Sensors

Unconventional oil and gas reservoirs have broad exploration and development prospects. Fracture parameters and effectiveness evaluation are two of the key tasks for the evaluation of these types of reservoirs. Array acoustic logging can be used for fracture evaluation to compensate for the deficiencies of the image logging fracture evaluation method. Therefore, to develop acoustic logging evaluation methods as well as nondestructive testing methods for fractures, experiments were conducted to study the shear wave transmission in fractured media. Experiment data demonstrate a good correlation between the shear wave attenuation coefficient and fracture width, and the shear wave attenuation coefficients rise logarithmically with the increase in the fracture width for all models with different porosities and distinct dip angles of fractures. The shear wave attenuation coefficient changes relatively faster with the fracture width when the fracture width is within 250 μm. In addition, the shear wave attenuation is affected by the core porosity and fracture dip angle. When the fracture width is constant, the shear wave attenuation caused by the 0° fracture is relatively larger and is obviously greater than that of the fractures at other angles, which is consistent with the existing experimental results. The results of this study can be used to guide further research on amplitude compensation methods for sonic signal transmission in fractured media and fracture evaluation methods. Full article

This paper aims at demonstrating the energy self-sufficiency of a LoRaWAN-based sensor node for monitoring environmental parameters exploiting energy harvesting directly coming from the artificial light used in indoor horticulture. A portable polycrystalline silicon module is used to charge a Li-Po battery, employed as the power reserve of a wireless sensor node able to accurately monitor, with a 1-h period, both the physical quantities most relevant for the application, i.e., humidity, temperature and pressure, and the chemical quantities, i.e., O₂ and CO₂ concentrations. To this aim, the node also hosts a power-hungry NDIR sensor. Two programmable light sources were used to emulate the actual lighting conditions of greenhouses, and to prove the effectiveness of the designed autonomous system: a LED-based custom designed solar simulator and a commercial LED light especially thought for plant cultivation purposes in greenhouses. Different lighting conditions used in indoor horticulture to enhance different plant growth phases, obtained as combinations of blue, red, far-red and white spectra, were tested by field tests of the sensor node. The energy self-sufficiency of the system was demonstrated by monitoring the charging/discharging trend of the Li-Po battery. Best results are obtained when white artificial light is mixed with the far-red component, closest to the polycrystalline silicon spectral response peak. Full article

To improve the user’s positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of ${\mathrm{g}}^2$o that fuses a Wi-Fi fingerprint and Bluetooth Low Energy (BLE) ranging technologies. In our model, the improvement in positioning can be formulated as a nonlinear least-squares optimization problem that a graph can represent. The graph regards users as nodes and our self-designed error functions between users as edges. In the graph, the nodes obtain the initial coordinates through Wi-Fi fingerprint positioning, and all error functions aggregate to a total error function to be solved. To improve the solution effect of the total error function and weaken the influence of measurement error, an information matrix, an edge selection principle, and a Huber kernel function are introduced. The Levenberg–Marquardt (LM) algorithm is used to solve the total error function and the affine transformation estimation is used for the drifting solution. Through experiments, the influence of the threshold in the Huber kernel function is explored, the relationship between the number of nodes in the graph and the optimization effect is analyzed, and the impact of the distribution of nodes is researched. The experimental results show improvements in the positioning accuracy of four common Wi-Fi fingerprint-matching algorithms: KNN, WKNN, GK, and Stg. Full article

We demonstrate a non-intrusive dynamic monitoring method of oil well flow based on distributed optical fiber acoustic sensing (DAS) technology and the turbulent vibration. The quantitative measurement of the flow rate is theoretically acquired though the amplitude of the demodulated phase changes from DAS based on the flow impact in the tube on the pipe wall. The experimental results show that the relationships between the flow rate and the demodulated phase changes, in both a whole frequency region and in a sensitive-response frequency region, fit the quadratic equation well, with a max R² of 0.997, which is consistent with the theoretical simulation results. The detectable flow rate is from 0.73 m³/h to 2.48 m³/h. The experiments verify the feasibility of DAS system flow monitoring and provide technical support for the practical application of the downhole flow measurement. Full article

Magnetic resonance (MR) imaging is an important computer-aided diagnosis technique with rich pathological information. The factor of physical and physiological constraint seriously affects the applicability of that technique. Thus, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct an MR image from the corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multi-conditional constraint generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging, respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we innovatively treat the real and generated MR image discrimination as object re-identification; cosine error fusing with original GAN loss is designed to enhance verisimilitude and textural features of the MR image. The experimental results with the challenging public CT-MR image dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation. Full article

