that this effect is largely due to incomplete averaging of an inhomogeneous local magnetic field by two xenon isotopes, and therefore, its value can be influenced by applying an external magnetic field gradient. A numerical model is derived that qualitatively describes the effect of the isotopic frequency shift, as well as its dependence on the cell temperature and the external magnetic field gradient. The model provides a good quantitative agreement with the experiment – e.g., at a temperature of 85°C, the isotope shift values predicted by the model coincide with experimental data with an accuracy of up to the experimental error, which, in turn, does not exceed 8% of the total change in the isotopic shift in the entire range of applied magnetic field gradients.

Due to the environmental concerns and new energy policies, worldwide expectations for energy production utilizing photovoltaic (PV) systems are increasing significantly. The aluminum electrolytic capacitor (AEC) is extensively used in filtering application for power electronic converters in PV systems since they can achieve the highest energy density with the lowest cost. However, the lifetime of an AEC is limited due to the electrolyte vaporization. The degradation of AECs challenges the efficiency and reliability of a PV system. Therefore, the health-monitoring of AECs is indispensable for the PV systems to operate reliably. In this paper, an online AEC-monitoring scheme based on magnetic-field sensing is proposed for PV systems under various working conditions. The AEC-monitoring technique using the equivalent series resistance (ESR) and capacitance (C) as the health indicators were developed for the power electronic converters in PV systems. The proposed methodology considering the voltage drops on C can improve the accuracy in ESR-estimation and achieve the estimation of C. The simulation results with Simulink verified that the proposed method was capable of estimating the health indicators accurately over various levels of solar irradiance and ambient temperature. The tunneling magnetoresistive (TMR) sensors were pre-calibrated from −25 to 100° C for implementation in PV systems. The experimental results proved that TMR sensors could measure the current of AECs effectively to achieve the precise estimations of the health indicators using the proposed technique. This technique is non-invasive, compact, and cost-effective since it can be realized with the TMR sensors or other MR sensors.

Last advances in low power sensors has led to the development of wireless visual sensor networks. These networks comparing to traditional wireless sensor networks provides valuable visual information, therefor are suitable for surveillance and control applications. One of the important challenges in wireless visual sensor networks is energy consumption. Therefore, in this paper we address the problem of energy optimization in wireless visual sensor networks. The entropy of the captured images in each camera node sensor as a quality criteria is used. For realization of this goal, we introduced a new formula for expressing the entropy of captured image in each camera node. This model is a function of the distance between camera node and target and the angel between the main view line of camera and the target. Then, we introduced a new formula which expresses the relation between the image entropy and the number of bits required for displaying image pixel value. In next step, we proposed node selection algorithm based on entropy. For proposing our algorithm, first optimization of energy consumption is formulated, then the problem is solved as a convex problem. We used from CVX library in MATLAB and Log-Barrier Method for solving our problem and shown the simulation result of them. Finally, we compare our proposed algorithm with MDT benchmark algorithm, CVX and Log-Barrier algorithms. Also we compare the sensitivity to error for both algorithms.

It is possible to demodulate the embedded communication symbols from the transmitted base waveform by using the relationship between the transmitted base signal and the synthesized signal in the spatial domain of a circulating code array. The spatial-frequency modulation coefficient (SFMC) is firstly analyzed to determine the frequency band used for the communication user in a certain direction. Perturbation phase modulation (PM) term is attached to the certain time duration of a linear frequency modulation (LFM) signal to embed communication symbols. The perturbation PM term is designed to be a weighted sum of multiple orthogonal sub-PM terms and the communication symbols are carried out by the coefficients of different sub-PM terms. An adjustable weighted coefficient is applied to control the degree of the perturbation PM term to make a balance between radar performance and communication bit error rate (BER) performance. The designed dual-function radar-communications (DFRC) system has the following properties: 1) Multidimensional waveform design issue for a DFRC system based on multiple input multiple output (MIMO) is converted into a transmitted base waveform design issue; 2) the communication receiver can be located in any direction; 3) the effect of embedded communication symbols on radar performance can be effectively controlled while with a satisfactory BER performance.

