P value = .000). Weight loss was the only significant different symptom . A total of 16 patients and 28 controls were included; 13 proved to be true positive and 20 proved to be true negative. The receiver operating characteristic curve showed a sensitivity of 81% and a specificity of 71%, with an accuracy of 75%. These results give a positive predictive value of 62% and a negative predictive value of 87%. This pilot study shows that the e-nose has the capability of diagnosing GC based on exhaled air, with promising predictive values for a screening purpose.The aim of this pilot study is to investigate the ability of an electronic nose (e-nose) to distinguish malignant gastric histology from healthy controls in exhaled breath. In a period of 3 weeks, all preoperative gastric carcinoma (GC) patients (n = 16) in the Beijing Oncology Hospital were asked to participate in the study. The control group (n = 28) consisted of family members screened by endoscopy and healthy volunteers. The e-nose consists of 3 sensors with which volatile organic compounds in the exhaled air react. Real-time analysis takes place within the e-nose, and binary data are exported and interpreted by an artificial neuronal network. This is a self-learning computational system. The inclusion rate of the study was 100%. Baseline characteristics differed significantly only for age: the average age of the patient group was 57 years and that of the healthy control group 37 years ( Up to 42% of the cases are in Eastern Asia (mainly in China), where the majority of the annual GC-related deaths occur.3 The high mortality rates in China are linked to late detection, which is partly explained by the lack of signs and symptoms in GC patients.4 In addition to that, the lack of efficient screening tools remains an unsolved issue in China, where the majority of the gastric cancer cases are already locally advanced or worse at time of diagnosis. This can also be seen in many Western countries where no national screening programs for gastric cancer exist, given the relatively low incidence of the disease. Locally advanced GC results in significantly decreased survival when compared with early-stage GC, which may yield 5-year survival rates of 90% after surgical resection, endoscopic submucosal dissection (ESD) or endoscopic mucosal resection (EMR).7 Therefore, early detection and treatment seems to be the only way to reduce mortality, complications, and costs associated with the disease.8Gastric carcinoma (GC) is the fourth most common cancer worldwide.9 Other radiological screening tools such as the conventional double-contrast barium radiograph with photofluorography, the Japanese screening strategy, requires highly trained personnel and, thus, has not yet been implemented in other countries. Although blood biomarkers such as CEA, CA125, and CA199 have a certain association with the progression of the disease, their sensitivity and specificity are too low for screening purposes.11Nowadays, endoscopy with biopsy remains the most accurate way of diagnosing GC. Therefore, the Korean screening program is indicated for individuals older than 40 years, in whom an upper-gastrointestinal (GI) series or an endoscopy is performed every two years. However, because this is a costly and invasive method, it is not routinely used as a screening tool in China or most other countries in the world. Moreover, some subtypes of gastric cancer can be easily missed on gastroscopy.12 The human breath contains a complex mixture of almost 3000 different volatile organic compounds (VOCs) that enter the exhaled air via the alveolar-capillary membrane of the respiratory tract.13 These can be identified by means of gas chromatography and mass spectrometry, which is a highly sensitive but costly and time-consuming technique because it requires both specialized equipment and trained personnel.15 Another analysis method that is commonly used in the consumer goods analyses, in the monitoring of air quality and detection of chemical agents, is pattern recognition by electronic nose (e-nose) technologies.16 e-Noses use an array of chemical sensors, a signal transductor and finally multivariate data analysis for pattern recognition to classify the samples.17 e-Nose technology also has many biomedical applications that can be used in the diagnosis of colon and lung cancer and chronic obstructive pulmonary disease (COPD), and cancers of the head and neck.1820 Given the promising results of this technology in the detection of other diseases, the aim of this pilot study was to investigate whether it was possible to distinguish gastric cancer patients from healthy controls based on their exhaled breath patterns.Breath analysis has recently emerged as a promising tool to diagnose several types of cancer and was shown to have especially high sensitivity and specificity for cancers of the head and neck and for colon carcinoma.The study was conducted in the Beijing Oncology Hospital in a period of three weeks. The experimental group included patients diagnosed with GC who were admitted to the hospital prior to undergoing surgical resection of the tumor. The study was approved by the medical ethical committee of the Beijing Oncology Hospital and was conducted in accordance with the Declaration of Helsinki. The exclusion criteria were; age younger than 18 years, cognitive impairment and the presence of a tracheostomy. Other exclusion criteria included the presence of any concurrent infection that was judged to possibly interfere with interpretation of the data and the presence of comorbidities such as COPD or other breathing difficulties, which made the breathing test difficult or impossible to execute.The control group included family members of the patients in the experimental group who were screened by means of endoscopy and were found not to suffer from GC. Patients and controls eligible for this study were given oral and written information about the study at the department. Informed consent was obtained from all patients.An extensive database containing basic demographic factors  of all the patients as well as the location of the measurement was prospectively created. A questionnaire was filled out by all patients to detect any of the following complaints; nausea, vomiting, acid reflux, heartburn, abdominal distension, loss of appetite, dysphagia, ructus, fatigue, hematemesis, blood in stools, abdominal discomfort, weight loss, or jaundice. Previous diagnostics of all participants were documented . For all patients included in the study, pathology results after surgical resection were documented: tumor location, tumor macroscopy, pathological differentiation , mucinous/signet ring appearance, TNM-staging based on CT and on ultrasound, CA125 level, and neoadjuvant therapy.21 One eNose was purchased for the purpose of this study and was used to test all patients.This study used the Aeonose, which is a CE-certified e-nose device manufactured by The eNose Company located in Zutphen, the Netherlands. It is a proprietary software package Aethena. Details have been described by Kort et al.22 The airflow is controlled by a solenoid switch selecting between 2 different inlets, thereby facilitating an active airflow across all sensors. One inlet is connected to an active carbon filter to provide a baseline free from environmental influence; the other inlet is attached to the breathing tube. The disposable mouthpiece consists of a nonrebreathing T-valve with an active carbon filter attached to the inlet. During sampling, a nose clamp was placed on the nose of the participant to avoid entry of nonfiltered air. The first 2 minutes are used to flush out environmental influences from the lungs after which the exhaled air is measured for 3 minutes followed by a 4-minute recovery period. The built-in absorber is cleared for one minute (20s heating followed by 40s cooldown) to release the attached volatiles, after which the final measurement is performed under influence of the clean reference air. This process takes another 5 minutes. Thus, each single measurement comprises an adsorption and desorption step, each associated with specific chemical dynamics at the sensor’s surface.It contains 3 micro-hotplate metal-oxide sensors and a pump. The hotplates are alternatively heated and cooled during the measurement during which the sensors are exposed to the exhaled air. Reactions of the VOCs on the sensors’ surfaces alter their conductivity, which generates unique patterns.Measurements were performed in the treatment room of the Department of Gastrointestinal Surgery in the Beijing Oncology Hospital. All participants were asked to inhale and exhale through the mouthpiece attached to the Aeonose for 5 minutes. The VOCs are analyzed within the e-nose and exported as numerical data. The main objective is not to define a specific VOC in the measurement, but rather to determine the pattern of resistance changes in the sensors caused by the absorption of the various VOCs in the breath of patients. This results in a graphic pattern specific for each disease.t test, χ2 test, and Fisher exact test. The Aeonose measures the air composition every 20 seconds using two 32-step sinusoidal modulations of