Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision. Natural disasters and local wars have become frequent in recent years. According to statistical data, the proportion of mechanical injuries to the human body caused by heavy object crushing after disasters and conflicts has reached 48.37% . Crush sResearch on the application of bio-radar in the field of vital sign detection in casualties can be traced back to the 1980s. KunMu Chen from Michigan State University in the United States used an X-band bio-radar to detect human subjects behind cinder brick walls . This stBy summarizing the literature, we found that bio-radar can be used for the non-contact detection and localization of casualties under the conditions of penetrating non-metallic materials (such as bricks and wood). If more accurate physiological parameters of the injured can be extracted from the radar echo and used to determine the compression state of the injured, rescue workers can be guided to scientifically formulate rescue sequences and prepare relevant treatment drugs and medical equipment in advance to ensure the survival rate of the trapped injured person. However, one of the main challenges is that the vital signs of trapped individuals are easily affected by clutter and noise. Another challenge is identifying the compression state of injured individuals by fully utilizing hidden features in bio-radar data.Currently, in the field of through-wall vital sign detection, commonly used bio-radars can be categorized into two types: continuous wave (CW) radar  and ultrIn summary, this study proposed a non-contact detection and recognition method based on bio-radar for the compression state of post-disaster wounds. It was divided into two parts: human-life signal extraction and post-disaster compression state recognition. The organizational structure of the study is as follows: In The UWB bio-radar is a pulse radio-based radar system that utilizes the Doppler effect to detect biological targets. Radar emits signals in the form of pulses with very short durations in the nanosecond range. The corresponding spectra of these emitted signals were concentrated in the frequency range of 30 MHz to 8000 MHz. The electromagnetic waves in these frequency bands have the ability to penetrate some non-metallic materials, making them suitable for application scenarios of penetrating debris and debris detection. Using a wide frequency band, the system can effectively obtain rich scattering information about the target, which is beneficial for the detection of detailed information.When operating UWB bio-radar, the radio frequency (RF) synthesis unit generates a series of pulse signals with the same pulse repetition period under the control of a microcontroller in the radar transmitter. These pulse signals are split into two channels as trigger signals. One channel of the pulse generator generates an ultra-wideband signal, which is amplified by a power amplifier and transmitted into space. The other channel enters a delay circuit, which is adjusted by the microcontroller to generate a corresponding delay and form a local reference signal. The waveform of the echo reflected by the target is similar to that of the transmitted waveform but is loaded with the Doppler shift caused by the motion of the human surface. Subsequently, the antenna receives the modulated echoes, and the received signal was filtered and amplified using a low-noise amplifier. When these received signals reach the digital integrator inside the radar, there is a time shift between the echo and range-gate signals. If the time delay of the echo signal matches that of the range gate signal, a series of output levels are obtained. These levels are then sampled using an ADC and converted into digital signals, which are transmitted to an upper-level computer via a universal serial bus for further processing. The center frequency of the UWB bio-radar used in this study was 7.29 GHz, with a bandwidth of 1.4 GHz, a pulse repetition frequency of 15.18 MHz, and a slow-scanning frequency of 17 Hz. The radar system utilizes a pair of microstrip antennas placed side by side, which offer advantages such as small size, high gain, and good directivity. The fast time dimension contains 93 distance-sampling points, corresponding to a maximum detection distance of 5 m. The optimal detection angle is facing the human target, within an angle range of ±30 degrees in the sagittal plane. This deployment enables the radar to efficiently receive signals reflected from the body surface, enhancing the signal amplitude and signal-to-noise ratio. Once the angle range is exceeded, the target will almost completely out of the radar main lobe, resulting in a significant decrease in amplitude and SNR. The structure diagram of the UWB bio-radar system and the schematic diagram of non-contact cardiopulmonary activity detection are shown in A fast-time signal is a data vector composed of all the distance sampling points of a bio-radar at a certain acquisition time. Fast-time signals at different times are sequentially concatenated to form a The collected raw radar echo data not only contain vital signs of the detected human body targets but also contain a large amount of static background interference. These static background interferences can be filtered out by subtracting the fast-time signal mean from the echo data. The background removal steps for the radar echo data can be expressed asAdditionally, electromagnetic