Distributed microring sensors using space-time function control is proposed for artificial microfacial sensors. The system consists of 6 different node locations, corresponding to the form of the human microfacial structure. Two space-time function input sources are fed into the system simultaneously. The distributed stereo network sensors are investigated. Each sensor node is embedded by a different gold grating period, in which the coupling between the photon and grating generates different plasmonic Bragg wavelengths outputs, which can be used to identify the node positions. The changes introduced to the sensor nodes via the space-time function relationship, such as the polariton (phonon), wavelength, frequency, and temporal change of the Bragg wavelength, can be measured. By using the whispering gallery mode output, the dipole oscillation of each node can be obtained, which can be used for a distributed facial sensor network. The distributed network is connected by the microring coupling in the system. By using the stereo sensor and space-time function sources, a balance of the two-channel sensing signals, known as a stereo sensor, can enable a self-calibration of the sensor, which is achieved. Moreover, exchange between the polariton and electron can be achieved, and electro-optic conversion is obtained. Moreover, the electro-optic conversion obtained by exchanging the polariton and electron energies means that both wireless and cable transmission modes can be employed.

This paper investigates electromagnetic (EM) vortex imaging based on dual coupled orbital angular momentum (OAM) beams. An OAM-generating scheme is proposed to generate radio waves carrying various dual coupled OAM at the X-frequency band and the experimental results of radar imaging in an anechoic chamber are illustrated for the first time. The uniform circular array (UCA) is exploited to generate dual coupled OAM beams and the radiated fields are presented by both theoretical analysis and full-wave EM simulation. Subsequently, the mathematical imaging model is established and the target reconstruction method is proposed. The relative imaging error (RIE) is introduce to evaluate the imaging performance. The imaging results indicate that the proposed method can have a better performance than EM vortex imaging without OAM-multiplexing and save half time of OAM beams transmitting. Moreover, the imaging experimental results in the anechoic chamber validate the effectiveness of the proposed method. This paper applies OAM-multiplexing beams to radar imaging and improves the vortex imaging efficiency, which can benefit the applications of OAM beams and the design of OAM-based radar systems.

In the field of temperature safety monitoring, it is important to quickly locate the position of temperature anomalies and start to alarm. The multimode fiber (MMF) has a certain temperature hysteresis effect when detecting the surrounding temperature along the sensing fiber, it deteriorates the warning-time of Raman distributed temperature sensor (R-DTS). This paper proposed and experimentally demonstrated an R-DTS with heat transfer functional model to perceive the surrounding temperature in advance. The temperature hysteresis effect of MMF cable in different temperature conditions are studied experimentally. The experimental results show that the temperature change rate of MMF and the surrounding temperature difference maintains a linear relationship after fitting. And this fitting relationship is used to perceive the surrounding temperature anomalies along the MMF for R-DTS system. The experimental results indicate that the warning-time of R-DTS can optimize from 23.4 s to 1.3 s at the temperature condition with 60 °C. It proves that this analytical model of the heat transfer function model can quickly perceive the environment temperature anomalies to avoid the temperature hysteresis effect of MMF at the difference temperature conditions. The research content can be applied in the temperature safety monitoring with high requirements for early-warning time, such as fire monitoring, power cable safety monitoring, and gas pipeline leakage detection.

We present studies on the long-term reliability of interferometric fiber-optic current sensors (FOCS) for use in electric power transmission systems. Accelerated ageing tests are performed on crucial optical sensor components and a three-phase sensor system is subjected to an extended field trial. The sensor components under test include the sensor’s superluminescent light emitting diode light source, the integrated-optic phase modulator and various passive components such as fiber couplers, fiber polarizers, polarization-maintaining fiber connectors, and fiber coatings. The components are exposed to accelerated ageing conditions for extended periods of time, i.e., temperature cycling (between −25 °C and 65 °C for up to 15000 cycles), constant high temperature at dry conditions (up to 115 °C for up to 20000 h), and damp heat (85 % relative humidity at 85 °C for up to 7900 h). Crucial component parameters such as the source wavelength and polarization extinction ratios are repeatedly measured as a function of temperature at defined intervals during the ageing periods and examined for potential drift of component failures. The field trial is carried out for a three-phase FOCS system integrated into 420 kV double-chamber circuit breakers over a period of more than three years. The sensor signals are compared to the signals of conventional current transformers. In addition, the evolution of various operational parameters such as the light source power is continuously recorded. The results prove a high degree of reliability of modern FOCS systems.

