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This study was aimed at analyzing data and evaluating the accuracy of a new subcutaneous continuous glucose-monitoring device by referring to finger-pricking measurement. The data were obtained from 7 diabetic patients. An improved implanted flex sensor was used to measure the interstitial glucose concentration every 3 min within 6 days, and five finger-pricking samples were collected every day for comparison. A periodic glucose change happened every day. 2.45% of CGM values were in the hypoglycemic range (180 mg/dl). The interstitial glucose concentrations (n=204) were well linearly correlated with the capillary glucose concentrations (r=0.94, P<0.01), but a delay occurred between 10 and 40 minutes within two measurements (CGM later), and the individual average delay times were relatively close to 24.6 minutes. Clarke’s error grid analysis showed that 86.7% of the points fell in zone A, 12.7% fell in zone B, and only 0.6% fell in zone D. An overall MARD was 10.22%. This study demonstrated that the CGM was accurate and highly reliable; thus, it constituted a new device for continuous glucose monitoring in diabetic patients.

Images, as one of the important carriers for information exchange, play an important role in work and daily life. Image encryption technology has received a lot of attention, and varieties of encryption technologies for images have emerged. Early image encryption technologies have shortcomings such as simple algorithm structure, small key space, and poor resistance to plaintext attacks. These algorithms have been unable to meet the needs of information security at this stage. Based on the wavelet algorithm, chaos algorithm, and cyclic encryption algorithm, combined with frequency domain encryption and spatial domain encryption, a digital image encryption algorithm based on adaptive wavelet is proposed in this paper. In terms of frequency, this paper enhances the adaptive ability of the wavelet algorithm by convex optimization-particle swarm optimization (PSO). At the same time, the chaotic algorithm is used to scramble the low-frequency coefficients. In the aspect of airspace, this paper uses the block-encryption adaptive encryption algorithm to rescramble the wavelet-reconstructed image. And the SHA-1 algorithm is introduced into the plaintext image generation key sequence as a cyclic index value for cyclic encryption. The key composition of the encryption algorithm proposed in this paper is complex, and the idea of a one-time pad is used, which makes the improved algorithm increase the key space and improve the ability to resist the choice of plaintext attack. In each cyclic encryption of the airspace, mutual encryption is performed by image subblocks, and the algorithm adaptability is improved. Through the experimental test of statistical characteristics, key space, and key sensitivity, the encryption performance of the algorithm is verified, and it has strong antiattack and interference ability. It is a relatively secure encryption algorithm.

A wireless sensor network consists of numerous low-power microsensor devices that can be deployed in a geographical area for remote sensing, surveillance, control, and monitoring applications. The advancements of wireless devices in terms of user-friendly interface, size, and deployment cost have given rise to many smart applications of wireless sensor networks (WSNs). However, certain issues like energy efficiency, long lifetime, and communication reliability restrict their large scale utilization. In WSNs, the cluster-based routing protocols assist nodes to collect, aggregate, and forward sensed data from event regions towards the sink node through minimum cost links. A clustering method helps to improve data transmission efficiency by dividing the sensor nodes into small groups. However, improper cluster head (CH) selection may affect the network lifetime, average network energy, and other quality of service (QoS) parameters. In this paper, a multiobjective clustering strategy is proposed to optimize the energy consumption, network lifetime, network throughput, and network delay. A fitness function has been formulated for heterogenous and homogenous wireless sensor networks. This fitness function is utilized to select an optimum CH for energy minimization and load balancing of cluster heads. A new hybrid clustered routing protocol is proposed based on fitness function. The simulation results conclude that the proposed protocol achieves better efficiency in increasing the network lifetime by 63%, 26%, and 10% compared with three well-known heterogeneous protocols: DEEC, EDDEEC, and ATEER, respectively. The proposed strategy also attains better network stability than a homogenous LEACH protocol.

In this paper, a new application of the optical mouse sensor is presented. The optical mouse is used as a main low-cost infrared vision system of a new proposal of a head-mounted human-computer interaction (HCI) device controlled by eye movements. The default optical mouse sensor lens and illumination source are replaced in order to improve its field of view and capture entire eye images. A complementary 8-bit microcontroller is used to acquire and process these images with two optimized algorithms to detect forced eye blinks and pupil displacements which are translated to computer pointer actions. This proposal introduces an inexpensive and approachable plug and play (PnP) device for people with severe disability in the upper extremities, neck, and head. The presented pointing device performs standard computer mouse actions with no extra software required. It uses the human interface device (HID) standard class of the universal serial bus (USB) increasing its compatibility for most computer platforms. This new device approach is aimed at improving comfortability and portability of the current commercial devices with simple installation and calibration. Several performance tests were done with different volunteer users obtaining an average pupil detection error of 0.34 pixels with a successful detection in 82.6% of all mouse events requested by means of pupil tracking. This research was funded by Indra, Accessibility Chair, 2017. This research also was supported by the Government of Catalonia (Comissionat per a Universitats i Recerca, Departament d’Innovació, Universitats i Empresa) and the European Social Fund.

This paper describes the characterization of the light hole, also known as the lumen, in implanted stents affected by restenosis processes using bioimpedance (BI) as a biomarker. The presented approach will enable real-time monitoring of lumens in implanted stents. The basis of the work hereby reported is the fact that neointimal tissues involved in restenosis can be detected and measured through their impedance properties, namely, conductivity and permittivity. To exploit these properties, a 4-electrode setup for BI measurement is proposed. This setup allows study of the influence of the various tissues involved in restenosis fat, muscle, fibre, and endothelium, together with the blood, on the BI value at several frequencies. In addition, BI simulation tests were performed using the electric physics module available in COMSOL Multiphysics®. Interestingly, fat constitutes the most influential layer on the value of impedance (measured in kΩ/μm—magnitude change per micrometre of lumen occlusion). A case study using a standard stent is also presented. In this study, where the involved tissues and blood were simultaneously considered, we conducted an analysis for stable and vulnerable plaques in restenosis test situations. In this regard, the proposed method is useful to test the stent obstruction and detect potential dangerous cases due to nonstable fat accumulation.