the sensor surface temperature, thus resulting in a vector of 64 values every 20 seconds for each of the three sensors. These numerical codes were exported and normalized to minimize and eliminate interdevice differences. The normalized data of qualified samples are then used to train an artificial neural network (ANN). The ANN is a computational system comparable to the neural network of the human brain and thereby enables itself to learn.Differences in baseline characteristics between the experimental and control groups were determined with the independent-sample Training groups are selected carefully to prevent separation shown from effects other than the disease . The network is being trained specicifically on the disease. Other effects should level out. Cross-validation is a technique that helps in this respect. These results were presented in a scatter plot and receiver operating characteristic (ROC) curve. A leave-one-out analysis was performed for final validation when plotting the negatives and positives against the predictive value  to detecWithin a period of three weeks, 44 individuals were included . All gastric cancer patients underwent a resection after which the gastric cancer was proven by histopathology. The baseline characteristics of the two groups are listed in All participants filled out questionnaires concerning symptoms possibly related to GI disease. Weight loss was the only symptom reported that differed significantly between the 2 groups, where GC patients had more complaints of weight loss than controls .Of the 44 included patients, 13 were proved true positives and 20 true negatives. The ROC curve showed a sensitivity of 81% and a specificity of 71% in differentiating between GC and healthy controls, with an overall accuracy of 75%. The Matthews Correlation Coefficient was calculated to show the binary classification between the patients and controls, which was 0.51. A positive predictive value of 62% and a negative predictive value of 87% was found. A leave-one-out analysis was performed for final cross-validation. When looking at the scatterplot, several outliers can be seen. Three patients (red dots) are seen amid the controls. One of these was a patient with a gastrointestinal stromal tumor (GIST) tumor identified on pathology. The location of the tumor did not seem to affect the outcome of the test.Three control patients were classified by the e-nose as if they had gastric cancer; these controls will be asked to undergo an endoscopy to determine the presence of GC.It has been shown that several diseases can be detected in exhaled breath. Until recently, breath analysis studies have mainly focused on the technique of gas chromatography and mass spectrometry. This technique has several disadvantages because sample preparation and data analysis are time-consuming and require highly trained personnel; it is, therefore, very costly. The e-nose has emerged as an alternative method to analyze the composition of breath samples and does so with pattern recognition software that allows the researcher to link specific patterns with a wide range of pathologies. This pilot study examined the ability of the e-nose to recognize the presence of GC in preoperative patients, in their exhaled air, and to distinguish them from healthy controls. Our results suggest that the e-nose is a very promising screening tool; further studies in a larger patient population are therefore recommended. Several limitations should be considered when interpreting our study results, of which the case-controlled nature and small sample size are the 2 most important. Furthermore, only Chinese patients were included in this pilot study. Even though this population is an accurate representative of the Eastern Asian population, we acknowledge that the applicability of our results to the Western populations is unknown. The control group consisted of family members of the patients included in this study, which allows for correction of the influence of the background of the patient/control on the breath composition. However, in further investigations, a general population should be used as a control group to further elucidate its efficacy as a screening tool.2325 Shehada et al25 have performed the only two other studies concerning detection of gastric cancer in exhaled breath, to our current knowledge. These studies distinguished early and late stages of GC from dyspeptic controls proven not to have GC.25 This resulted in a sensitivity of 87% in 30 patients and 77 controls9 and a sensitivity of 87% in 149 gastric cancer patients versus 129 controls.25 These good results in sensitivity in large populations are promising results that show the possibility of detecting gastric cancer in exhaled breath. Silicon nanowire sensors were used; these require a coating that were chosen based on results from earlier mass spectrometry studies for gastric cancer, although in this study, patients breathed in a collecting bag to collect breath samples. These are stored in tubes that are then refrigerated and transported for analysis, which requires less time and material and fewer personnel. Moreover, in the breath test performed by the silicon nanowire sensors, patients were required to fast and withhold alcohol and cigarettes two hours before the breath tests. The advantage of the e-nose used in this study is the real-time analysis. By immediately analyzing the exhaled air in the e-nose during and after the breath test, no storage and additional testing is used.Previous literature has proven that the e-noses are able to distinguish between different types of oncological and nonmalignant diseases.26 suggested that the overall VOC profile does not differ by age group or gender.The most important advantage of the e-nose is evident: the test is completely noninvasive. The 100% enrollment rate of this pilot study implies that breath testing can be widely accepted as a future screening method. With three false positives and 13 true positives, three false negatives and 20 true negatives in our group of 44 individuals, the e-nose proved to have a sensitivity of 81% and a specificity of 71%. Postoperative pathological examination proved one of the false negatives to be a GIST tumor, which may explain the deviating results. The location of the tumor did not seem to affect the outcome of the test, even though the sample size might have been too small to detect the association between tumor site and test outcome. The other 2 false-negative breath samples were obtained from measurements that were interrupted shortly because the patients were short of breath, which may have altered the outcome. The high sensitivity of the e-nose can be explained by the broad spectrum of VOC reactions on multiple sensors at different temperatures and by the use of an ANN, which recognizes patterns more sensitively with every inclusion through a self-learning system. A study by Dragonieri et al27 However, exhaled breath patterns may be influenced by environment and lifestyle (diet in particular), although this was not taken into account here, and the results showed a good sensitivity. Further investigation and validation studies in different patient populations are necessary.Moreover, the fact that the GIST tumor did not cluster with those of the other GC patients, of which pathological investigation proved to be adenocarcinoma might show the ability of the e-nose to differentiate between types of GCs. Our and previous findings suggest that use of the e-nose enables the screening of multiple diseases with a single measurement.In current medical practice, endoscopy may be suggested in patients with gastric complaints or a positive family history, resulting in many negative diagnostic tests. We believe that the e-nose may find its role in medical practice as a screening technique and may well be used in combination with, or instead of, endoscopy as a first-line diagnostic tool for GC. By selecting patients based on the outcome of the e-nose, a high-risk patient population can be targeted for further examination with endoscopy and biopsy. Therefore, by reducing the need of invasive examinations, the e-nose may substantially reduce health care costs.28 Therefore, and also because of the self-learning capacity of the ANN involved, which will increase the sensitivity and specificity tremendously, the Aeonose is believed to be a promising tool for future screening purposes. Based on the current results, a prospective cohort study with a larger and more varied population is planned to further train the ANN in recognition of gastric cancer. After this, a blinded validation should take place in a group to further explore and determine the diagnostic accuracy of the e-nose.When compared with endoscopy and biopsy, the accuracy of the Aeonose to differentiate between GC patients and healthy controls in this pilot study remains relatively low. On the other hand, this pilot study of GC detection in exhaled breath already shows more potential than the reported accuracy of all blood tests investigated for GC.This pilot study shows that the e-nose has the capability to distinguish gastric cancer patients from healthy controls, based on samples from exhaled air.