attenuation occurs during the propagation of radar waves through obstacles, resulting in the received echo amplitude being significantly smaller than that of the initial transmitted pulse. If normalization processing is performed based on the collected channel signals, compensation can be provided for distant target echoes. The steps of attenuation compensation preprocessing can be expressed asFinally, by calculating the energy sum of each position point in By preprocessing radar echoes, it is possible to suppress interference while extracting radar echo signals from the target location. However, because the vibrations caused by human heart motion on the body’s surface are much smaller than the chest expansion caused by breathing, the radar cardiac signals reflected in the echo signals are very weak and are easily overshadowed by strong background noise and slow-drift interference caused by slight movements of the human body. This makes it difficult to separate and reconstruct the radar vital signs of injured targets from the echoes, thereby affecting the determination of the compression state of injured individuals. To address these issues, this study proposed a non-contact sensing and identification method for the post-disaster compression state of injured individuals based on UWB bio-radar. The method consists of two steps: first, the reconstruction of radar vital signs from noisy echo signals, and second, the automatic recognition of the compression state of injured individuals using neural networks. A flowchart of the proposed method is shown in Variational mode decomposition (VMD) is an adaptive and completely non-recursive signal-processing method. The objective was to iteratively determine the optimal solution of the variational model and determine the bandwidth and center frequency of each mode. Therefore, the VMD can deconstruct a given radar echo i-th IMF obtained through decomposition, and i-th IMF. By introducing the quadratic penalty term Subsequently, the variational problem of the VMD method is solved using the alternating direction multiplier iteration algorithm, and Finally, given the accuracy i-th IMF and the original signal as an example, the formula is as follows:Particle swarm optimization (PSO) is an evolutionary computation technology. It adjusts the fitness of particles to the environment by iteratively comparing the optimal state of individual particles and the global optimal state in the fitness function area and then finds the optimal solution in the area. When tuning the parameters of VMD, selecting the appropriate fitness function is the key to the smooth operation of PSO. Due to the influence of the parameters i-th IMF, and Owing to the sensitivity of VMD to noise, the partial IMF center frequency j-th reconstruction component Therefore, the column indexes form a set of symbol sequences:(1)The maximum cross-correlation between each IMF and the radar echo signal after VMD processing was used as the fitness function of the PSO to optimize the VMD parameters.(2)The radar echo signal was processed based on the parameter-optimized VMD and decomposed into a series of IMFs;(3)The entropy value of each IMF arrangement was calculated separately;(4)The entropy values of each IMF were compared and filtered with Using Performing time-frequency domain analysis on the reconstructed human radar life signal can obtain the corresponding time-frequency images. These images can intuitively reflect the variation in the frequency components with time. Compared with one-dimensional radar life signals, such as breathing and a heartbeat, the time-frequency images are more suitable as inputs for network models to achieve identification of the compression state. A continuous wavelet transform uses finite length and convertible wavelet basis functions to enhance its local generalization ability. The commonly used wavelet bases include Haar and Morlet wavelets, Symlet, and the Db wavelet series. In order to select wavelet basis functions suitable for analyzing radar life signals, the four wavelet bases mentioned above were used individually to process the reconstructed radar life signals. The results based on different wavelet bases are shown in It can be seen that the Morlet wavelet had a clearer time-frequency resolution and was selected for the time-frequency analysis of the reconstructed radar life signals. The processed time-frequency images were used as the dataset for the subsequent network training and recognition.As a hierarchical neural network, a convolutional neural network (CNN) essentially imitates the working principles of the human visual system by constructing convolutional kernels of different sizes to extract unique local features from input data, thereby achieving accurate data recognition. A large number of experiments  showed tTo prevent gradient dispersion and network degradation caused by deepening layers while fully extracting feature information, this study proposed an improved CNN scheme based on multiscale feature extraction and a residual structure by referring to the inception structure in the GoogLeNet neural framework and combining it with residual networks. First, in a multiscale feature-extraction structure, parallel stacking is used to increase the network width, and the feature-extraction ability is improved by utilizing convolutional kernels of different sizes. The feature dimensions of the different branches were then concatenated to obtain