In this paper, g-C3N4-WO3 composite materials were prepared by hydrothermal processing. The composites were characterized by means of X-ray powder diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and N2 adsorption-desorption, respectively. The gas sensing properties of the composites were investigated. The results indicated that the addition of appropriate amount of g-C3N4 to WO3 could improve the response and selectivity to acetone. The sensor based on 2 wt% g-C3N4-WO3 composite showed the best gas sensing performances. When operating at optimum temperature of 310°C, the responses to 1000 ppm and 0.5 ppm acetone were 58.2 and 1.6, respectively, and the ratio of the S1000 ppm acetone to S1000 ppm ethanol reached 3.7.

Rapeseed cleaning loss is an important factor to weigh rapeseed combine harvester performance. With the development of sensor technology, establishing a rapeseed cleaning loss monitoring system can be interesting and necessary. However, the rapeseed combine harvester made in China is always with larger vibration, which is not beneficial for sensor monitoring accuracy. Experiment results indicate that inertia force from vibrating sieve unbalanced motion has the greatest influence on the monitoring accuracy of the sensor in its installation position. To reduce the effect of vibration on monitoring accuracy of the sensor, the vibration absorption and isolation structure for cleaning loss sensor has been designed. Moreover, the vibration isolation design was performed on the basis of vibration suppression. The vibration isolation rubber was designed to isolate the vibration at the joint of the sensor with the frame. The experimental results shown that the above design can reduce the vibration disturbance and highlight the grain collision signal greatly, which is beneficial for improving the sensor discrimination ability.

Digestive diseases are one of the common broiler diseases that significantly affect production and animal welfare in broiler breeding. Droppings examination and observation are the most precise techniques to detect the occurrence of digestive disease infections in birds. This study proposes an automated broiler digestive disease detector based on a deep Convolutional Neural Network model to classify fine-grained abnormal broiler droppings images as normal and abnormal (shape, color, water content, and shape&water). Droppings images were collected from 10,000 25-35-day-old Ross broiler birds reared in multilayer cages with automatic droppings conveyor belts. For comparative purposes, Faster R-CNN and YOLO-V3 deep Convolutional Neural Networks were developed. The performance of YOLO-V3 was improved by optimizing the anchor box. Faster R-CNN achieved 99.1% recall and 93.3% mean average precision, while YOLO-V3 achieved 88.7% recall and 84.3% mean average precision on the testing data set. The proposed detector can provide technical support for the detection of digestive diseases in broiler production by automatically and nonintrusively recognizing and classifying chicken droppings.

Hilly areas necessitate a field road vehicle with high automation to reduce the amount of labor required to transport agricultural products and to increase productivity. In this paper, an adaptive integrated navigation method (combining global navigation satellite system (GNSS) and inertial navigation system (INS)) and path tracking control strategy of field road vehicles are studied in view of the problems of frequent GNSS outages and high automatic control precision requirement in hilly areas. An indirect Kalman filter (KF) is designed for the GNSS/INS information fusion. A modified method for calculating the KF adaptive factor is proposed to effectively suppress the divergence of the KF and a threshold judgement method to abandon the abnormal GNSS measurement is proposed to deal with GNSS interruptions. To achieve automated driving, a five-layer fuzzy neural network controller, which takes the lateral deviation, heading deviation, and path curvature as input and the steering angle as output, is proposed to control vehicle autonomous tracking of the navigation trajectory accurately. The proposed system was evaluated through simulation and experimental tests on a field road. The simulation results showed that the adjusted KF fusion algorithm can effectively reduce the deviation of a single GNSS measurement and improve the overall accuracy. The test results showed the maximum deviation of the actual travel trajectory from the expected trajectory of the vehicle in the horizontal direction was 12.2 cm and the average deviation was 5.3 cm. During GNSS outages due to obstacles, the maximum deviation in the horizontal direction was 12.7 cm and the average deviation was 6.1 cm. The results show that the designed GNSS/INS integrated navigation system and trajectory tracking control strategy can control a vehicle automatically while driving along a field road in a hilly area.

D-S evidence theory is widely used in data fusion. However, the result of Dempster’s combination rule is not efficient and in highly conflicting situation. Though the existing methods have been proved efficient to deal with conflict in some applications, the indirect conflict among evidence is neglected to some degree. To solve this problem, a new method is proposed based on decision-making trial and evaluation laboratory (DEMATEL) and the belief correlation coefficient in this paper. The application in target recognition illustrates the efficiency of the proposed method. Compared with Dempster’s rule, averaging method and weighted averaging method, etc., the results obtained by the proposed method have better performance. The main reason is that the indirect conflict is well addressed in the proposed method.

A novel, efficient, and accurate method to detect gear defects under a complex background during industrial gear production is proposed in this study. Firstly, we first analyzed image filtering and smoothing techniques, which we used as a basis to develop a complex background-weakening algorithm for detecting the microdefects of gears. Subsequently, we discussed the types and characteristics of gear manufacturing defects. Under the complex background of image acquisition, a new model S-YOLO is proposed for online detection of gear defects, and it was validated on our experimental platform for online gear defect detection under a complex background. Results show that S-YOLO has better recognition of microdefects under a complex background than the YOLOv3 target recognition network. The proposed algorithm has good robustness as well. Code and data have been made available.