To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi–Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg–Marquardt (L–M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM’s  accuracy and robustness. Finally, a Shi–Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising. Recently, many visual simultaneous localization and mapping (SLAM) systems have been proposed, since they are fundamental building blocks for many emerging technologies, such as autonomous cars, virtual reality, and augmented reality . Mobile At present, the SLAM system based on vision sensors has gained popularity in the field . AccordiThe direct and semi-direct formulations optimize the photometric error based on the grayscale invariant assumption to estimate the camera motion, since the sensors provide the photometric measurements . J. EngeIn the DSO series and LDSO, the photometric parameters were introduced to compensate for the vignetting and response function of input images as constants. However, the compensation, based on pre-trained photometric calibration files, could not update photometric parameters for dynamic illumination in real time. Inspired by  and 17]17], in oAccording to the photometric parameters of the auto exposure camera, an optimization equation was established based on the corner point tracker. Then, the vignetting factors and the response function were dynamically updated to compensate for the input sequence illumination condition to realize the photometric calibration in real time. A scene point is illuminated by a light source and reflects the energy back into space . The gloWhen the vision sensor captures the scene as an image, the radiance of the scene’s points are converted into irradiance nction G . The floThe effective incident off-axis light of the front lens was changed according to the size of the aperture and the exposure time, which caused the pixel intensity of the image to gradually weaken from the center out. In the auto-exposure mode, the adaptive exposure time was determined by the different scenes. The response function is a process through which the received photon is nonlinearly converted into a brightness value.The imaging model of the photometric image formation in Vignette  follows .(3)V(x)Response function The main response function After constructing the model of the vignetting factors nction G . The floThe Shi–Tomasi corners are generally utilized as the global features to represent an image, owing to their good affine invariance. Those corners are tracked by the Kanade–Lucas (LK) optical flow on the image pyramid, which is called the KLT tracker, to construct an optical flow energy equation. We segmented an image into 32 × 32 regions and defined a constant of tracked candidate points to obtain a good effect. When the tracked feature was lost, a new candidate was extracted from the high-gradient region that contained fewer points. If the max gradient of a region was lower than the threshold, the region was filtered .For a set of tracked points The estimation of the vignetting and response functions requires multiple images, which are difficult to collect in time during one calibration. Therefore, the states of the current vignetting and response function were maintained after the current estimation to compensate the last frame. Then, the compensated frame was used for localization, mapping, and evaluating the photometric parameters in the next frame, as shown in In the direct formulation, the minimized photometric error was utilized to achieve the camera pose based on the grayscale invariance assumption. DSO integrates the photometric parameters to simulate the vignetting effect and the gamma attenuation to enhance the robustness of VO. However, the scene radiance of VO was calculated using the pretrained photometric calibration files that were proposed in .To further enhance the performance of the direct formulation, the photometric calibration should adapt to a continuous pixels’ brightness value to respond to the illumination challenge. When the last frame enters the sliding window, it is compensated based on the previous frames and then applied to the front-end of the SLAM system.After the frame is compensated, it enters the VO model to join the localization and mapping. The covisual pixels in all frames of the sliding window are projected onto the current frame to build the photometric error equation, which is given by Equation (7).or graph .There are at most on costs ,12.In The tracking of the indirect feature, which was utilized to update the response function and the vignetting factor, was simultaneously performed with the estimations of the inverse depth and the camera pose in the sliding window. Thus, the tracking equations in the sliding window are rewritten as Equations (8) and (9).rameters ; and p′ rameters . AccordiThe L–M algorithm is applied to calculate the Jacobian matrix of the residual Equations 12)–(14) describe the iterative solution process of Equation (11) based on the L–M approach. Here, the weight matrix –(14) desBy solving When a new key frame is inserted, the current sliding window is optimized using the bundle adjustment (BA)  like theIn The least squares problem represented by Equation (16) can be iteratively solved using the L–M algorithm, and then the best current camera pose  library .In LDSO, the loop closure detection was realized by calculating the ORB descriptor of the tracked Shi–Tomasi corner. However, the tracked candidate points may be extracted in the low gradient region, which increases the calculation burden and affects the performance of the loop closure detection . In the As can be seen from The proposed algorithm was operated on a laptop with an Intel i7-8750H CPU, 16G of memory, and Ubuntu16.04. GPU acceleration was not adopted during the experiments. The simulation and the actual experiments were designed to evaluate the localization accuracy, loop closure performance, and point cloud map of the proposed algorithm.The experimental designs were grouped according to the dataset in this paper. Firstly, the localization accuracy, the photometric parameters ‘calibration and the pixels’ tracking performance were tested on the TUM dataset . SecondlThe proposed algorithm was evaluated using the TUM-Mono and KITTI Odometry datasets in a monocular setting. The TUM dataset  is a sceAs shown in x-axis, y-axis, and z-axis compared with the enhanced DSO [nced DSO  and the nced DSO .The photometric parameters of randomly selected frames were calculated and are shown in As can be seen in The last frame of TUM mono dataset sequence 04 was captured to compare the condition of exposure and tracking performance after photometric calibration and the experimental results are shown in To further verify the performance, the KITTI dataset was utilized to test the localization accuracy, the loop closure detection performance, and the timing cost of the proposed algorithm. The KITTI dataset  was joinIn the evaluation of , monoculx-axis and z-axis were recorded in The trajectories along the At present, the SLAM algorithm’s performance indicators mainly include the ATE (absolute trajectory error) and RPE (relative position error). The ATE was utilized to compare the localization accuracy of the SLAM algorithm due to its directness. We calculated the ATEs of DSO, LDSO, and the proposed algorithm on KITTI and collected them in The ATE of mono-ORBSLAM could not be obtained because of the tracking failure around the 585th frame of the KITTI dataset sequence 01 as The experimental results in The 6-DoF rigid body motion error was calculated, as shown in x-axis and the z-axis were 0.2153 m and 0.5259 m, respectively, and the rotational error of the camera pose was less than 0.05 rad, which further proves that the proposed system can better cope with the illumination changes in the KITTI dataset sequence 00.The precision–recall (PR) curve is widely applied to evaluate the performance of loop closure detection. The percentage of loop closures, which were correctly detected in all the detections, was represented as the precision ratio. The percentage of loop closures, which were correctly detected in all real loop closures, was defined as the recall ratio. To compare the loop closure detection performance of the proposed system and LDSO, the PR curve of the loop closure detection is illustrated in The research in this paper was mainly based on the improvement of LDSO  and onliThe experimental results show that the real-time photometric calibration direct SLAM system can obtain a 19.7% higher accuracy performance and 4.7% bigger timing costs than LDSO. Due to the performance improvement of the direct SLAM, the extra time burden is acceptable.The EuRoC micro aerial vehicle datasets  were proAs can be seen from We emphatically evaluated the proposed algorithm in an actual environment to prove the enhancement related to LDSO. To collect the real-time images, the Basler acA1920-155uc camera was selected, which is a global shutter complementary metal oxide semiconductor (CMOS) industrial camera produced by Basler, Germany. It has a 1920 × 1200 maximum resolution and a 164 fps maximum rate. The camera and notebook are equipped on a TurtleBot2, which is a robot operation system (ROS)-based mobile research platform that was produced by YUJIN, Korea. The experimental platform and the camera calibration are shown in To achieve better real-time performance, the adopted resolution was 640 × 480 during the experiment, which can be modified in the calibration file of the camera. After the preparation of the experiment, the ROS  was utilized to control the ground equipment to acquire the scene’s visual information and perform positioning and mapping. The process of the experiment was recorded as In In this paper, a real-time photometric calibrated monocular direct visual SLAM was proposed to dynamically compensate for the input sequence’s exposure. It solved the problem that the LDSO had—poor positioning and mapping robustness, due to illumination challenges. The enhanced sparse direct visual SLAM formulation was more suitable for the research and application of navigation and positioning in mobile ground equipment. Firstly, the vignetting and response function according to the photometric formation were introduced into the front-end. Secondly, the Shi–Tomasi corners of the input sequence were filtered and added to the tracking optimization equation using the tracked pixel in visual odometry. Then, the L–M approach was utilized to iteratively calculate the photometric parameters in the sliding window to compensate for the exposure condition of the input sequence. Finally, the tracked Shi–Tomasi corners in the adaptive photometric calibration and their ORB features were applied to achieve loop closure detection. The results of multiple simulations and experiments show that the proposed method had a better positioning and mapping performance than DSO and LDSO. In particular, the DSO which was integrated with the online photometric calibration, was also complementally tested to prove the generalization performance of the algorithm  to a cerIn future work, we will consider enhancing the adaptability of the online photometric calibration to further calibrate the visual odometry. The insertion mechanism of the key frames and the loop closure strategy will be adjusted to improve the calculation efficiency. In addition, we will seek to introduce semantic information into the direct SLAM to achieve a better loop closure detection.