Multiple sensors are often mounted in a complex manufacturing process to detect failures. Due to the high reliability of modern manufacturing processes, failures only happen occasionally. Therefore, data collected in practical manufacturing processes are extremely imbalanced, which often brings about bias of supervised learning models. Data collected by the multiple sensors can be regarded as multivariate time series or multi-sensor stream data. The high dimension of multi-sensor stream data makes building models even more challenging. In this study, a new and easy-to-apply data augmentation approach, namely, imbalanced multi-sensor stream data augmentation (IMSDA), is proposed for imbalanced learning. IMSDA can generate high quality of failure data for all dimensions. The generated data can keep the similar temporal property of the original multivariate time series. Both raw data and generated data are used to train the failure detection models, but the models are tested by the same real dataset. The proposed method is applied to a real-world industry case. Results show that IMSDA can not only obtain good quality failure data to reduce the imbalance level but also significantly improve the performance of supervised failure detection models. Full article

The uniaxial piezoelectric sensor was developed to overcome the problem of reflecting the output charge of the piezoelectric element as a combination of vectors in the three stress directions. The work performance of the uniaxial piezoelectric sensor under varying load patterns and load rates was investigated. The sensor was embedded in concrete to monitor stress, and its elastic modulus was used as the intermediate bridge to establish the correlation between the embedded sensor and the external sensor. Furthermore, a correction factor for the charge transformation strain was suggested to overcome the mismatching of the sensor’s medium and the concrete. Considering related circumstances, a new stress monitoring method based on a uniaxial piezoelectric sensor was proposed, which can achieve stress whole-process monitoring in concrete and confining stress monitoring in the reinforced concrete column. The results reveal that through the proposed method, the output charge curve of the sensor has a substantial overlap with the stress waveform and high fitting linearity. The work performance of the sensor was stable, and its sensitivity was not affected by loading rate and load pattern. The sensor was embedded in concrete and can coordinate with the concrete deformation. The correction factor of strain obtained by the sensor embedded in concrete was 1.07. The relationship between the charge produced by the embedded sensor and its external calibration sensitivity may be used to implement the whole process of stress monitoring in concrete. Full article

Crowded event entrances could threaten the comfort and safety of pedestrians, especially when some pedestrians push others or use gaps in crowds to gain faster access to an event. Studying and understanding pushing dynamics leads to designing and building more comfortable and safe entrances. Researchers—to understand pushing dynamics—observe and analyze recorded videos to manually identify when and where pushing behavior occurs. Despite the accuracy of the manual method, it can still be time-consuming, tedious, and hard to identify pushing behavior in some scenarios. In this article, we propose a hybrid deep learning and visualization framework that aims to assist researchers in automatically identifying pushing behavior in videos. The proposed framework comprises two main components to generate motion information maps: (i) deep optical flow and wheel visualization; (ii) A combination of an EfficientNet-B0-based classifier and a false reduction algorithm for detecting pushing behavior at the video patch level. In addition to the framework, we present a new patch-based approach to enlarge the data and alleviate the class imbalance problem in small-scale pushing behavior datasets. Experimental results (using real-world ground truth of pushing behavior videos) demonstrate that the proposed framework achieves an 86% accuracy rate. Moreover, the EfficientNet-B0-based classifier outperforms baseline CNN-based classifiers in terms of accuracy. Full article

This paper presents a multi-gain radio frequency (RF) front-end low noise amplifier (LNA) utilizing a multi-core based on the source degeneration topology. The LNA can cover a wide range of input and output frequency matching by using a receiver (RX) switch at the input and a capacitor bank at the output of the LNA. In the proposed architecture here, to avoid the saturation of RX chain, 12 gain steps including positive, 0 dB, and negative power gains are controlled by a mobile industry processor interface (MIPI). The multi-core architecture offers the ability to control the power consumption over different gain steps. In order to avoid the phase discontinuity, the negative gain steps are provided using an active amplification and T-type attenuation path that keeps the phase discontinuity below ±5 degrees between two adjacent power gain steps. Using the multi-core structure, the power consumption is optimized in different power gains. The structure is enhanced with the adaptive variable cores and reactance parameters to maintain different power consumption for different gain steps and remain the output matching in an acceptable operating range. Furthermore, auxiliary linearization circuitries are added to improve the input third intercept point (IIP3) performance of the LNA. The chip is fabricated in 65 nm complementary metal-oxide semiconductor (CMOS) silicon on insulator (SOI) process and the die area is 0.308 mm². The proposed architecture achieves the IIP3 performance of −10.2 dBm and 8.6 dBm in the highest and lowest power gains, which are 20.5 dB and −11 dB, respectively. It offers the noise figure (NF) performance of 1.15 dB in the highest power gain while it reaches 14 dB when the power gain is ˗11 dB. The LNA consumes 16.8 mA and 1.33 mA current from a 1 V power supply that is provided by an on-chip low-dropout (LDO) when it operates at the highest and lowest gains, respectively. Full article