Real-time kinematic (RTK) technique is important for mapping applications requiring short measure time, the distance between rover and base station, and high accuracy. There are several RTK methods used today such as the traditional RTK, long base RTK (LBRTK), network RTK (NRTK), and precise point positioning RTK (PPP-RTK). NRTK and LBRTK are popular with the advantage of the distance, the time, and accuracy. In the present study, the NRTK and LBRTK measurements were compared in terms of accuracy and distance in a test network with 6 sites that was established between 5 and 60 km. Repetitive NRTK and LBRTK measurements were performed on 6 different days in 2015-2017-2018 and additionally 4 campaigns of repetitive static measurements were carried out in this test network. The results of NRTK and LBRTK methods were examined and compared with all relevant aspects by considering the results of the static measurements as real coordinates. The study results showed that the LBRTK and NRTK methods yielded similar results at base lengths up to 40 km with the differences less than 3 cm horizontally and 4 cm vertically.

Using downhole annular electromagnetic flow measurement to obtain annular flow in real-time is a foundation of microflow control drilling technology. The existing annular flow electromagnetic measurement method based on low-frequency rectangular excitation is affected by slurry interference and formation fluid invasion, which results in large noise generated on the electrode output signal. These noise causes the instability of the flow measurement system output and lower accuracy. Double-frequency rectangular wave excitation has the advantages of excellent zero-point stability attributed to low-frequency rectangular wave excitation and fast response speed with great ability to suppress slurry interference. First, the double-frequency rectangular wave excitation for annular flow electromagnetic measurement is researched, and its corresponding electromagnetic induction signal process is investigated. In order to verify the feasibility of downhole annular electromagnetic flow measurement, a flow verification platform for comparison of standard and detected parameters is established for simulation experiment, and the ability to suppress slurry interference and response speed of the technology in downhole slurry flow measurement are analyzed. The test results show that the downhole annular electromagnetic flow measurement based on double-frequency rectangular wave excitation can not only satisfy the requirements of regular electromagnetic flow measurement but also suppress the annular slurry interference effectively.

[EN] Pharmacological agents are often used to induce labor. Failed inductions are associated with unnecessarily long waits and greater maternal-fetal risks, as well as higher costs. No reliable models are currently able to predict the induction outcome from common obstetric data (area under the ROC curve (AUC) between 0.6 and 0.7). The aim of this study was to design an early success-predictor system by extracting temporal, spectral, and complexity parameters from the uterine electromyogram (electrohysterogram (EHG)). Different types of feature sets were used to design and train artificial neural networks: Set_1: obstetrical features, Set_2: EHG features, and Set_3: EHG+obstetrical features. Predictor systems were built to classify three scenarios: (1) induced women who reached active phase of labor (APL) vs. women who did not achieve APL (non-APL), (2) APL and vaginal delivery vs. APL and cesarean section delivery, and (3) vaginal vs. cesarean delivery. For Scenario 3, we also proposed 2-step predictor systems consisting of the cascading predictor systems from Scenarios 1 and 2. EHG features outperformed traditional obstetrical features in all the scenarios. Little improvement was obtained by combining them (Set_3). The results show that the EHG can potentially be used to predict successful labor induction and outperforms the traditional obstetric features. Clinical use of this prediction system would help to improve maternal-fetal well-being and optimize hospital resources., This work received financial support from the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R and RTI2018-094449-A-I00), Universitat Politècnica de València VLC/Campus (UPV-FE-2018-B02), Generalitat Valenciana (GV/2018/104), and Bial S.A.

Simulated moving bed (SMB) chromatographic separation technology is a new adsorption separation technology with strong separation ability. Based on the principle of the adaptive neural fuzzy inference system (ANFIS), a soft sensing modeling method was proposed for realizing the prediction of the purity of the extract and raffinate components in the SMB chromatographic separation process. The input data space of the established soft sensor model is divided, and the premise parameters are determined by utilizing the meshing partition method, subtractive clustering algorithm, and fuzzy C-means (FCM) clustering algorithm. The gradient, Kalman, Kaczmarz, and PseudoInv algorithms were used to optimize the conclusion parameters of ANFIS soft sensor models so as to predict the purity of the extract and raffinate components in the SMB chromatographic separation process. The simulation results indicate that the proposed ANFIS soft sensor models can effectively predict the key economic and technical indicators of the SMB chromatographic separation process.

In the study, ultrasonic longitudinal mode guided waves were employed to detect defects in elbowed tubes (without welds) with a diameter of 10 mm. Finite element simulation results highlighted that the emitted L(0,1) mode guided waves experienced strong reflection and mode conversion at the elbow region to generate F(1,1) mode, followed by slow and weak F(2,1) mode. The guided wave reflected from the elbow with a through-wall defect was manifested as two overlapped wave packets, which were good indicators of a defective elbow. To conduct L(0,1) mode guided waves inspection on the small-diameter elbowed tubes, a novel tailored squirrel-cage magnetostrictive sensor was employed in the experiment. The new sensor employed the configuration of segmental iron-cobalt strips and small-size permanent magnet arrays. The entire sensor is composed of two identical C-shaped sensor elements and can be recycled and installed conveniently. Experimental results obtained from healthy and defective tubes were consistent with the conclusions obtained from finite element simulations. An artificial through-wall defect at the elbow and a notch defect at the straight part of the tube could be simultaneously detected by L(0,1) mode guided waves through comparing experimental signals with simulation results.