Intermittent pneumatic compression (IPC) is a proactive compression therapeutic technique in the prophylaxis of deep vein thrombosis, reduction of limb edema, and treatment of chronic venous ulcers. To appropriately detect and analyze biomechanical pressure profiles delivered by IPC in treatment, a dynamic interface pressure monitoring system was developed to visualize and quantify morphological pressure mapping in the spatial and temporal domains in real time. The system comprises matrix soft sensors, a smart IPC device, a monitoring and analysis software, and a display unit. The developed soft sensor fabricated by an advanced screen printing technology was used to detect intermitted pressure by an IPC device. The pneumatic pressure signals inside the bladders of the IPC were also transiently collected by a data acquisition system and then transmitted to the computer through Bluetooth. The experimental results reveal that the developed pressure monitoring system can perform the real-time detection of dynamic pressures by IPC and display the morphological pressure mapping multi-dimensionally. This new system provides a novel modality to assist in the effective evaluation of proactive compression therapy in practice. The study results contribute to understanding the working mechanisms of IPC and improving its functional design based on intuitive biomechanical characteristics of compression delivery profiles. Compression therapy has become a mainstay for prophylaxis and the treatment of chronic venous insufficiency (CVI) of the lower extremities . An incrIntermittent pneumatic compression (IPC) is a type of increasingly used dynamic compression therapeutic techniques that are applied to improve venous circulation in the limbs of patients with CVI, edema, or the risk of DVT or pulmonary embolism ,6. An IPFerraresi et al., established a mathematical model to numerically analyze the working correlations among the involved multicomponent factors  ,17. The In addition, variations in the biomechanical properties and deformations on the human skin induced by different external forces have attracted increasing attention in fields of electronics, biology, and biomedicine . DagdeviDue to the increasing awareness of personalized medicine, the latest wearable device for monitoring and therapy require more intuitive and sophisticated interfaces to be in contact with the body for communication, sensing, and biofeedback ,26. DiffThe IPC device mimics the muscular function to generate a peristaltic centripetal pressure or sequential gradient pressure waves on the targeted positions of limbs for augmenting venous return. During the interaction between the IPC device and a lower limb, pneumatic pressures in bladders, air mass flow supplied by the IPC device, and the resultant interface pressures existing within the IPC device‒human body system are the key parameters for determining the pressure dosage delivery, compression cycles, and compression therapeutic effects. In this study, to detect the key parameters in the IPC device operation in a timely manner, a novel matrix soft sensor-based monitoring system was designed and developed to detect, record, and display different dynamic interface pressure magnitudes and mappings in real time induced by the IPC device on a continuous skin surface of a lower limb in two-dimensional (2D) and 3D views. The involved matrix soft sensors were fabricated by transferring a nanoforce-sensitive semiconductor ink to a polyethylene terephthalate substrate by using advanced screen-printing technology. The proposed IPC monitoring device demonstrated the applicability to qualitatively and quantitatively illustrate interface pressure magnitudes and distributions with a sustained or an intermittent mode during specific inflation‒deflation cycles. Moreover, the air pressure inside the pneumatic bladder(s) (chamber) was monitored using differential air pressure sensors to establish numerical relationships between dynamic pressure mapping and the pneumatic air pressures of the IPC device. In this new system, Bluetooth was used to wirelessly transmit voltage signals and the converted pressure values to display morphological pressure mapping in a timely manner on the computer screen by using the engineered algorithm-driven software unit.3) and a nanoforce-sensitive semiconductor ink layer  as shown in 3. The external pressure applied on the cell areas are reflected by the resistance variation in the semiconductor in the sensing area; thus, the real-time measurement of the intermitted or sustained skin pressure was obtained using the IPC device.To detect the interface pressure between the IPC and a lower limb, a matrix soft sensor was engineered in a sandwich structure that comprised two polyethylene terephthalate (PET) substrates  [0R is the initial resistance of the semi-conductive ink; ε is the strain; D is the filler particle diameter; φ is the volume fraction of the filler particles; h is Plank’s constant; m is electron mass; ϕ is the height of the potential barrier between two adjacent filler particles; and 0S is the initial distance between the two adjacent filler particles.Considering that the semiconductor ink is a type of composite material , tion (3)  as folloε(t) is the induced strain; P is the applied pressure; 0E and 1E are the elastic moduli of the springs; μ1 is the viscosity of the damper element; t is the time.In application, the strain could change slightly with time when a constant external pressure is applied to the soft sensor. A single Maxwell or a single Kelvin-Voigt element cannot accurately represent the transient response of the soft sensor. Therefore, a constitutive equation of the standard linear solid model was considered, where the compressive stress component is only of interest, while other stress and strain components would not be taken into account ,51. ThenTherefore, the relationship between the resistance of a semi-conductive ink and the applied external pressure, in respect of the effects of contact and creep, can be expressed as follows:sensor) was verified by the experimental measurement. A vibratory shaker and a Keithley digital multimeters system  × 70 (length) cm2. This laminated wrapping layer formed the major body of the IPC unit and was unfolded onto a wooden leg model to simulate the treatment condition. Moreover, this IPC unit comprised a pneumatic pump, a data acquisition (DAQ) card, matrix soft sensor, a differential pressure sensor, and air pipes. These components were used to form a pump control system for both air source supply and dynamic pressure delivery. Pneumatic bladders and pneumatic pump  were connected using flexible air pipes. One differential pressure sensor (model no. MPX 5100DP) was connected with the air pipes by using tee joints, as shown in To monitor the dynamic pressures induced by IPC in the inflation‒deflation cycles, the developed soft sensor was mounted onto a wooden leg model, as illustrated in The DAQ system is the core of the IPC dynamic pressure monitoring system and is used to collect pressure signals that are produced by the IPC unit. The unit comprised the Bluetooth module, RX‒TX port, data processing unit, controller, A/D converter, and rotary switches, as shown in refR = 30 kÙ, based on the sensor resistance) and ground (GND) through a rotary switch and connecting cables. The input analog voltage (inputA) can be calculated by the voltage division equation, i.e.,ccV is the system voltage; sensorR represents the cell sensor resistance.To calculate the pressure voltage data, the sensor array was connected to the reference resister (outputD) can be calculated byrefV is the input full-scale voltage; and m is the bit length of outputD. The refV of the A/D converter channels can be selected to the microcontroller’s positive supply voltage ccV by using a software controller.The digital output of the voltage value  can be rewritten asWhen ccV did not affect digital outputs. Each cell of the soft sensor was calibrated using different counterweights. The size of the bottom pad of the counterweight was 12 × 12 mm2, which fully matched each cell zone based on the standard testing procedures [Equation (8) presents the relationship between the digital resistances of A/D converters and the sensor resistances. ocedures ,57. The 3). Through the calibration, different sensor cells achieved the same pressure level prior to formal testing.In the calibration, the counterweights were put on each sensor cell one by one in a designed sequence, and the corresponding voltage signals were collected using the developed data acquisition system as aforementioned. Through calibration, a series of counterweights and their corresponding signals were recorded. The relationship between the produced voltages (V) and the pressure (kPa) for each sensor cell shows an approximately linear trend via a curve fitting algorithm  Figure . The volTo visualize the morphological pressure mapping conducted between the IPC unit and the lower limb, a software was designed and developed based on a multi-thread theory for the real-time monitoring ,60, thatMoreover, the software was used to observe the dynamic variations during pressure dosage delivery and interactions with tissue deformation, thereby obtaining intuitive biofeedback on pressure treatment in practical