In this paper, a fiber-coupled whispering gallery mode (WGM) microsphere resonator was fabricated with using a simple package scheme. The resonant properties of the WGM resonator are analyzed and their responses to bulk refractive index and temperature are theoretically and experimentally investigated. Experiment results show that the bulk refractive index sensitivity is about 20.49 nm/RIU with detection limit of $4.3\times 10^{-4}$ RIU and very low temperature sensitivity of 7.38 pm/°C. Moreover, the WGM microsphere resonator is firstly used for acoustic intensity level sensing, and the sensitivity is about −1.9 pm/dB, which can enlarge the application field of the WGM sensor and can be used for noise monitoring to enhance environmental protection.

A D-shaped plastic optical fiber (POF) assisted by a long period grating (LPG) is proposed as a refractive index (RI) sensor. The LPG is fabricated on the D-shaped region by a simple mechanical die press print method. The RI sensing performances of the D-shaped POFs with LPGs imprinted on POFs with different diameters (of 0.25, 0.50, and 1.00mm) are studied, and the results show that the D-shaped POF with an LPG achieve higher RI sensitivity than that of the one without an LPG. When the D-shaped POF with an LPG fabricated on POF with a thin diameter of 0.25mm, the extremely high sensitivities of 2676%/RIU and 9786%/RIU are obtained in the RI ranges of 1.33-1.40 and 1.40-1.45, respectively. Additionally, the temperature dependence of the sensor is also investigated. The experimentally confirmed high sensitivity of our proposed sensor, combined with its simple structure, easy fabrication and low cost, makes it a promising candidate for RI sensing applications.

A novel person identification (PID) technique is developed in this study, which exploits a new biometric called bronchial breath sound and speech signal acquired by a stethoscope. In addition to investigating the acoustic characteristics of breath sounds for PID, we evaluate three identification methods, including support vector machines (SVM), artificial neural networks (ANN), and i-vector approach. Recognizing the requirement that the amount of sound data collected from each person should be as small as possible, this work studies data augmentation (DA) techniques that avoid the system training process from the overfitting problem when the training sound data is insufficient. In addition, we apply feature engineering techniques to find the informative subset of breath sound features which is beneficial for PID. Our experiments were conducted using a dataset composed of 16 subjects, including an equal number of male and female participants. In the test phase, both Support Vector Machine combined with feature selection and Artificial Neural Networks approaches yielded the promising accuracies of 98%.

This paper reports on the development and testing of a novel barrier sensor for UHF Partial Discharge (PD) detection in gas-insulated equipment. The sensor features a unique dual-slot planar antenna backed by an air-filled cavity. The dual-slot arrangement allows different parts of the antenna to resonate at different frequency ranges in the UHF spectrum. As a result, the sensor exhibits broader bandwidth and higher sensitivity than other barrier sensors. Finite Element Analysis simulations have been used to optimize the sensor design. Furthermore, testing using a specially made PD test rig, GTEM cell testing and testing on a gas-insulated line section in the high voltage laboratory, have validated the simulation results and the capabilities of the sensor. The Dual-Slot Barrier (DSB) sensor exhibits a bandwidth of 0.3 – 2.0 GHz with a mean effective height of 13 mm, and an effective height above 2 mm for 90% of the frequency range. The sensor can be used with both wideband instruments, such as oscilloscopes, and narrow band instruments such as frequency downconverters. Additionally, its optimized dimensions and unique replaceable sealing attachment ensure maximum compatibility for retrofitting on a wide range of equipment.

Residence times difference (RTD) fluxgate magnetometers measure the magnetic field using the time difference between positive and negative pulses of the induced voltage signal in the time domain. Sensitivity is one of the most important parameters to evaluate the performance of the magnetometers. According to our best knowledge of existing literature, for the RTD fluxgate sensors, there are no available models that can clearly set up relationships between physical parameters of an RTD fluxgate and the sensor sensitivity. Moreover, the relationship between the sensitivity and the excitation conditions is very complicate, thus the sensitivity calibration process is time-consuming. This paper presents a sensitivity model at a near-zero magnetic field. According to the model, by using relevant parameters of the excitation coil and driving signal, and the hysteresis state of the magnetic core, accurate predictions of sensitivity for the sensors can be made. RTD fluxgates with different structures were designed and built to experimentally verify the presented sensitivity model. The testing results indicates that the model is valid and the relative error of its prediction on sensitivity is less than 4%. Using the proposed sensitivity model, we can not only study situations of various excitation signals but also have a theoretical foundation to miniaturize the RTD fluxgate sensors.