There are a large number of insulators on the transmission line, and insulator damage will have a major impact on power supply security. Image-based segmentation of the insulators in the power transmission lines is a premise and also a critical task for power line inspection. In this paper, a modified conditional generative adversarial network for insulator pixel-level segmentation is proposed. The generator is reconstructed by encoder-decoder layers with asymmetric convolution kernel which can simplify the network complexity and extract more kinds of feature information. The discriminator is composed of a fully convolutional network based on patchGAN and learns the loss to train the generator. It is verified in experiments that the proposed method has better performances on mIoU and computational efficiency than Pix2pix, SegNet, and other state-of-the-art networks.

A novel electret sensor, made from a polytetrafluoroethylene (PTFE) foil, is proposed for monitoring of radon concentration in closed spaces. A triode air system was implemented to form the electret foil. In order to calibrate the electret sensor, the ionization chamber with the electret embedded at its bottom, was placed in an emanation chamber containing radon of known concentration. The change in the effective charge density of the electret resulting from the interaction of the electret sensor with ionizing radiation, make it possible to determine the degree of its response to the radon concentration in the examined environment. The electric charge of the electret sensor was evaluated at the beginning and at the end of the concentration measurement using a field chopper. The measurement of the value of the electric field between the electret and the reference electrode directly corresponds to the effective surface charge density of the electret. The measuring system was calibrated in values of the electret equivalent voltage of Uz. In order to assess the electret properties, both the thermostimulated charge relaxation (TSQ) method and the thermostimulated discharge current (TSDC) method were used to evaluate corresponding parameters. These investigations led to the optimization of the electret formation process for the maximum sensitivity to the effects of alpha particles resulting from radon radioactive decay.

This research discusses the polymer optical fiber sensor for respiratory measurements. The infrared LED that produces light will propagate along the polymer optical fiber which will be received by the phototransistor and the differential amplifier. The output voltage in the form of an analog signal will be converted to a digital signal by the Arduino Uno microcontroller and displayed on the computer. The polymer optical fiber sensor is installed on the corset using a variety of configuration (straight, sinusoidal, and spiral), placed in the abdomen, and a variety of positions (abdomen, chest, and back) using only a spiral configuration. While doing the inspiration, the stomach will be enlarged so that the optical fiber sensor will have strain. The strain will cause loss of power, the resulting light intensities received by the phototransistor are reduced, and the output voltage on the computer decreases. The result shows that the highest voltage amplitudes were in the spiral configuration placed in the abdominal position for slow respiration measurements with the highest range, sensitivity, and resolution which are 0.119 V, 0.238 V/s, and 0.004 s, respectively. The advantages of our work are emphasized on measurement system simplicity, low cost, easy fabrication, and handy operation and can be connected with the Arduino Uno microcontroller and computer.

Because of the complexity of the environment and the limited resources of nodes, there will be an imbalanced energy consumption and a short life among nodes in the wireless sensor network. In this paper, by introducing the concept of game theory and supermodel game theory, we solve the challenge of a wireless sensor network topology control method based on cross-layer information design. The cross-layer information such as node degree, network connectivity, and MAC layer interference is integrated into the design of utility function to establish a new topological game model. Then, based on this topology control model, we propose a cross-layer optimized energy-balanced topology control algorithm (COETC). Compared with other algorithms, our COETC algorithm not only guarantees the network connectivity and robustness while reducing the required node transmitting power but also achieves good energy balance and high energy efficiency. Ultimately, our method effectively prolongs network lifetime and improves network performance.

Acoustic emission (AE) is an effective nondestructive evaluation method for assessing damage in materials; however, few works in the literature have focused on one quantification method of damage in concrete under fatigue loading by using AE for characterizing the entire three main deterioration behaviors simultaneously. These deterioration behaviors include Young’s modulus degradation, fatigue total strain, and residual strain development. In this work, an AE quantification method of fatigue damage in concrete was developed, by combining AE and a fiber bundle-based statistical damage model (fiber bundle-irreversible chain model). By establishing a relationship between normalized AE counts and the damage variable based on the fiber bundle-irreversible chain model, the method was proposed. Additionally, this method was verified against the experimental results. It is able to capture the mechanisms of damage accumulation and characterize the three deterioration behaviors simultaneously.

A new fluorescence probe L was rationally designed and synthesized for the recognition of Cu2+ ions by the combination of coumarin hydrazide and 2-acetylpyrazine. The photochemical properties and selectivity of L for Cu2+ ions in a CH3CN/HEPES (3 : 2, v/v) buffer were investigated by UV-vis absorption and fluorescence emission spectra. A highly selective and sensitive response of L for Cu2+ ions over other competing metal ions was observed with limit of detection in 3 μM. The coordination stoichiometry of L to Cu2+ ions was determined to be 1 : 1 by the UV-vis absorption spectrum, the fluorescence titrations, and density functional theory (DFT) calculations. Moreover, L was applied successfully for recognition of intracellular Cu2+ ions in living cells.

This paper reviews the practical process of designing the electrodes on electroless immersion gold-plated test strips for glucose measurement. We have found that great care must be taken on determining the width and length of the working and reference electrodes, to yield the optimum performance on electroless immersion gold-plated glucose test strips for glucose measurement. Preferably, a width of at least 0.6 mm and a length of at least 24 mm are recommended to prevent variance in electrode resistance during mass production of electroless immersion gold-plated glucose test strips, in order to enhance the accuracy of glucose measurement. This paper also recommended that, if two different geometric sizes of test strips are going to be used on the same glucose meter, both test strips must use different lengths and widths on the working and reference electrodes to yield the same electrical resistance and hence similar electrochemical performance.