use. The data processing design of the computer software is presented in The computers were connected to the remote receiver  through Bluetooth. First, an overall configuration including the receiver data buffer size, read interval timeout, and timer interval was set carefully and concertedly to ensure a smooth flow of data reception, display, and processing. Second, after connecting to the Bluetooth virtual serial port, the timer was forced to initiate with interruptions of the waiting status. Two event-driven responses comprising the receiving data event and timer event corresponded to the receiving data and processing data, respectively. For the receiving data event, the sensor values were extracted from the data packages after reading the virtual serial port data buffer and then were transformed to pressure values based on a communication agreement. For the timer event, the sensor values were displayed as time-varying curves in real time. Finally, the program returned to the main body after storing the data.The IPC unit was regarded as a primary part to deliver a controllable pressure dosage to the lower limb. The working modes  and control logic were governed by the IPC unit. Here, the pressure dosage mode of 23 s‒7 s‒7 s in one compression cycle, which sustains the 37 s treatment condition, was adopted as an example to illustrate a general working mode of the IPC. This working mode includes three stages. First, the inflation process is included in the first 23 s. In this process, the pneumatic pump controller was operated, and the bladder was charged with a constant pneumatic air mass flow. This procedure allowed the pneumatic pressure inside the bladders to gradually increase. Second, the holding process was conducted from 24 to 30 s. In this process, the pneumatic pump stopped working, and the valves closed to maintain the pneumatic pressure inside the bladders for 7 s. Finally, the deflation process was conducted in the final 7 s. Here, the valves were opened to deflate the bladder pneumatic air.By setting working modes in the testing system, the periodic working rhythms of the IPC unit can be detected by determining the varying amplitudes of the pneumatic pressures. Here, the amplitudes were governed by the pneumatic pump controller and the air valves. The pneumatic pressure variations can be used to determine the pneumatic air mass flow rate under different inflation‒deflation cycles, and to analyze the interactive mechanism of the design parameters of the IPC unit, and to validate the developed IPC monitoring system. Under IPC operation, the pneumatic air was pumped into the bladder and the resultant interface pressure was produced and exerted on the limb skin and tissue. The interface pressure values increased with an increase in the air mass flow rate, expansion of the pneumatic bladder, and acting forces supplied by the IPC. When the deflated valves open, the pneumatic pressure of the bladders and the mechanical force exerted on the skin disappeared. In this system, the control and monitoring ports were the two separated parts. The time-domain dynamic pneumatic pressure and the skin force pressure were monitored in real time and displayed on the screen of the terminal unit. The software interface revealed and specified the quantitative and qualitative relationships between the pneumatic pressure inside the bladders and the interface contact pressure on the skin surface.In this study, a multi-functional software interface was developed to monitor, visualize, and record data details collected via Bluetooth in real time . The user-defined input parameters  and real-time output parameters  pressure, average pressure, minimum pressure, and pressure morphological mapping detected by the engineered soft sensor) were set in the interface of the developed morphological pressure mapping system. Moreover, the variations in the skin pressures sensed by each row and line cells of the soft sensor array were simultaneously displayed in the interface of the software with changes in the pneumatic air supplied by the pump controller in the inflation‒deflation cycles. As shown in An intermitted pressure mode of 23 s‒7 s‒7 s input by the user on the software interface  was adopFirstly, the developed software received the input parameters by the user for analysis and the DAQ board initiated the ports for receiving the orders. Then, the software opened the port for receiving data and setting the timer for controlling the compression cycle and treatment time . When the DAQ board received the first order  from the software and the chip timer collected the pressure data from the soft sensor, a real-time pressure monitoring process started. The pneumatic pump worked for 23 s to inflate the bladder with a constant pneumatic air mass flow. At the same time, the software created the lines or morphologic mappings to display pressure profiles in the software interface based on the received data.Secondly, following the timer control order , the pneumatic pump stopped working, and the valves closed to maintain the pneumatic pressure inside the bladders for 7 s. Then, the deflation valves opened in the next 7 s  to allow the pneumatic pressure inside the bladders and the contact pressure on the skin surface to release till disappear accordingly.Finally, one compression cycle was completed and followed by the next cycles repeatedly. In this system, the control and monitoring ports were two separated parts to ensure the real-time monitoring smoothly. The quantitative and qualitative pneumatic compression, skin pressure and their dynamic relations were displayed on the screen of the terminal unit as shown in 0d indicates the initial thickness of the bladder at the initial inflation stage and 0h represents the thickness of the expanded bladder at the maximum inflation or holding stage. t1, t2, and t3 indicate the inflation, holding, and deflation times of the bladder, respectively. sP presents the pressure gap between the detected skin pressures and the pneumatic pressure inside the bladders. d0P presents the pressure gap between the contact skin pressures and the pneumatic pressures inside the bladders at the initial inflation stage, and h0P presents the pressure gap between the skin pressures and the pneumatic pressures inside the bladders at the holding stage of the compression treatment.It can be seen that the skin pressures altered due to variation in the corresponding pneumatic pressures in a similar trend profile within one compression cycle. However, the values of skin pressures were lower than those of the pneumatic pressures in general. The energy produced by the inflated airtight bladder not only has to counteract the resistance of the bladder shell for expanding, but also has to deform the soft tissue to transform the aerodynamic force to the biomechanical force, thus promoting hemodynamics for venous return. Contact condition between the IPC bladders and the lower limbs largely influence the displacement (deformation) of contact interface because the human lower limbs are irregular in the cross-sections with varying surface curvatures and tissue stiffness, for example, the tibia bone at the anterior calf with a less radius of curvature (ROC) but higher stiffness, and the gastrocnemius located at the posterior calf with a greater ROC but lower stiffness. Such anatomic geometrics and biomaterials vary the interface pressure synergistically in the compression cycle. In addition, the pneumatic energy inside the bladder may be lost during the transmission process due to potential leakage, material fatigue, or consumption in the expansion on the opposite wall far from the skin surface. These could also be the potential reasons for the resultant lower skin pressures.It can be seen that , the defIn this study, a dynamic interface pressure monitoring system was designed and developed to detect and visualize interface (skin) pressures induced by an IPC device in 2D and 3D morphological mapping. A sandwich-structure matrix soft sensor was used as the core element with the developed hardware and software systems to demonstrate the real-time monitoring ability and to vividly display the sustained and intermittent pneumatic pressure by using IPC for different inflation‒deflation compression cycles. The dynamic interface pressures in 2D and 3D views, the quantitative pneumatic pressures inside the bladder, and their dynamic relations were integrated to simultaneously display in the designed interface of the proposed dynamic pressure monitoring system. The system can not only detect specific pressures at the targeted zones but also conduct morphological pressure mapping in a continuum interface. The novel dynamic pressure monitoring system presents the favorable capability of intuitively reflecting biomechanical forces and their variations under rhythm pressures by IPC. This capability provides valuable technical guidance for performing parameter design of the high-performance pneumatic compression device. The IPC device design can be realized by visualizing, quantifying, and real-time analyzing the variations and relations of the contact condition of the IPC and lower limb, pneumatic pump control, and bladder morphologies, thus improving compression therapeutic efficacy and fulfilling personalized demands in daily treatment.