The problem of the calibration of a triaxial sensor considering its dependence on temperature is addressed. The sensor is modeled with a linear dependence on the measurements, but a non-linear dependence on the temperature. A calibration method is presented. The calibration method consists of an iterative algorithm that minimizes a distance, followed by a validation step. A prototype of a calibration system is designed to collect data from multiple triaxial sensors. The calibration algorithm is used to extract information on the temperature dependence of the sensors and on the dependence of the calibration parameters over time. Several sensors are calibrated, and the results are used in a dead reckoning experiment.

In this work we present a novel technique to estimate the resonance frequency of LC chipless tags (inductor-capacitor parallel circuit) with improved sensitivity and linearity. The developed reader measures the power consumption of a Colpitts oscillator during a frequency sweep. The readout circuit consists of a Colpitts oscillator with a coil antenna, varactor diodes to change the oscillator frequency, analog circuitry to measure the power consumption and a microcontroller to control the whole system and send the data to a PC via USB. When an LC tag is inductively coupled to the oscillator, without contact, a maximum power peak is found. As shown by an experimental calibration using an LC tag made on FR4 substrate, the frequency of this maximum is related to the resonance frequency. Both parameters, power consumption and resonance frequency, present an excellent linear dependence with a high correlation factor (R 2 = 0.995). Finally, a screen-printed LC tag has been fabricated and used as relative humidity sensor achieving a sensitivity of (−2.41 ± 0.21) kHz/% with an R 2 of 0.946.

A framework to detect aortic valve opening (AO) phase with the help of seismocardiogram (SCG) signal is proposed. A small electronic circuit board is designed, which consists of 3-D MEMS based accelerometer, pre-amplifier, and filter. It is interfaced with standard data acquisition system to record SCG signals. The signal is decomposed using a proposed modified variational mode decomposition technique. In the first stage of decomposition, baseline drift is suppressed. Whereas, in the second stage, signal information related to AO instants are extracted. Gaussian derivative filtering is performed on each of the decomposed modes to enhance the systolic profiles. These filtered modes are named as Gaussian derivative filtered modes (GDFMs). The GDFMs with probable AO peaks are selected based on proposed relative GDFM energy (RGE). The signal is reconstructed from the selected GDFMs and it is emphasized using the weights derived from squared RGE. The iteratively extracted maximum slope information is incorporated for systole envelope construction. Finally, peaks are detected using Hilbert transform and cardiac cycle envelope. The robustness of the proposed framework is evaluated using clean and noisy SCG signals from two different databases. For publicly available database (CEBS, Physionet), mean detection error rate 5.2%, sensitivity 97.3%, positive predictivity 97.4%, and detection accuracy 95.1% are found. For our real-time SCG database, the values of these metrics are 6.9%, 96.7%, 96.4%, and 93.4%, respectively. The developed system shows good detection rates even on less number of analyzed beats.

This paper presents a novel and power efficient readout architecture for uncooled microbolometers that offers adequate noise performance along with a circuit-based method for self-heating compensation. The proposed method employs an event-generation scheme and a time-mode readout chain to achieve dynamic mode of operation resulting in low power. The readout chain consists of a capacitive transimpedance amplifier (CTIA) based current-to-time converter followed by a two stage time-to-digital converter (TDC). The CTIA employs a novel integration scheme for time amplification along with a modified reset mechanism to achieve self-heating compensation. We also present a model to analyze the implications of time-mode readout on imager operation. An experimental readout chip, based on this approach, has been designed using a 130 nm bulk CMOS technology. The proposed architecture offers robust frontend processing and achieves a per channel power consumption of $66~\mu \text{W}$ , which is considerably lower than the most recently reported designs, while maintaining better than 10-mK readout noise equivalent temperature difference (NETD).

The Micro-Doppler effect is a special manifestation of the signatures and motion states of the target. It plays an important role in the target classification and recognition. However, the micro-Doppler effects of many objects in the environment, such as wind turbines, tower cranes, and air conditionings, etc., act as time-varying clutter to radar systems, causing false alarms and missed detections. In this paper, we investigate the recognition and mitigation problems of the micro-Doppler clutter (MDC) in radar systems. Firstly, the recognition model of the MDC is obtained using the support vector machine (SVM). Through this recognition model, we are able to know the distribution of the MDC in range-Doppler (RD) maps. Then, a spatial subspace projection algorithm is used to mitigate the MDC along the Doppler dimension in the RD map. The proposed algorithm is verified by both simulated and measured data.