HF-band radio-frequency identification (RFID) is a robust identification system that is rarely influenced by objects in the robot activity area or by illumination conditions. An HF-band RFID system is capable of facilitating a reasonably accurate and robust self-localization of indoor mobile robots. An RFID-based self-localization system for an indoor mobile robot requires prior knowledge of the map which contains the ID information and positions of the RFID tags used in the environment. Generally, the map of RFID tags is manually built. To reduce labor costs, the simultaneous localization and mapping (SLAM) technique is designed to localize the mobile robot and build a map of the RFID tags simultaneously. In this study, multiple HF-band RFID readers are installed on the bottom of an omnidirectional mobile robot and RFID tags are spread on the floor. Because the tag detection process of the HF-band RFID system does not follow a standard Gaussian distribution, extended Kalman filter- (EKF-) based landmark updates are unsuitable. This paper proposes a novel SLAM method for the indoor mobile robot with a non-Gaussian detection model, by using the particle smoother for the landmark mapping and particle filter for the self-localization of the mobile robot. The proposed SLAM method is evaluated through experiments with the HF-band RFID system which has the non-Gaussian detection model. Furthermore, the proposed SLAM method is also evaluated by a range and bearing sensor which has the standard Gaussian detection model. In particular, the proposed method is compared against two other SLAM methods: FastSLAM and SLAM methods utilize particle filter for both the landmark updating and robot self-localization. The experimental results show the validity and superiority of the proposed SLAM method.

In this paper, an energy-efficient control solution for an Unmanned Ground Vehicle (UGV) in a Wireless Sensor Network is proposed. This novel control approach integrates periodic event-triggered control, packet-based control, time-varying dual-rate Kalman filter-based prediction techniques, and dual-rate control. The systematic combination of these control techniques allows the UGV to track the desired path preserving performance properties, despite (i) existing scarce data due to the reduced usage of the wireless sensor device, which results in less number of transmissions through the network and, hence, bandwidth and battery saving; (ii) appearance of some wireless communication problems such as time-varying delays, packet dropouts, and packet disorder; and (iii) coping with a realistic scenario where external disturbance and sensor noise can arise. The main benefits of the control solution are illustrated via simulation.

Many applications rely on MEMS microphone arrays for locating sound sources prior to their execution. Those applications not only are executed under real-time constraints but also are often embedded on low-power devices. These environments become challenging when increasing the number of microphones or requiring dynamic responses. Field-Programmable Gate Arrays (FPGAs) are usually chosen due to their flexibility and computational power. This work intends to guide the design of reconfigurable acoustic beamforming architectures, which are not only able to accurately determine the sound Direction-Of-Arrival (DoA) but also capable to satisfy the most demanding applications in terms of power efficiency. Design considerations of the required operations performing the sound location are discussed and analysed in order to facilitate the elaboration of reconfigurable acoustic beamforming architectures. Performance strategies are proposed and evaluated based on the characteristics of the presented architecture. This power-efficient architecture is compared to a different architecture prioritizing performance in order to reveal the unavoidable design trade-offs.

The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

Two- or three-dimensional visualization of magnetic flux density distribution around sources is quite informative for understanding the field environment intuitively. Measurement of magnetic flux density, as well as positioning of measuring points in space, has to be performed in a short time because temporal variation in the source current results in different field environments. It is also valuable to obtain the frequency spectrum of the magnetic field simultaneously at the time of field measurement, especially for sources generating high-frequency components like inverter-driven equipment. This paper develops a high-performance magnetic field sensor which satisfies the above requirements. And, as an application of it, the magnetic field visualization system using the augmented reality (AR) technique is proposed by combining two sensors: one is a magnetic field sensor described above, and the other is a Kinect sensor which has a skeleton tracking function as position determination.

The development of perpetually powered sensor networks for environment monitoring to avoid periodic battery replacement and to ensure the network never goes offline due to power is one of the primary goals in sensor network design. In many environment-monitoring applications, the sensor network is internet-connected, making the energy budget high because data must be transmitted regularly to a server through an uplink device. Determining the optimal solar panel size that will deliver sufficient energy to the sensor network in a given period is therefore of primary importance. The traditional technique of sizing solar photovoltaic (PV) panels is based on balancing the solar panel power rating and expected hours of radiation in a given area with the load wattage and hours of use. However, factors like the azimuth and tilt angles of alignment, operating temperature, dust accumulation, intermittent sunshine and seasonal effects influencing the duration of maximum radiation in a day all reduce the expected power output and cause this technique to greatly underestimate the required solar panel size. The majority of these factors are outside the scope of human control and must be therefore be budgeted for using an error factor. Determining of the magnitude of the error factor to use is crucial to prevent not only undersizing the panel, but also to prevent oversizing which will increase the cost of operationalizing the sensor network. But modeling error factors when there are many parameters to consider is not trivial. Equally importantly, the concept of microclimate may cause any two nodes of similar specifications to have very different power performance when located in the same climatological zone. There is then a need to change the solar panel sizing philosophy for these systems. This paper proposed the use of actual observed solar radiation and battery state of charge data in a realistic WSN-based automatic weather station in an outdoor uncontrolled environment. We then develop two mathematical models that can be used to determine the required minimum solar PV wattage that will ensure that the battery stays above a given threshold given the weather patterns of the area. The predicted and observed battery state of charge values have correlations of 0.844 and 0.935 and exhibit Root Mean Square Errors of 9.2% and 1.7% for the discrete calculus model and the transfer function estimation (TFE) model respectively. The results show that the models perform very well in state of charge prediction and subsequent determination of ideal solar panel rating for sensor networks used in environment monitoring applications.