In order to monitor the health condition of structures in a more sensitive and accurate way, a novel and universal methodology called direct coupling mechanical impedance (DCMI) for characteristic signatures extraction is presented in this paper. This methodology is used to obtain DCMI signatures from measured raw signatures (RSs) with the surface-bonded piezoelectric sensors (PZT), which is developed from a pertinent electromechanical impedance (EMI) theoretical model for surface-bonded circular PZT. The proposed DCMI methodology has the advantages of simple calculation and magnifying the signatures when compared with the existing methods. Combining the extracted DCMI signatures with the root mean square deviation (RMSD) index is able to quantify the correlation between the health condition and the signatures variation more effectively. To verify the effectiveness of proposed DCMI methodology, experiments are conducted on aluminum plates and a part of fuselage in detail. The experimental results sufficiently demonstrate that the presented universal DCMI methodology possesses better sensitivity than the raw signatures when utilized for evaluating the health condition of metallic structures, including those made of metal-matrix nanomaterials. Fatigue, external impact and corrosion are factors that are likely to cause incipient damages inside structures, including those made of metals, metal-matrix nanomaterials, which even lead to a disastrous structural failure. Hence, discovering and diagnosing the damages on the structures made by metal or metal-matrix nanomaterials in time is an extremely important issue for primary/secondary bearing structures, especially in the aeronautic community.The conventional technique to detect the internal damages inside structures is non-destructive testing (NDT). The typical damage detecting methods of NDT include visual inspection ,2, ultraTo conduct a study on the issue of on-line monitoring of metal-matrix  structures, a universal direct coupling mechanical impedance (DCMI) methodology is presented. Then, a derivation of integrating the DCMI with the index of root mean square deviation (RMSD) is also presented to quantify the correlation between damages and signatures variation. It should be particularly pointed out that the DCMI is also applicable to the structures made of metal-matrix nanomaterials. This is because these structures usually show some similarities in mechanical behaviors with the structures made of conventional metals. Hence, the verifying experiments are only conducted on aeronautic 7075-T6 aluminum alloy plates (specimen-1 and -2) and a part of aircraft fuselage (specimen-3), which are selected as typical metallic structures in this paper. It should be pointed out that the heat treatment of aluminum alloy used in experiments undergoes solution treatment first and artificial aging second. These specimens are used for simulating different structures to verify the feasibility of DCMI methodology, respectively. The experimental results demonstrate that with the use of the proposed DCMI signatures extraction approach, more sensitive signatures and better monitoring results can be obtained when compared with the measured raw signatures.r, thickness = h) can be written as Equation (1) in plural field. The meaning of involved variables has been enumerated in nomenclatures section.As derived and demonstrated in our previous investigation , a novelThe current existing EMI analytical models ignore the influence of the adhesive layer beneath the PZT while this influence is considered in the EMI model presented in this section. Moreover, a simpler method is also proposed to compute the local mechanical impedance (LMI) of host structures. tigation . Referritigation , the admAccording to Equation (2), the component I only depends upon the material parameters of PZT, while component II partly depends on the PZT-host structure interaction due to the If we separately define the component I and component II as non-sensitive term and sensitive term to damages, then Equation (2) can be rewritten as Equation (3):Previous investigations ,55,56 haGenerally, her work , the proAs mentioned above, despite researchers deeming that the susceptance plot cannot be used for health monitoring, after filtering the component I from the raw signatures, the The sensitive component in Equation (2) can be expressed in the plural form as Equation (9):As described above, a newly developed universal signatures extraction approach DCMI is developed based on the novel EMI model. The DCMI signatures are calculated with a deduced fraction including the mechanical impedance of PZT and host structure, as well as adhesive layer beneath the PZT, which is a brand-new idea for impedance/admittance signatures processing on the basis of corresponding analytical model.Although research on extracting alleged effective impedance signatures of skeletal structures from the raw signatures has been performed by Bhalla and Soh , there iFirstly, in order to write conveniently in the following process of derivation, the complex expressions of f denotes the “fraction”. The expression of Secondly, symbol Furthermore, defining the expression of fX) and imaginary part (fY) are able to be determined, and the DCMI signatures can then be obtained accordingly. The main advantage of extracted DCMI signatures is that there is no need to calculate the precise values of With the use of Equation (13), the real part :ta point .In order to validate the feasibility of presented DCMI methodology and damage quantification approach, the confirmatory experiments are amply conducted in this section. The raw signatures are separately acquired via surface-mounted PZT on two aluminum plates, which are separately simulating the single damage in-situ propagation and multi-damage ex-situ development. Then, the acquired raw signatures are used for extracting DCMI signatures according to the aforementioned calculation process. The experimental setup involved in this section concentrates on investigating the correlation between the damage growth and signatures variation for metallic structures.By separately measuring the raw signals under healthy and damage conditions, any damage giving rise to structural impedance variation can be evaluated. Empirically, drilling holes on the structural surface and enlarging its diameter is an easier and more controllable work than fabricating other types of damages. Hence, the damage condition is just simulated through drilling a hole on the aluminum plate (specimen-1), whose thickness is 5 mm. The diameter of drilled hole is small enough compared with the size of aluminum plate in terms of damage area ratio to total surface area, as shown in Generally, the impedance signal processing is focused on the frequency scope below 500 kHz, while a few of investigations concentrate on high frequency. Hence, a scope from 500 kHz to 2.5 MHz is selected in experiments to study the effectiveness of DCMI under the action of high frequency. The data are acquired with a precise WK-6500B impedance analyzer with a step of 5 kHz. The PZT is excited under harmonic voltage whose amplitude is 1 V. The hardware and the instrumentation used in our experiments have been shown in For the purpose of studying the effectiveness of DCMI extraction approach in a more extensively way, another experiment was also conducted on the aluminum plate (specimen-2). Seven holes with an identical diameter of 4 mm were drilled on the specimen surface. Then the raw signatures were acquired with impedance analyzer in the states from ST-0 to ST-7, respectively. The testing specification on specimen-2 is shown in To demonstrate the effectiveness of DCMI methodology in practical and engineering issues, a part of the fuselage was selected to study the damage growth on a complex structure. The specimen of fuselage used in the experiment is shown in In this section, comparative analyses on the basis of obtained raw signatures and extracted DCMI signatures are performed. Moreover, so as to investigate the correlation between damage development and raw/extracted signatures variation, histograms of RMSD are drawn. Then, the effectiveness of presented novel DCMI extraction approach is discussed.As above-stated, the damage in-situ propagation was simulated by enlarging the diameter of the drilled hole. The raw conductance (fX) for specimen-1. In fY). As above-mentioned, the susceptance signatures are conventionally believed having weak interactions with the structural damage; thus, it is usually not adopted to investigate the correlation between damages and signatures variation in EMI techniques. In By using the developed DCMI approach to extract the corresponding signatures from measured raw signatures, it can be clearly seen that there are visible peaks in For the purpose of comparing the extracted signatures and raw signatures, as well as demonstrating the effectiveness of DCMI extraction approach comprehensively, an index of RMSD is adopted to quantify the correlation between signatures variation and damage in-situ propagation. The RMSD values are separately calculated based on the raw signatures and extracted DCMI signatures. The calculated RMSD values are shown in In order to investigate the effectiveness of DCMI approach more extensively and sufficiently, apart from the study of damage in-situ propagation on specimen-1, another experiment is also conducted on specimen-2. As stated in fX). In order to study the variation in amplitude effectively, the detailed views of the main characteristic peak are plotted based on raw signatures and extracted signatures within the selected frequency scope, respectively. In fY). In In order to compare the extracted signatures with raw signatures, as well as demonstrating the validity of DCMI extraction approach comprehensively, the RMSD is also adopted to quantify the correlation between signatures variation and damage severity development in this multi-damage case. Similar to the experiment performed on the specimen-1, the RMSD values are separately calculated based on the raw signatures and extracted DCMI signatures, including their real part and imaginary part. The calculated RMSD values of specimen-2 are shown in fX). In fX) are calculated. More regular and apparent changing features are observed in terms of the bar’s height variation.fY). By taking a closer look at the With the setup shown in In order to take a closer look at the first peak, a detail view is plotted both in For the purpose of taking a closer look at the first peak, a detail view is also plotted in To investigate the signatures changing with damage growth, the RMSD values were also calculated in terms of Equation (14); however, the RMSD values of extracted DCMI signatures are slightly different from that of specimen-1 and -2 because of the signatures acquired from the fuselage being too complex. The main difference lies in the number of data point used for RMSD calculation. For the purpose of reducing data processing scale, a novel method was developed. All of peak points in In this section, a DCMI extraction approach was used in experiments to obtain more sensitive signatures to study the correlation between damages growth and signatures changing. By plotting the curves and comparatively analyzing their varying regularity, it can easily be found that the smoothness of each curve is improved. Obvious variations between each curve are found in terms of detail view, as well as the differences between RMSD bars. Hence, the effectiveness of the DCMI approach is convincingly demonstrated and it has potential application in the community of EMI-based structural health monitoring.In this paper, a universal DCMI signatures extraction approach is developed from a novel EMI model to monitor the health condition of metallic structures with piezoelectric sensors. Theoretically, any solid body possesses mechanical impedance; thus, the methodology is apparently suitable for structures composed of metal, metal-matrix nanomaterials and composites, the impedance of which can be measured with surface-bonded PZT. With the use of DCMI method, some issues of health monitoring are likely to be solved in an effective way, which has practical significance in engineering structures. The developed extraction approach is used to obtain a series of more sensitive signatures to investigate the influence of damages on structures. Three experiments, damage in-situ and ex-situ development on aluminum plates, as well as aircraft fuselage, are conducted to study the effectiveness of the methodology, respectively.