a set of multichannel feature maps. For feature maps with a large number of channels, a channel attention mechanism is used to help the network learn more important feature channels. This not only improves the expression and classification performance of the network but also reduces the impact of redundant information and improves the robustness and generalization ability of the network. Finally, a 1 × 1-pixel convolutional kernel in the inception structure is used to downscale the input feature map, achieving the goal of reducing the amount of parameter computation, and thus, accelerating the network model training.Additionally, in a traditional CNN, the convolutional modules in the upper layer and the convolutional modules in the lower layer are usually end-to-end, but such a network structure often prevents the latter from utilizing the initial input information of the previous convolutional module, thereby limiting the feature-learning ability of the convolutional layer on the input data. Therefore, by introducing a residual structure with the characteristic of “cross layer connection”, the network can synchronously transmit the original features to the next layer during forward propagation, thereby avoiding information loss or redundancy. Finally, the extracted features are converted into one-dimensional data through the fully connected layer, and the classification results of the human compression state are output through the cross-entropy loss function. The structure of the multiscale feature learning network is shown in The detailed network parameters are shown in In order to recognize the compression state of the injured, it is necessary to preprocess the raw echo radar data first, and then use the PSO-VMD algorithm based on permutation entropy to denoise the preprocessed signal and reconstruct the radar human life signal. Due to the fact that the human vital sign characteristic information is mainly contained in the spectrum of the reconstructed radar life signal, the reconstructed human life signal is conducted via time-frequency analysis. Then, the time-frequency maps with compression state information are sent into a network to complete the training process. Finally, the trained network is applied to evaluate the type of compression state. An indoor free-space detection experiment was conducted to verify the performance of the proposed PSO-VMD algorithm based on permutation entropy. The subjects were detected without any obstacles by using UWB bio-radar. Simultaneously, two contact detection devices—an RM6240E pressure breathing detector and an IX-B3G portable electrocardiograph—were used for synchronous data collection as a reference. The proposed algorithm was applied to extract radar respiratory and cardiac signals from the radar echo signals. Finally, the radar respiratory and cardiac signals were compared with contact reference signals. The signals collected by the contact and contactless devices exhibited strong consistency. As shown in It should be noted that some damp building materials will cause significant losses above 1 GHz. In this work, to mainly verify the compression states recognizing the effectiveness of the proposed method, the optimal test scenario with an obstacle barrier of multi-layer dry gypsum board was set. In this experiment, five signals were collected from each participant, each lasting 30 s. Using the contact signal as a reference, the average respiratory rate and heart rate measurement error rates of each subject for the five signals are shown in To replicate a scene of human bodies trapped under the ruins of squeezing, radar signal collection experiments were conducted on 10 adult subjects  aged between 21 and 45 years using a self-developed squeezing platform. All ten volunteers signed an informed consent form. A self-developed squeezing platform was designed to simulate heavy objects pressing on a human torso after a disaster. To better replicate a real post-disaster scene and ensure the subjects’ safety simultaneously, the maximum weight that the subjects could accept was used as the upper weight limit of the heavy objects applied in the experiment, and two different states, namely, “compression” and “no compression”, were set. Considering the different trapped postures of buried personnel after actual disasters, two different postures, namely, “supine” and “prone”, were also set in this experiment. The four types of states for subjects with different compression levels and postures are listed in In the “no compression” state, four layers of 15 mm thick gypsum board were used as the obstacle between the subject and the radar, and the obstacle did not contact the subject’s body. The “compression” state refers to the situation where bricks were placed on a wooden board to build a compressive device with a total weight of 10 kg. The device was pressed on the subject’s body with its weight regardless of whether the subject was in a supine or prone position. This pressure was evenly distributed throughout the torso of the subject. The intensity of the pressure was determined by the contact area between the compressive device and the human body. Therefore, the pressure and intensity of pressure were independent of the subject’s weight, gender, and posture. To expand the total number of signals, four random detection distances between the UWB bio-radar and the subject within the range of 1.5 to 2 m were set to collect signals for each subject . Four