In this paper, a novel algebraic closed-form method is proposed to estimate the position and velocity of a moving target in distributed multiple-input multiple-output radar systems with erroneous sensor locations by utilizing time delay and Doppler shift measurements. Unlike the existing methods that introduce nuisance parameters to build the pseudo-linear equations, the proposed method uses the singular value decomposition approach to establish new linear equations with regard to the target location with no nuisance parameters, and then derives a weighted least-squares (WLS) solution. To further improve the localization accuracy, the solution is refined through estimating the error by another WLS estimator. Based on the theoretical derivation and numerical simulations, the proposed estimator is demonstrated to be approximately unbiased and can attain the CRLB under small noise conditions. Moreover, the simulation results show that the proposed method achieves better target location accuracy than the state-of-the-art algorithms.

Multi sensor image fusion enhances the human visual perception and machine interpretation of the scene by integrating complementary and redundant information given by multi sensor data. In this paper, we proposed a multi sensor image fusion method that provides a high contrast fused image having no structural bias and which is more robust to different types of source images. These objectives are achieved through an intelligent ensemble of guided image filter, nonsubsampled shearlet transform, texture energy measures, and morphological operations. The proposed method is validated on medical, infrared-visible, and multi focus images. The qualitative and quantitative assessment proved the superiority of the proposed method compared to state of the art image fusion methods.

In multiple sensor extended target tracking problems, asynchronous measurements are inevitable, since sensors usually have distinct sampling rates and initial sampling times. This paper presents a new distributed extended target tracking algorithm with asynchronous measurements for multiple sensor scenarios. A distributed Bayesian estimation scheme for asynchronous measurements using random matrix framework is derived. We also proposed an effective implementation using particle filtering. Compressed Gaussian Mixture approximations of extended state distributions are exchanged and fused between neighbor sensors. The temporal evolution of elliptic extent parameters can be obtained explicitly in our algorithm. Simulations show reasonable performance with a significant reduction of communication costs for small size systems compared with the centralized algorithm.

We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector’s threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data.

In a context of buried objects detection using electromagnetic induction (EMI), dipole inversion refers to the estimation of object’s location and magnetic polarizability tensor from EMI and sensor’s positional data. In case of planar coil sensors, dipole inversion may become a surprisingly difficult and potentially ill-posed problem due to non-uniform distribution of directional sensitivities, nonlinear nature of target localization, as well as strong correlations between tensor elements and target’s depth. In this paper, we evaluate inversion performances of two categories of planar coil sensors; single-receiver sensors used in conventional metal detection (MD), and multi-receiver sensors aimed at metal characterization (MC). We use three different inversion methods; nonlinear least squares (NLS), HAP method featuring novel auxiliary source model for improved object localization, and differential evolution (DE). Comparative study is performed using synthetic EMI and sensor’s positional data under realistic scenarios involving different targets, depths and signal-to-noise ratios (SNRs). Our results suggest that relatively simple planar MC sensors clearly outperform conventional MD sensors, especially at greater depths. On average, DE method notably improves the invertibility of MD sensors, while a denser scan pattern may help to tackle lower SNR at greater depths.

Utilizing the bistatic range (BR) and bistatic range rate (BRR) obtained from time delay difference and Doppler shift difference, a moving target can be localized with the passive multistatic radar (PMR) network. This paper presents a two-stage closed-form method for target localization in PMR. Different from the conventional two-stage weighted least squares (TSWLS) methods, the proposed method employs the weighted spherical-interpolation method to obtain an initial estimate in the first stage and reduces the error in the initial estimate with the deviation refinement in the second stage. Theoretical analysis through mean-square error (MSE) shows the proposed method approaches the Cramér-Rao lower bound (CRLB) accuracy under the assumption of mild measurement noises. Simulation results validate the analytical results. The proposed method is also shown to achieve the accuracy improvement in target velocity estimate at relatively higher noise levels.