A sound source localization device based on a multimicrophone array with the rectangular pyramid structure is proposed for mobile robot in some indoor applications. Firstly, a time delay estimation method based on the cross-power spectral phase algorithm and a fast search strategy of peak value based on the geometric distribution of microphones are developed to estimate the sound propagation delay differences between two microphones. Moreover, a rejection strategy is presented to evaluate the correctness of the delay difference values. And then, the device’s geometric equations based on the time-space mapping relationship are established to calculate the position of the sound source. For fast solving the equations, the multimicrophone array space is divided into several subspaces to narrow the solution range, and Newton iteration algorithm is introduced to solve the equations, while its solution is evaluated by an evaluation mechanism based on coordinate thresholds. Finally, some experiments are carried out to verify the performance of the device, of which the results show that the device can achieve sound source localization with a high accuracy.

This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

In this paper, the profile of the bar is detected by laser vision technology. During the detection process, obvious isolated outliers can be observed in the profile data; dimension parameter and profile-fitting accuracy are seriously affected by these outliers. In order to eliminate these outliers and improve the measurement accuracy, this paper uses Hampel identifier and moving mean identifier to identify isolated outliers. At the same time, the profile data is fitted, and the fitting results and fitting accuracy were analyzed and compared between the original data and the renovated data. The experiment proves that the outliers in the data must be identified and processed in the data measurement process. The Hampel identifier has better recognition effect, its algorithm is simple, efficient, and robust, and it can play an important role in the preprocessing of profile data based on structured light.

With the development and progress of technology, people’s requirements for living quality are increasingly higher. This study builds an indoor thermal comfort environmental monitoring system through the Internet of Things (IoT) architecture to explore the thermal comfort of people in indoor environments. Then, the applicable indicators are selected from a series of thermal comfort pointers, and the controllable indoor environmental parameters are analyzed and simulated on MATLAB to obtain the impact on the thermal comfort indicators, which can serve as important data to set up the fuzzy rule base. Next, according to the ISO7730 comfort standard and energy saving, three ways to control thermal comfort are proposed. With Arduino UNO as the development substrate, the sensing nodes for the indoor environment are set up, and the wireless sensing network is configured with ESP8266 to transmit the sensing data to the terminal. Monitored by the C# human-machine interface, the controllable load is controlled by wireless remote mode. Finally, the data is stored in the database for follow-up experimentation and analysis. Through actual measurement experiments, the thermal comfort and energy saving effects, under comfort, general, and energy-saving modes, as proposed in this study, are verified to achieve a balance between thermal comfort and energy saving.

Traffic data plays a very important role in Intelligent Transportation Systems (ITS). ITS requires complete traffic data in transportation control, management, guidance, and evaluation. However, the traffic data collected from many different types of sensors often includes missing data due to sensor damage or data transmission error, which affects the effectiveness and reliability of ITS. In order to ensure the quality and integrity of traffic flow data, it is very important to propose a satisfying data imputation method. However, most of the existing imputation methods cannot fully consider the impact of sensor data with data missing and the spatiotemporal correlation characteristics of traffic flow on imputation results. In this paper, a traffic data imputation method is proposed based on improved low-rank matrix decomposition (ILRMD), which fully considers the influence of missing data and effectively utilizes the spatiotemporal correlation characteristics among traffic data. The proposed method uses not only the traffic data around the sensor including missing data, but also the sensor data with data missing. The information of missing data is reflected into the coefficient matrix, and the spatiotemporal correlation characteristics are applied in order to obtain more accurate imputation results. The real traffic data collected from the Caltrans Performance Measurement System (PeMS) are used to evaluate the imputation performance of the proposed method. Experiment results show that the average imputation accuracy with proposed method can be improved 87.07% compared with the SVR, ARIMA, KNN, DBN-SVR, WNN, and traditional MC methods, and it is an effective method for data imputation.

As the airbag of a flexible robot is affected by external environmental factors during the profiling process, there are many uncertainties in the process of deformation of the airbag. For this reason, the general nonlinear control strategy cannot obtain an accurate data model. In this paper, a flexible robot profiling MFA (Model-Free Adaptive) model based on adaptive predictive dynamic linear optimization is proposed. Firstly, the real-time thickness of the airbag is obtained through edge detection by using the image processing algorithm. Secondly, the airbag aerodynamic model is constructed by visual servo control strategy. Then, a nonlinear control system based on model-free adaptive control is established. Thirdly, the weighting factor is used to limit the variation range of the input quantity, and the deviation of the actual value and the expected value is corrected by the adaptive prediction mechanism. Finally, the servo control the airbag is completed. The experimental results show that the improved model proposed in this paper solves the overshoot phenomenon of the standard control model with less control error and higher robustness.

In underwater acoustic wireless sensor networks (UAWSNs), designing media access control (MAC) protocols is highly challenging because of their significant influence on network performance. It is a great challenge to design suitable MAC protocols for UAWSNs because of the specific characteristics of underwater acoustic channels, such as low communication rates, large propagation delays, and limited available bandwidth. Orthogonal frequency division multiple access (OFDMA) is a special case of OFDM-based multicarrier modulation in which multiple user symbols are transmitted simultaneously using different subcarriers with overlapping orthogonal frequency bands. In this paper, we propose an OFDMA-based subcarrier pregrouping MAC protocol for UAWSNs, termed OSPG-MAC. To make it easier to allocate subcarriers and avoid conflict, we group all the subcarriers into fixed subchannels in advance and allocate them in terms of these subchannels. To maximize the total throughput on each subchannel, a bit and power allocation algorithm for the protocol is proposed, termed the OSPG-MAC algorithm. Simulation results show that the performance of OSPG-MAC is better than that of classic underwater MAC protocols, including SFAMA and T-Lohi.