(1)As long as the PZT is surface-bonded on the structures, the DCMI methodology can be used for monitoring the damage propagation on simple and complex structures, which are made of metals, metal-matrix nanomaterials and composites.(2)More obvious peaks can be extracted from the raw signatures with the use of DCMI methodology, which may have potential applications in the SHM community in the future.(3)The correlation between damages growing and signatures variation can be well quantified via combing DCMI signatures with the RMSD index.(4)The DCMI methodology can extract more sensitive signatures from measured raw signatures in a simpler way.(5)The susceptance signatures can also be used for assessing the regularity of damage changing after extracting signatures with the DCMI.Furthermore, in order to quantify the correlation between damages and EMI signatures variation, the statistical index RMSD is adopted. Through combining the DCMI signatures with RMSD, the influence on structures caused by damages in-situ propagation or ex-situ development are capable of being well quantified. Hence, the feasibility and effectiveness of DCMI methodology can then be convincingly demonstrated. Based on the comprehensive and detailed comparison, the following remarks can be made:


The Internet of Vehicles (IoV) is attracting many researchers with the emergence of autonomous or smart vehicles. Vehicles on the road are becoming smart objects equipped with lots of sensors and powerful computing and communication capabilities. In the IoV environment, the efficiency of road transportation can be enhanced with the help of cost-effective traffic signal control. Traffic signal controllers control traffic lights based on the number of vehicles waiting for the green light . So far, the utilization of video cameras or sensors has been extensively studied as the intelligent means of the vehicle queue length estimation. However, it has the deficiencies like high computing overhead, high installation and maintenance cost, high susceptibility to the surrounding environment, etc. Therefore, in this paper, we propose the vehicular communication-based approach for intelligent traffic signal control in a cost-effective way with low computing overhead and high resilience to environmental obstacles. In the vehicular communication-based approach, traffic signals are efficiently controlled at no extra cost by using the pre-equipped vehicular communication capabilities of IoV. Vehicular communications allow vehicles to send messages to traffic signal controllers  communications) so that they can estimate vehicle queue length based on the collected messages. In our previous work, we have proposed a mechanism that can accomplish the efficiency of vehicular communications without losing the accuracy of traffic signal control. This mechanism gives transmission preference to the vehicles farther away from the traffic signal controller, so that the other vehicles closer to the stop line give up transmissions. In this paper, we propose a new mechanism enhancing the previous mechanism by selecting the vehicles performing V2I communications based on the concept of road sectorization. In the mechanism, only the vehicles within specific areas, called sectors, perform V2I communications to reduce the message transmission overhead. For the performance comparison of our mechanisms, we carry out simulations by using the Veins vehicular network simulation framework and measure the message transmission overhead and the accuracy of the estimated vehicle queue length. Simulation results verify that our vehicular communication-based approach significantly reduces the message transmission overhead without losing the accuracy of the vehicle queue length estimation. Nowadays, the automobile industry is focused on developing smart vehicles equipped with various sensors, computing power and communication functionalities. The Internet of Vehicles (IoV)  is part The efficiency of road transportation depends heavily on the performance of traffic signal controllers. Traffic signal control systems have been rapidly evolved during the last several decades . Thanks Therefore, in this paper, we propose to use vehicular communications for traffic signal control because the vehicular communication functionality, one of the essential capabilities of IoV, has many advantages like no additional operational  cost, lightweight computing, resilience to lighting condition  and resilience to harsh road condition . In controlling traffic signals, we can substitute vehicular communications for video cameras and sensors, which can be achieved by making vehicles notify traffic signal controllers of their existence via V2I communications so that traffic signal controllers can estimate how many vehicles are waiting for the green light .For vehicular communications, the IEEE Wireless Access in Vehicular Environments (WAVE)  and the In our previous work , we propIn this paper, to overcome this sequential transmission characteristic of the distance-based mechanism, we propose a new mechanism that can reduce the number of messages transmitted to the traffic signal controller. This newly proposed mechanism is called the sector-based mechanism. The sector-based mechanism further reduces the number of the vehicles sending messages to the traffic signal controller by adopting the concept of sectors. There can be a number of sectors in a road segment between two consecutive intersections. A sector of a road segment is a subarea of the road segment. Instead of all the vehicles waiting for the green light having rights to perform V2I communications, only the vehicles within the sectors are allowed to transmit messages to the approaching traffic signal controller. That is, the set of candidate vehicles for sending messages to the traffic signal controller of the sector-based mechanism is smaller than that of the distance-based mechanism, resulting in less transmissions to the traffic signal controller. For the performance evaluation, intensive simulations are carried out by utilizing the vehicle network simulation framework Veins  based onThe rest of the paper is organized as follows: in In this section, we first go over the definition of the vehicle queue. The vehicle queue is defined as a line of the vehicles stopping at the red light and the vehicles approaching to the stopping vehicles at speeds slower than the given stopping speed in the Highway Capacity Manual . In 20]20], the For the estimation of the vehicle queue length, first of all, the vehicles waiting for the green signal have to be recognized, which can be accomplished by utilizing devices like video cameras mounted on fixed roadside structures such as traffic signal controllers or like sensors installed under the pavement. The time-stringent control of traffic signals requires real-time processing of video frames and the accurate measurement of vehicle queue length requires sophisticated deployment of sensors.In the video-based approach, the first thing to do for the vehicle queue length estimation is detecting vehicles from video frames in real time. After the vehicle detection process, vehicles are tracked and counted in real time. Thanks to various computer vision techniques and hardware capabilities, real-time processing of vehicle detection, tracking and counting becomes possible ,5,6,7,8.In the sensor-based approach, various types of sensors, like inductive loop detectors, ultrasonic sensors, magnetometers, radar/lidar based sensors, etc., are installed near to intersections for vehicle detection, tracking and counting ,15,16,17Instead of using video cameras or sensors, the mechanisms utilizing GPS-mounted probe vehicles have been proposed for the estimation of the vehicle queue length ,31,32,33The aim of our mechanisms differs from that of the probe vehicle-based mechanism in that our mechanisms use V2I communications for the real-time traffic signal control. That is, we consider the environment where the traffic signal controller detects vehicles through V2I communications and estimates the length of the vehicle queue and, then, controls the traffic signals. As the age of IoV is approaching ,35, all The road is composed of road segments each of which has vehicles heading to an intersection with a traffic controller. In this paper, we consider a road segment starting from Rlen see . The vehIf we allow any vehicles in the vehicle queue to transmit VI messages , the number of VI message transmissions will be the same as the number of the vehicles in the vehicle queue. From the perspective of the accuracy in estimating the vehicle queue length, this mechanism is the best. However, this will lead to higher possibility of collisions. Therefore, we need to figure out the way of reducing the number of VI messages. The optimal way of achieving this is to allow only the last vehicle in the vehicle queue to transmit a VI message. However, a vehicle has no means of knowing that it is the last vehicle in the vehicle queue because it cannot know whether there are any vehicles following itself. If the V2V communication is adopted for that purpose, a vehicle can know whether there are any following vehicles or not. Even with the V2V communication, in the situation of contiguous vehicles running on the road, if a vehicle does not send a VI message because of any following vehicles, the transmission of a VI message may be delayed, resulting in non-reactive traffic signal control. Therefore, we proposed a mechanism, the distance-based mechanism, in which an IVQ vehicle sends a VI message according to its distance from the intersection in our prior work . In the An IVQ vehicle can transmit a VI message From the collected VI messages, the traffic signal controller estimates how many vehicles are in the vehicle queue. The estimated length, Here, However, at the red light, vehicles tend to line up one after another with slowing down their speeds with some time gap and may send VI messages to the traffic signal controller sequentially