sets of signals with different compression levels and postures were collected at each distance with a single collection time of 180 s. A control group under the detection scene with no target was also set up to simulate an actual post-disaster rescue. To avoid overfitting owing to the small sample size of the training set, a data augmentation method was adopted to expand the training set sample size. By adding random noise to the time-frequency images, the dataset was expanded three times to the original size, and the training and test sets were configured in a ratio of 8:2. This not only expanded the number of training sets but also enabled the neural network to adaptively learn to improve its anti-noise ability during the training process, which further enhanced the anti-noise performance and classification generalization ability. This study used Tensorflow 2.0 to build a classification network for compression state recognition and trained it based on the above dataset. The loss and accuracy curves in the training phase are shown in The overall classification accuracy of the network and the recall and precision of each category were calculated and are listed in To further verify the superiority of this method, the network was compared with a support vector machine (SVM) based on respiratory features and a 1D-CNN network based on radar life signals. The SVM based on respiratory features was shown to exhibit good classification performance in the field of human and animal identification using bio-radar . To succFrom Owing to the correlation between the probability of a subject suffering from crush syndrome and the duration of compression, the longer the duration of compression, the more severe the crush injury and the higher the incidence of crush syndrome. Therefore, wounded patients who may suffer from crush syndrome should be identified as soon as possible and the crushing of heavy objects should be relieved as soon as possible. If the injured limbs are exposed, a tourniquet should be used at the proximal end of the injured limb, and an alkaline liquid should be injected to correct acidosis and hyperkalemia and prevent the occurrence of acute renal failure.This study focused on the various challenges faced by bio-radar in the field of post-disaster life detection and proposed a full-process solution based on UWB bio-radar for post-disaster casualty life signal detection and compression state recognition. In this scheme, a parameter-optimized VMD was proposed to process the radar echo signal, and the permutation entropy algorithm was applied to achieve a fast and accurate reconstruction of the subject life signal. Subsequently, an improved CNN based on multiscale feature extraction and a residual structure was proposed to achieve automatic recognition of the compression state. This network could fully learn the input data feature information while preventing gradient dispersion and network degradation caused by deepening the layers. According to the experimental results, the accuracy of this method in identifying the compression state of the subject improved by 4.19% and 6.01% compared with the SVM and 1D-CNN, respectively. This study is of great significance for extracting the vital signs of squeezed subjects and guiding rescue workers in performing correct and scientific rescues in practical applications.However, this study has certain limitations. On the one hand, the trapped postures of the simulated wounded were only summarized as supine and prone in this work, without fully considering the various trapped postures that a rescue worker may face after a disaster occurs. To protect the subjects outdoors during the obstacle experiment, we controlled the weight of the cover plate acting on the human torso to only 10 kg. In actual disasters, the weight of the crushed ruins may be much greater than this value. In addition, the rubble that covers the injured in a real-life earthquake is randomly orientated and highly scattered. Therefore, in the future, we will construct more realistic post-disaster rescue scenarios and apply different modulation schemes of UWB radar to research more complex and heavier compression state identification.Owing to its ability to detect vital signs caused by the human heart and lung activity at a certain distance and through certain obstacles, UWB bio-radar is often used in post-disaster searches and rescues to detect the life signals of subjects in buried situations. However, noise from the detection environment and spontaneous sway interference of the trapped human body cause the detected radar life signals to exhibit a low signal-to-noise ratio and strong randomness, making it difficult to obtain more effective information and further evaluate the compression state. Focusing on the above problems, we proposed a method that combines PSO-VMD based on permutation entropy and an improved CNN based on multiscale feature extraction and residual structure. The effectiveness and progressiveness of this method were verified with experimental results. This study provides a foundation for further in-depth analyses of the life status of trapped individuals. This novel method is expected to be applied in post-disaster rescues, battlefield searches, and rescue tasks and can provide guidance for the optimization of rescue strategies and rational allocation of medical resources, promote the safety of trapped personnel, and reduce the casualty rate in disaster relief areas.