In this paper we propose a global positioning algorithm of multiple assets based on Received Signal Strength (RSS) measurements that takes into account the gain uncertainties of each hardware transceiver involved in the system, as well as the uncertainties on the Log-Distance Path Loss (LDPL) parameters. Such a statistical model is established and its Maximum Likelihood Estimator (MLE) is given with the analytic expression of the Cramér-Rao Lower Bound (CRLB). Typical values of those uncertainties are given considering whether calibration is done in production, in situ, or if hardware is used uncalibrated, in order to know what is the expected accuracy in function of the calibration setup. Results are tested by numerical simulations and confronted to real measurements in different room configurations, showing that the theoretical bound can be reached by the proposed MLE algorithm.

Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based computer vision technique with the influence line theory. Theoretical background and derivations are introduced from both aspects of structural analysis and computer vision techniques. In addition, to evaluate the effectiveness and accuracy of the proposed traffic sensing method through field tests, a systematic analysis is performed on a continuous box-girder bridge. The obtained results show that the proposed method can automatically identify the vehicle load and speed with promising efficiency and accuracy and most importantly cost-effectiveness. All these features make the proposed methodology a desirable bridge weigh-in-motion system, especially for bridges already equipped with structural health monitoring system.

Medical studies have intensively demonstrated that sports activity can enhance both the mental and the physical health of practitioners. In recent years, fitness activity became the most common way to motivate and engage people in sports activity. Recently, there have been multiple attempts to elaborate on the “ideal” IoT-based solution to track and assess these fitness activities. Most fitness activities (except aerobic activities like running) involve one or multiple interactions between the athlete’s hand palms and body or between the hand palms and the workout materials. In this work, we present our idea to exploit these biomechanical interactions of the hand palms to track fitness activities via a smart glove. Our smart glove-based system integrates force-sensitive resistor (FSR) sensors into wearable fitness gloves to identify and count fitness activity, by analyzing the time series of the pressure distribution in the hand palms observed during fitness sessions. To assess the performance of our proposed system, we conducted an experimental study with 10 participants over 10 common fitness activities. For the user-dependent activity recognition case, the experimental results showed 88.90% of the F score for overall activity recognition. The result of leave-one-participant-out cross-validation showed an F score ranging from 58.30% to 100%, with an average of 82.00%. For the exercise repetition count, the system achieved an average counting error of 9.85%, with a standard deviation of 1.38.

The selectivity of a SAW (surface acoustic wave) sensor, with a Co3O4 sensitive thin film for NH3 (ammonia) and the influence of SnO2 on its sensitivity, was studied. Thin films were deposited by pulsed laser deposition (PLD) on quartz SAW sensor substrates. Two sensors with different types of sensitive films were developed: a Co3O4 thin film sensor (S1) and a SnO2/Co3O4 thin film sensor (S2). The sensitive films were deposited in conditions which ensured a porous structure. The sensors were tested in the presence of three gases: NH3, methanol, and toluene. The selectivity of Co3O4 for NH3 was determined from the difference in the frequency shifts of the sensor for NH3 and for VOCs (volatile organic compounds). The positive influence of SnO2 on the sensitivity of sensor S2 was observed from the lower limit of detection (LOD) of this sensor and from the differences in frequency shifts between sensor S1 and sensor S2.

As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.

Nowadays, more than half of the world’s population lives in urban areas. Since this proportion is expected to keep rising, the sustainable development of cities is of paramount importance to guarantee the quality of life of their inhabitants. Environmental noise is one of the main concerns that has to be addressed, due to its negative impact on the health of people. Different national and international noise directives and legislations have been defined during the past decades, which local authorities must comply with involving noise mapping, action plans, policing, and public awareness, among others. To this aim, a recent change in the paradigm for environmental noise monitoring has been driven by the rise of Internet of Things technology within smart cities through the design and development of wireless acoustic sensor networks (WASNs). This work reviews the most relevant WASN-based approaches developed to date focused on environmental noise monitoring. The proposals have moved from networks composed of high-accuracy commercial devices to the those integrated by ad hoc low-cost acoustic sensors, sometimes designed as hybrid networks with low and high computational capacity nodes. After describing the main characteristics of recent WASN-based projects, the paper also discusses several open challenges, such as the development of acoustic signal processing techniques to identify noise events, to allow the reliable and pervasive deployment of WASNs in urban areas together with some potential future applications.

Frequency diverse array (FDA) has attracted much attention in recent years due to its range-angle-dependent beampattern. Multiple-input multiple-output (MIMO) radar can offer waveform diversity to increase the virtual aperture length for azimuth coherent focus processing in radar imaging. Combining the advantages of FDA and MIMO radar, FDA-MIMO radar can steer multiple beams to different targets in the same line of sight (LOS) of radar with different waveforms. In this paper, an improved FDA model with the logistic map is proposed to get the aperiodic and range-angle uncoupling beampattern. Based on the proposed FDA, combining the FDA-MIMO radar, the waveform and chirp rate jitter techniques are adopted to mainlobe jamming suppression. Simulation results show the effectiveness of the proposed method.