because of the larger time gap between the stopping times of two back-to-back vehicles compared with the random time delay gap between them determined according to the distance from the upcoming intersection. This may result in non-optimal VI message transmissions because of the VI message transmissions of the vehicles in the middle of the vehicle queue. Therefore, in the following subsection, we propose a new mechanism that can further reduce the VI message transmission overhead by limiting the areas in which vehicles are allowed to transmit VI messages.The objective of the sector-based mechanism is to reduce the number of candidate IVQ vehicles to transmit VI messages. Compared with the distance-based mechanism in In order to reduce the number of VI message transmissions from a sector, if an IVQ vehicle overhears the VI message transmission from another IVQ vehicle in the same sector, it gives up its transmission. The sector identifier is included in the VI message so that multiple VI messages with the same sector identifier cannot be transmitted.The starting positions of sectors are determined by the sector length Then, the number of sectors is the largest In the sector-based mechanism, the traffic signal controller collects VI messages and computes the length of the vehicle queue based on the collected VI messages, like in the distance-based mechanism. However, because there exists a gap  between two consecutive sectors, the last vehicle in the vehicle queue may be located at the last sector, Here, Simulations were performed with the vehicular network simulation framework Veins  based onWe evaluate and compare our distance-based and sector-based mechanisms with the Naïve mechanism for various simulation scenarios. The Naïve mechanism measures the vehicle queue length by making every IVQ vehicle to send a VI message to the traffic signal controller. The degree of saturation (%) is taken as one of the performance affecting factors, which is a ratio of demand (the number of vehicles moving on the road segment) to capacity (the maximum possible number of vehicles on the road segment) in percentage. We evaluate the performance of the proposed mechanisms for two degree of saturation scenarios, 30% and 50%. For the sector-based mechanism, the sector length The accuracy of the vehicle queue length estimation: The accuracy of the estimated vehicle queue length is measured in terms of the arithmetic mean (AM), the mean absolute deviation (MAD) and the mean absolute percentage error (MAPE). With a given data set, AM is obtained by dividing the sum of the given data set by the set size. MAD is the average of the absolute deviations from the mean (AM) of the given data set and MAPE is a measure of prediction accuracy in percentage. If The MAD of The number of VI message transmissions: This performance factor is used for measuring the message transmission overhead sent from the IVQ vehicles to the traffic signal controller. The number of VI message transmissions is measured by counting in the original transmissions and the retransmissions of VI messages during the simulation time.The VI message transmission delay: The transmission delay of a VI message is the time taken for a VI message to be delivered to the traffic signal controller successfully.For the performance analysis, we measure three performance factors:For the performance evaluation, we have executed five simulation runs for each mechanism and for each In order to measure the accuracy of each of the proposed vehicle queue length estimation mechanisms, So far, we have analyzed the simulation results presented in tables for two cases of the degree of saturation. The common objective of the distance-based and the sector-based mechanisms is to estimate the vehicle queue length accurately with less message transmission overhead compared with the Naïve mechanism. Thus, we depict the accuracy in terms of AM and the number of VI message transmissions of the distance-based and the sector-based mechanisms in In [Performance] As we have observed from the simulation results and analysis in [Required Capabilities] Both the distance-based and the sector-based mechanisms require the GPS capability for the location information and a digital map for the intersection information and the V2X communication capability for VI message transmissions. The sector-based mechanism additionally requires the information on sectors. The capabilities required by both mechanisms are easily installable and do not require additional cost once vehicles are pre-equipped with GPS and V2X communication modules and digital maps. For situation awareness, smart vehicles are essentially equipped with GPS and V2X communication modules. Thus, both of our mechanisms are good for utilizing the intrinsic capabilities of smart vehicles. In the case of the sensor-based approach, sensors require communication capabilities like wires to transmit monitoring data to traffic signal controllers and the capabilities to lengthen the lifetime of sensors such as energy harvesting. For the video-based approach, the real-time processing capabilities are required to control traffic signals at the right time and the countermeasures for adversary environmental conditions are required to acquire video images with manageable qualities.[Operational Cost] Our V2X communication-based mechanisms require only the communication modules installed in vehicles which are intrinsic capabilities of smart vehicles for situation awareness. So, there is no additional installation and maintenance cost incurred solely by our mechanisms. Besides, the installation and the maintenance of the communication modules can be easily done only through vehicle inspections at vehicle manufacturing factories and vehicle maintenance centers. On the other hand, the video camera- and the sensor-based approaches require manual installation and maintenance of devices and/or communication links on the road, incurring a lot of efforts and costs.[Robustness] Our V2X communication-based mechanisms are resistant to harsh outside-world environments because they are not affected by weather, lighting and road pattern. The only obstacle is any objects hindering communications between vehicles and traffic signal controllers, but wireless communications are more resilient to obstacles than the line-of-sight nature of video cameras. With regard to robustness, the sensor-based approach is the weakest because sensors are susceptible to harsh road conditions like noise and vibrations. On the other hand, the V2X communication-based and the video-based approaches are not affected by road conditions. As for weather conditions, the video-based approach is the weakest because rain or snow will blur video images. Therefore, we can assert that our V2X communication-based approach is the best from the perspective of robustness.[Extensibility] Based on the vehicular information, like moving direction and speed, obtained from V2I communications, traffic signal controllers can cooperate for more enhanced control of traffic signals. On the other hand, the mechanisms using sensors are not capable of supporting cooperation among traffic signal controllers because there is no way of acquiring or deducing the direction of a vehicle. The video-based approach can guess the next road segment that a vehicle is heading towards via the lane on which the vehicle is. However, it has no way of knowing the route of a vehicle. On the contrary, our mechanisms may let traffic signal controllers know the routes of vehicles via VI messages so that they can collaborate on the optimization of traffic signal control in a wide area. By means of V2X communications, traffic signal controllers can collect various kinds of information from vehicles so that they can accomplish many valuable functionalities.In the previous subsection, we have observed the microscopic performance of our V2X communication-based mechanisms in estimating the vehicle queue length for traffic signal control. In this subsection, we will discuss the pros and cons of our mechanisms in a broad sense, including comparisons with other relevant mechanisms. The aspects taken for the discussion are performance, required capabilities, operational cost, robustness, and extensibility:From the above discussion, we can conclude that the V2X communication-based approach is the best in estimating the vehicle queue length. The only limitation that we confront with is the penetration ratio of the vehicles equipped with V2X communication modules on the road. The optimistic point is that the immense efforts to realize the cooperative intelligent transport system (C-ITS) in Europe (known as the connected vehicle technology in the United States) ,35 will The Internet of Vehicles (IoV) allows vehicles to communicate with anything in the Internet, including vehicle themselves and traffic signal controllers on the road. In this paper, we focused on a cost-effective ITS application of controlling traffic signals in real-time in the IoV environment. Vehicular communication capabilities enable vehicles to communicate directly with traffic signal controllers  located at intersections so that traffic signal controllers can estimate how many vehicles are waiting for the green light . Thus, with V2I communications, we can avoid using conventional computing-intensive video cameras or high operational-cost sensors for the real-time estimation of vehicle queue length. Furthermore, V2I communications are robust compared with video-taking and sensing in the harsh road environment with full of obstacles.In the V2X communication-based approach, both the V2I communication overhead and the accuracy of the vehicle queue length estimation must be considered. For the reduction of V2I communication overhead, we proposed the distance-based mechanism in  and newlFor the performance comparison of our mechanisms, we carried out simulations based on the Veins vehicle network simulation framework for various performance determining factors and analyzed the performance in terms of the message transmission overhead and the accuracy of the vehicle queue length estimation. The message transmission overhead indicates the utilization of constrained wireless link resource, so less message transmissions are preferred. From the simulation results, we showed that our mechanisms significantly reduce the number of message transmissions without losing the accuracy of the estimated vehicle queue length, compared with the Naïve mechanism. The sector-based mechanism decreases the message transmission overhead about a sixth of the Naïve mechanism and a third of the distance-based mechanism. This indicates that our V2X communication-based mechanisms are especially good for the road situation with many vehicles. Also, we verified that the proper selection of sector length and inter-sector distance is important for achieving acceptable performance of the sector-based mechanism. According to the simulation results, the sector length 10~20 m and the inter-sector distance 10~20 m are good enough for the case of the vehicle length 5 m and the inter-vehicle distance 2.5 m. Because sector length and inter-sector distance are easily tunable parameters, our sector-based mechanism can be applied to any road environments at no extra cost. Also, based on the discussion in