The unique arrangement of the heavy-ion beam diagnostic in ISTTOK enables one to measure the evolution of temperature, density and pressure-like profiles in normal and AC discharges. The fast chopping beam technique provided the possibility to reduce the noise on the measurements of the plasma pressure-like profile and for the precise control of the plasma column position in real time. The consequent improvements in S/N levels allowed the observation of the effects of runaway beam magnetic energy conversion into plasma local heating. In addition, it made it possible to follow the evolution of the quiescent plasma maintained during AC transitions when the plasma current is null. The use of a new operation mode in the cylindrical energy analyzer provided an improved resolution up to five times in determining the fluctuations of the plasma potential as compared to the normal operation mode. Such analyzer is extremely compact (250 mm × 250 mm × 120 mm) and provides a unique geometry in order to cover the whole plasma diameter. The detector configuration choice gives the possibility for the simultaneous measurements of plasma poloidal magnetic field, plasma pressure-like and plasma potential profiles together with their fluctuations. Full article

This work presents research on unique optofluidic systems in the form of air channels fabricated in PDMS and infiltrated with liquid crystalline material. The proposed LC:PDMS structures represent an innovative solution due to the use of microchannel electrodes filled with a liquid metal alloy. The latter allows for the easy and dynamic reconfiguration of the system and eliminates technological issues experienced by other research groups. The paper discusses the design, fabrication, and testing methods for tunable LC:PDMS structures. Particular emphasis was placed on determining their properties after applying an external electric field, depending on the geometrical parameters of the system. The conclusions of the performed investigations may contribute to the definition of guidelines for both LC:PDMS devices and a new class of potential sensing elements utilizing polymers and liquid crystals in their structures. Full article

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting indi vidual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM. Full article

This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application. Full article

The core body temperature tends to decrease under general anesthesia. Consequently, monitoring the core body temperature during procedures involving general anesthesia is essential to ensure patient safety. In veterinary medicine, rectal temperature is used as an indicator of the core body temperature, owing to the accuracy and convenience of this approach. Some previous studies involving craniotomy reported differences between the brain and core temperatures under general anesthesia. However, noninvasive imaging techniques are required to ascertain this because invasive brain temperature measurements can cause unintended temperature changes by inserting the temperature sensors into the brain or by performing the surgical operations. In this study, we employed in vivo magnetic resonance thermometry to observe the brain temperatures of patients under general anesthesia using the proton resonance frequency shift method. The rectal temperature was also recorded using a fiber optic thermometer during the MR thermometry to compare with the brain temperature changes. When the rectal temperature decreased by 1.4 ± 0.5 °C (mean ± standard deviation), the brain temperature (white matter) decreased by 4.8 ± 0.5 °C. Furthermore, a difference in the temperature reduction of the different types of brain tissue was observed; the reduction in the temperature of white matter exceeded that of gray matter mainly due to the distribution of blood vessels in the gray matter. We also analyzed and interpreted the core temperature changes with the body conditioning scores of subjects to see how the body weight affected the temperature changes. Full article

This paper deals with the problem of vertical oscillation of rail and road vehicles under symmetrical and asymmetrical loading and symmetrical and asymmetrical kinematic excitation. The term asymmetry is understood as the asymmetric distribution of vehicle mass and elastic and dissipative elements with respect to the axes of geometric symmetry, including asymmetric kinematic excitation. The various models used (spatial, planar, quarter-plane) are discussed and their analytical solutions are outlined. The theory of the spatial model is applied to the chassis of a model railway vehicle. The basic relations for the calculation of the equations of motion of this vehicle are given. In the next section, the experimental solution of a four-axle platform rail car is described and the measurements of vertical displacement and accelerations when crossing wedges (representing unevenness) are given. Full article