Joint torque sensing is an important technique for high-performance control of modern robotic systems, especially in an environment that requires man-machine collaboration. However, in many cases, traditional torque sensors are not suitable for robots because of the inevitable increase of joint flexibility and joint size. To address this problem, two novel methods are proposed to estimate torque of robotic joint with harmonic reducer via calibration of its existing flexibility without the need for any additional elastic elements. The first approach utilizes a new harmonic drive compliance model, which is more convenient for calibration and less dependent on the manufacturer’s parameters to estimate the output torque. The second method relies on a system based on a back-propagation (BP) neural network to fit the non-linear relationship among the output torque, motor current, and other information that can be obtained from double encoders mounted on motor-side and load-side. The two proposed methods were experimentally investigated and the results show that the estimated torque values were in good agreement with the measurements obtained using a commercial torque sensor. Finally, different suitable application scenarios are presented according to the specific performance of each technique.

Indoor localization using magnetic and inertial sensors has shown its effectiveness due to the pervasiveness of magnetic fields and independence from external infrastructure. However, when massive crowdsourced data are obtained in mass-market applications, new issues occur and degrade the magnetic matching (MM) performance. To alleviate these issues, an orientation-aided stochastic MM method is presented. First, orientation-aided magnetic fingerprints are applied to effectively improve fingerprint fidelity when massive data are used. Second, this paper characterizes crowdsourced magnetic fingerprints and reveals that the stochastic magnetic components may have different histograms, such as the symmetric, left-skewed, bimodal, and irregular ones. Based on such outcome, Gaussian distribution and histogram model based stochastic MM methods are presented to mitigate the degradation from stochastic magnetic components, which have not been involved in the existing MM methods such as nearest neighbor and dynamic time warping. Compared to the deterministic MM methods, this research provides a deeper insight into the use of magnetic fingerprints and more accurate MM and MM/dead-reckoning integrated localization solutions.

Orthogonal Frequency Division Multiplexing (OFDM) technique is obtained significant attention in radar applications for its interference resilience property. In this paper, Fractional Fourier Transformation (FRFT) and phase analysis techniques are proposed to enhance ranging accuracy of an OFDM Radar. A proof-of-concept radar is built and tested at 79 GHz and a range accuracy of $20~\mu \text{m}$ at 5 MHz measurement rate was measured. The range accuracy is 500 times higher than using fast Fourier transformation (FFT) method.

For many diseases, treatment is more effective in the early stage of a disease; hence detection of the early signs of a disease is highly significant. Biomechanical motion can be such an early indicator. This paper proposes a novel method for monitoring biomechanical motion based on electromagnetic sensing techniques. The proposed method has the advantages of being non-invasive, easy to perform, of low cost, and highly effective. Theoretical models are set up to model the sensor responses and wearable sensor systems are designed. Experiments are performed to monitor various biomechanical movements, including eye blinking frequency, finger/wrist bending level and frequency. From experiments, both the fast blinking behavior with an average frequency of ~1.1 Hz and the slow blinking behavior with an average frequency of 0.4 Hz can be monitored; various finger-bending status are identified, such as the fast finger-bending with an average frequency of ~1.5 Hz and the slow finger-bending with an average frequency of ~1/6 Hz. Both simulations and measurement results indicate that the proposed electromagnetic sensing method can be used for biomechanical movement detection and the system has the advantages of being easy to put on / take off, and without the need for direct contact between sensors and human body.

In this paper, a small, compact, and intelligent pediatric portable pacifier is proposed to measure and assess Non-Nutritive Sucking (NNS) activity in premature infants. A prototype of the system is implemented on a $21\times24$ mm 2 printed circuit board, which is small enough to be completely embedded on the back of all commercial pacifiers. The system portability allows it to be easily used in space-limited biomedical applications without dealing with any special electronic instruments or technical knowledge. The measured NNS data is stored on an on-board memory card in order to be analyzed by smartphones and computers. Experimental results confirmed that the proposed system is a proper tool for measuring and evaluating NNS data. The parameters extracted from the NNS signal represent crucial information on the physical growth, neural development, and integrity of the central nervous system of premature infants.