Using high-precision sensors to monitor and predict the deformation trend of supertall buildings is a hot research topic for a long time. And in terms of deformation trend prediction, the main way to realized deformation trend prediction is the deep learning algorithm, but the accuracy of prediction result needs to be improved. To solve the problem described above, firstly, based on the conditional deep belief network (CDBN) model, the levenberg-marquardt (LM) was used to optimize the CDBN model; the LM-CDBN model has been constructed. Then taking CITIC tower, the tallest building in Beijing as the research object, the real-time monitoring data of the shape acceleration array (SAA) as an example, we used LM-CDBN model to analyse and predict the building deformation. Finally, to verify the accuracy and robustness of LM-CDBN model, the prediction results of the LM-CDBN model are compared with the prediction results of the CDBN model, the extreme learning machine (ELM) model, and the unscented Kalman filter-support vector regression (UKF-SVR) model, and we evaluated the result from three aspects: training error, fitness, and stability of prediction results. The results show that the LM-CDBN model has higher precision and fitting degree in the prediction of deformation trend of supertall buildings. And the MRE, MAE, and RMSE of the LM-CDBN model prediction results are only 0.0060, 0.0023mm, and 0.0031mm, and the prediction result was more in line with the actual deformation trend.

Light detection and ranging (LiDAR) data collected from airborne laser scanner system is one of the major sources to reconstruct Earth’s surface features. This paper presents a method for detecting model key points (MKPs) of the buildings using LiDAR point clouds. The proposed approach utilizes shaded relief images (SRIs) derived from the LiDAR data. The SRIs based on the concept of the shape from shading could provide unique information about individual surface patches of the building roofs. The main advantage of the proposed approach is to detect directly MKPs, which are primitives for 3D building modeling, without segmenting point clouds. Depending on the location of the light source, the SRIs are created differently. Therefore, integration of the multidirectional SRIs created from different locations of the light source could provide more reliable results. In addition, the vertical exaggeration (i.e., scaling Z-coordinates) is also beneficial because constituent surface patches of the roofs in the SRIs created with vertically exaggerated LiDAR data are more distinguishable. To determine the MKPs of the roofs, building data was separated from other objects using modified marker-controlled watershed algorithm in accordance with criteria to specify buildings such as area, height, and standard deviation. This process could remove the unnecessary objects such as trees, vegetation, and cars. The curvature scale space (CSS) corner detector was used to determine MKP since this method is robust to geometric changes such as rotation, translation, and scale. The proposed method was applied to simulated and real LiDAR datasets with various roof types. The experimental results show that the proposed method is effective in determining MKPs of various roof types with high level of detail (LoD).

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

The modeling of solar radiation pressure is the most important issue in precision GNSS orbit determination and is usually represented by constant and periodic terms in three orthogonal axes. Unfortunately, these parameters are generally correlated with each other due to overparameterization, and furthermore, the correlation does not remain constant throughout a long-term period. A total of 500 weeks of GPS daily solutions were estimated with the empirical CODE orbit model (ECOM) to cover various block types of satellites. The statistics of the postfit residuals were analyzed in this study, which shows the dominant annual variation of the correlations over time. There is no significant difference between eclipsing and noneclipsing satellites, and the frequency of the correlation exactly corresponds to the GPS draconitic year. Based on the residual analysis, the ECOM is the most appropriate for the Block IIR/IIR-M satellites but does not properly account for the behavior of either older Block IIA or newer IIF satellites. In addition, the daily mean residuals show a different pattern for satellite orbital planes. Therefore, the orbit model should be customized for the block types and orbital plane for better representation of multi-GNSS orbits.

Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight. To address this problem, this paper presents an automated coregistration approach for orthophotos generated from VHR images acquired from multisensors mounted on UAV platforms. Spatial information from the orthophotos, provided by the global navigation satellite system (GNSS) at each image’s acquisition time, is used as ancillary information for phase correlation-based coregistration. A transformation function between the multisensor orthophotos is then estimated based on conjugate points (CPs), which are locally extracted over orthophotos using the phase correlation approach. Two multisensor datasets are constructed to evaluate the proposed approach. These visual and quantitative evaluations confirm the superiority of the proposed method.

Optical Waveguide Lightmode Spectroscopy (OWLS) is widely applied to monitor protein adsorption, polymer self-assembly, and living cells on the surface of the sensor in a label-free manner. Typically, to determine the optogeometrical parameters of the analyte layer (adlayer), the homogeneous and isotropic thin adlayer model is used to analyze the recorded OWLS data. However, in most practical situations, the analyte layer is neither homogeneous nor isotropic. Therefore, the measurement with two waveguide modes and the applied model cannot supply enough information about the parameters of the possible adlayer inhomogeneity and anisotropy. Only the so-called quasihomogeneous adlayer refractive index, layer thickness, and surface mass can be determined. In the present work, we construct an inhomogeneous adlayer model. In our model, the adlayer covers the waveguide surface only partially and it has a given refractive index profile perpendicular to the surface of the sensor. Using analytical and numerical model calculations, the step-index and exponential refractive index profiles are investigated with varying surface coverages from 0 to 100%. The relevant equations are summarized and three typically employed waveguide sensor structures are studied in detail. We predict the errors in the calculated optogeometrical parameters of the adlayer by simulating the OWLS measurement on an assumed inhomogeneous adlayer. We found that the surface coverage has negligible influence on the calculated refractive index below film thicknesses of 5 nm; the calculated refractive index is close to the refractive index of the adlayer islands. But the determined quasihomogeneous adlayer refractive index and surface mass are always underrated; the calculated quasihomogeneous thickness is heavily influenced by the surface coverage. Depending on the refractive index profile, waveguide geometry, and surface coverage, the thickness obtained from the homogeneous and isotropic modeling can even take negative and largely overestimated values, too. Therefore, experimentally obtained unrealistic adlayer values, which were dismissed previously, might be important indicators of layer structure.

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.