Electronic Health Records (EHR) are the healthcare sector’s core digital strategy meant to improve the quality of care provided to patients. Despite the benefits afforded by this digital transformation initiative, adoption among healthcare organizations has been slower than desired. The sheer volume and sensitive nature of patient records compel these organizations to exercise a healthy amount of caution in implementing EHR. Cyberattacks have also increased the risks associated with non-optimal EHR implementations. An influx of high-profile data breaches has plagued the sector during the COVID-19 pandemic, which put the spotlight on EHR cybersecurity. One objective of this research project is to aid the acceleration of EHR adoption. Another objective is to ensure the robustness of the system to resist malicious attacks. For the former, a systematic review was used to unearth all the possible causes why the adoption of EHR has been anemic. In this paper, sixty-five existing proposed EHR solutions were analyzed and it was found that there are fourteen major challenges that need to be addressed to reduce friction and risk for health organizations. These were privacy, security, confidentiality, interoperability, access control, scalability, authentication, accessibility, availability, data storage, data ownership, data validity, data integrity, and ease of use. We propose EHRChain, a new framework that tackles all the listed challenges simultaneously to address the first objective while also being designed to achieve the second objective. It is enabled by dual-blockchains based on Hyperledger Sawtooth to allow patient data decentralization via a consortium blockchain and IPFS for distributed data storage. Full article

Bio-inspired transverse brachiation robots mimic the movement of human climbers as they traverse along ledges on a vertical wall. The constraints on the locomotion of these robots differ considerably from those of conventional brachiation robots due primarily to the need for robust hand-eye coordination. This paper describes the development of a motion control strategy for a brachiation robot navigating between wall ledges positioned on a level plane or at different elevations. We based our robot on a four-link arm-body-tail system performing a four-phase movement, including a release phase, body reversal phase, swing-up phase, and grasping phase. We designed a gripper that uses passive wrist joint motion to grasp the ledge during the tail swing. We also developed a dynamic model by which to coordinate the swing-up movement, define the phase switching conditions, and time the grasping action of the grippers. In experiments, the robot proved highly effective in traversing between wall ledges of the same or different elevations. Full article

To achieve multi-gas measurements of quartz-enhanced photoacoustic spectroscopy (QEPAS) sensors under a frequency-division multiplexing mode with a narrow modulation frequency interval, we report a frequency-domain detection method. A CH₄ absorption line at 1653.72 nm and a CO₂ absorption line at 2004.02 nm were investigated in this experiment. A modulation frequency interval of as narrow as 0.6 Hz for CH₄ and CO₂ detection was achieved. Frequency-domain 2f signals were obtained with a resolution of 0.125 Hz using a real-time frequency analyzer. With the multiple linear regressions of the frequency-domain 2f signals of various gas mixtures, small deviations within 2.5% and good linear relationships for gas detection were observed under the frequency-division multiplexing mode. Detection limits of 0.6 ppm for CH₄ and 2.9 ppm for CO₂ were simultaneously obtained. With the 0.6-Hz interval, the amplitudes of QEPAS signals will increase substantially since the modulation frequencies are closer to the resonant frequency of a QTF. Furthermore, the frequency-domain detection method with a narrow interval can realize precise gas measurements of more species with more lasers operating under the frequency-division multiplexing mode. Additionally, this method, with a narrow interval of modulation frequencies, can also realize frequency-division multiplexing detection for QEPAS sensors under low pressure despite the ultra-narrow bandwidth of the QTF. Full article

Source localization with a passive sensors array is a common topic in various areas. Among the popular source localization algorithms, the compressive sensing (CS)-based method has recently drawn considerable interest because it is a high-resolution method, robust with coherent sources and few snapshots, and applicable for mixed near-field and far-field source localization. However, the CS-based methods rely on the dense grid to ensure the required estimation precision, which is time-consuming and impractical. This paper applies the complex variational mode decomposition (CVMD) to source localization. Specifically, the signal model of the source localization problem is similar to the time-domain frequency-modulated signal model. Motivated by this, we extend CVMD, initially designed for nonstationary time-domain signal analysis, to array signal processing. The decomposition results of the array measurements can correspond to the potential sources at different locations. Then, the sources’ direction and range can be estimated by model fitting with the decomposed subsignals. The simulation results show that the proposed CVMD-based method can locate the pure far-field, pure near-field, mixed far-field, and near-field sources. Notably, it can yield high-resolution localization for the coherent sources with one single snapshot with low computing time. Full article