The minimum conveying velocity or the lowest point of pressure drop curve can reflect the characteristics of particles moving from deposition to suspension in pneumatic conveying. However, when the mass flow rate is low, the change of particles flow cannot be effectively reflected by the above methods. In this paper, based on a 4-arc electrode electrostatic sensor, with localised sensitivity, the detailed scale signals that represent the particle random motion are extracted by the combination of Empirical Mode Decomposition (EMD) and Hurst exponent. The differences in the particle random motion energy ratio (PRMER) of 4-channel electrode signals are used to characterize the transition of flow state. In the verification experiment by using a belt-style electrostatic velocity measured rig, the higher the velocity of rubber belt or the closer to the electrode, the higher the PRMERs of electrostatic signal. In the experiment of gas-solid two-phase flow rig, the PRMERs of 4-channel electrode signals are all increasing with the increase of superficial gas velocity, and the PRMERs of different electrodes are rather different at low superficial gas velocity ( ${V}_{g}$ ). When ${V}_{g}$ drops at the minimum pressure drop point ( ${P}_{{MPD}}$ ), the PRMERs of top/bottom and left/right electrodes begin to separate (e.g. the particle mass flow rates are 100 kg/h and 120 kg/h). Furthermore, when the solid mass flow rate is 80 kg/h, the proposed method can clearly characterize the transition of flow states in the pipeline, but the pressure drop phase diagram cannot.

Along with methods such as Hidden Markov Model and Linear Discriminant Analysis, environmental sensors including temperature, humidity or carbon dioxide are used to estimate indoor occupancy, which have various applications. Most of the previous studies have neglected the time-dependent character of the indoor occupancy information or real-time response of the system. In this paper, we propose to use low invasive, fast-sampling infrared array sensors to collect data from the actual scene and establish the Inhomogeneous Hidden Markov Model to capture the time dependence of occupancy for buildings occupancy estimation. First, to avoid raw sensor datas susceptibility to external influences, the non-negative matrix factorization is adopted to reduce the dimension of the raw matrix and eliminate interferences caused by environmental changes. Second, the Softmax Regression Model is used to calculate the emission probability matrix for clarifying the dynamic relationship between environmental parameters and indoor occupancy, which is weak. Third, the Forward algorithm and the Viterbi algorithm are applied to achieve online and off-line estimation. Experiments are made with real recorded data. Our performance results demonstrate that method we proposed is effective for indoor occupancy estimation.

Device-free or passive localization techniques allow positioning of targets, without requiring them to carry any form of transceiver or tag. In this paper, a novel device-free visible light positioning technique is proposed. It exploits the variation of the ambient light levels caused by a moving entity. The target is localized by employing a system of artificial potential fields associated with a set of photodiodes embedded into an indoor environment. The system does not require the existing lighting infrastructure to be modified. It also employs a novel calibration procedure that does not require labelled training data, thus significantly reducing the calibration cost. The developed prototype system is installed in three typical indoor environments consisting of a corridor, foyer, and laboratory and was able to attain median errors of 0.68m, 1.20m and 0.84m respectively. Through experimental results, the proposed VLP technique is benchmarked against an existing wireless RSSI-based device-free localization approach, and was able to attain a median error 0.63m lower than the wireless technique.

The yaw attitude of an unmanned aerial vehicle (UAV) is important for navigation applications. Magnetometers are attractive because they can directly resolve the yaw attitude of the UAV. Roboticists, however, often dismiss magnetometers because of the instrument’s susceptibility to unwanted magnetic sources, such as hard irons, soft irons and electric currents generated by the UAV’s powertrain. Dynamic current-induced magnetometer biases are especially hard to fix because of their time dependency. A hardware fix is to isolate or shield the current carrying wires of the powertrain from the magnetometer. However, for UAVs with weight and space restrictions, this solution may not be feasible. An alternative is to fix the errors using software. This work takes the software approach. Specifically, a model for the current-induced magnetometer bias as a function of the throttle command is established. Based on this model, an adaptive estimator is developed that determines the model parameters in realtime. The advantage of our method over existing techniques is the ability to handle rapid changes in throttle command, thanks to the inclusion of the bias model. Experiments show the estimator can compensate for the current-induced magnetometer bias across all throttle settings and yaw angles.

This work details an innovative method to search a complex enclosed area for a gamma-ray emitting material using a handheld gross-counts gamma detector. It is not a wide area search tool. It relies on triangulating a search area and using the Currie limit of detection to determine the presence of radioactive gamma-ray emitting materials. Results of experimental trials of the model indicate its effectiveness in detecting radiation sources in complex environments. This highlights its potential application in locating radiological material for nuclear security purposes. The method is currently 2-D but is being extended to 3-D.