Chilaiditi's sign refers to colonic interposition between the liver and the diaphragm in the right subphrenic space secondary to the relaxation of the suspensory ligaments of the right colic flexure. The diagnosis of Chilaiditi's sign is based on radiological findings with the following three criteria: 1) The right hemidiaphragm must be adequately elevated above the liver by the intestine, 2) the bowel must be distended by air to illustrate pseudo-pneumoperitoneum, and 3) the superior margin of the liver must be depressed below the level of the left hemidiaphragm. In this report, we present the case of a 49-year-old female presenting with signs and symptoms suggestive of Chilaiditi syndrome managed with laparoscopic surgery. We also present a literature review with a summary of previous studies and propose a novel management staging system for this syndrome. Chilaiditi's sign, also known as the interposition of the colon above the liver, refers to the presence of the colon in the right subphrenic space secondary to the relaxation of the suspensory ligaments of the right colic flexure . This coWhile there are several cases of Chilaiditi syndrome reported in the literature, there is a paucity of data with regard to its management. In this study, we present a case of Chilaiditi syndrome in a 49-year-old female. Furthermore, we propose a novel staging system for patients with Chilaiditi syndrome based on radiological and clinical findings, with the aim of streamlining future medical management.A 49-year-old female with no pertinent past medical history presented to the emergency department with a chief complaint of sharp chest pain for 3-4 hours. The pain was localized to the right upper quadrant (RUQ) and epigastric regions, with radiation to the right shoulder and chest. The patient reported an increased frequency of intermittent pain associated with nausea, which was exacerbated by deep breathing and oral intake. The patient also endorsed occasional dysphagia, globus sensation, palpitations, and long-standing chronic constipation. She had no relief with over-the-counter antacids and laxatives for many years and did report a family history of constipation and colonic perforation. Surgical history was significant for appendectomy and cholecystectomy. Physical examination revealed marked abdominal distention and mild epigastric pain with decreased bowel sounds. A CT scan of the abdomen and pelvis revealed a diffusely hyperlucent diaphragm with a lobular appearance, consistent with bowel gas and Chilaiditi syndrome Figure . Follow-Despite implementing a combination of medical management, lifestyle modifications, the use of multiple laxatives, and changes in diet, her bowel movements continued to be infrequent, occurring every 2-3 weeks. Further evaluation with a colonoscopy, a sitz marker study, and a balloon expulsion test in the absence of anorectal abnormalities excluded other conditions including inflammatory bowel disease, constipation, perforation, and abdominal or pelvic floor dysfunction. After a thorough discussion with the patient regarding the risks and benefits of surgical management, a laparoscopic-assisted subtotal colectomy with ileorectal anastomosis was performed. The patient was later successfully discharged on postoperative day 6.Postoperatively, the patient's recovery was complicated by the development of a pelvic abscess, which was successfully treated with percutaneous drainage. Subsequently, she reported no nausea or vomiting and was tolerating a diverse diet with an average of 3-5 bowel movements per day.Upon initial presentation, our patient displayed signs and symptoms consistent with a potential diagnosis of Chilaiditi syndrome including sharp, localized pain in the chest and RUQ of the abdomen accompanied by nausea. The pain was not worse with breathing but may have been triggered by oral intake. Treatment through medical management including oral antacids was unsuccessful, and she required surgical intervention. While our patient's case was successfully managed with surgery, this may not be the case for all patients with this syndrome. Table We propose a three-stage grading system for Chilaiditi syndrome patients. This system is based on radiological and clinical findings with the goal of simplifying future medical management Table . The proWe also propose the use of our treatment algorithm Figure  to guideChilaiditi syndrome is a complex condition that can manifest in various complications, depending on the underlying causes. This report highlights and describes a standardized staging system to guide the medical management of Chilaiditi syndrome patients. This will help ensure that patients receive the most appropriate treatment for their condition and that treatment is tailored to their level of severity. This will ultimately lead to improved outcomes for patients with Chilaiditi syndrome.


Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function  is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results. Electrical impedance tomography (EIT) is a low-cost and non-invasive technique for visualizing the internal structure of the human body ,2,3. TheImage quality in EIT is limited due to very poor spatial image resolution and low sensitivity in the center of the field of view. Despite this, EIT has shown its potential in medical diagnostics , especiaA barrier to the development of EIT is the problem of measuring very small signals. The very low signal-to-noise ratio makes it impossible to increase the number of electrodes. It leads to poor sampling of the signal at the edge of the field of view, which translates into a small number of measurements and poor spatial resolution of the image. In addition, a large difference in the measurement values for adjacent and opposite electrodes leads to poor numerical conditioning of the linear system matrix.The new approaches for measurement in EIT were presented in the literature, for example, dual frequency EIT, multiple frequency EIT , and a nA big challenge in the EIT is to solve the inverse problem, which consists of determining the cross-sections of the tested object on the basis of measurements made using electrodes placed around this object. Ongoing research is conducted in various directions. In the analytical approach to the inverse problem, the D-Bar method, a generalization of the Calderon method, is of great interest ,18. The In the algebraic approach to tomographic reconstruction in EIT and CCEIT, the inverse problem is non-linear and severely ill-posed because the number of measurements is low in relation to the reconstructed image’s number of pixels. The extremely poor numerical conditioning of the sensitivity matrix makes the inverse problem difficult to regularize ,20. UnfoThe best image reconstruction results are obtained by non-linear algorithms, such as the Levenberg–Marquardt (LM) algorithm, in which the non-linear function is minimized . Non-linWhile the progress of classical reconstruction methods is slow, deep learning methods are developing dynamically. Over the past few years, machine learning  and deepThere are three main machine learning approaches to solving inverse EIT problems: single network direct reconstruction, joint reconstruction, and hybrid reconstruction . The sinTraining ANN to reconstruct images in EIT requires a large dataset containing a correspondence between capacitance measurements and the corresponding electrical parameters’ spatial distribution. Gathering a set of patient medical records of this size is problematic. The numerical modeling of the chest phantom and the generation of measurements by solving a simple problem allow us to obtain a relatively large dataset in a reasonable time. During ANN training, it is important to artificially introduce noise into simulated data as it increases model stability and decreases the possibility of overfitting . OverfitExamples of the ANN’s use for electrical tomography image reconstruction can be found in many scientific papers, for example, in ,36. DiffFrom the point of view of the clinical application of electrical impedance tomography, there is an interest in anomaly detection in the lung region . In the Considering the promising properties of CCEIT, the mentioned potential of neural networks in solving the inverse problem, and the need to identify lung regions with pathological changes, we attempted to use deep neural networks to reconstruct thorax images, based on which it would be possible to identify areas affected by pathological changes. In this paper, we present a new enhanced chest phantom that takes into account the appearance of three pleural diseases  causing pathological accumulation of air or free fluid in the pleural cavity and leading to the collapse of the adjacent lung regions.Discovering the lung lobes with disease presence is described in . HoweverWe propose a modification of the thorax numerical phantom presented in , in whicIn our work, the image quality of a 32-electrode CCEIT was studied, whereas in other papers, a smaller number of electrodes is used. For example, in the study  devoted The use of ANN in electrical tomography image reconstruction is commonly known. However, in this paper, we use ANN-based reconstruction for the first time in the case of a lung model with regions affected by pleural diseases. This work is focused on exploring the required ANN complexity depending on the assumed level of noise. To achieve that goal, we have chosen a very simple shallow and sophisticated deep convolutional neural network, a fully connected network (FCNN), and a conditional generative adversarial network (cGAN), respectively. Pixel-based metrics are used to assess the results in most papers. In this work, we have proposed the use of the diagnostic value measured using the area under the receiver operating characteristic (ROC) curve (AUC) metric to evaluate image reconstruction quality.This work uses supervised learning, which requires a training dataset containing ordered input and output data pairs. In electrical tomography, a pair of data is an image of a tomographic cross-section of the tested object and electrode measurements for this object.In CCEIT, the input data are the measurements of inter-electrode capacitances, and the output data are the images of the permittivity distribution in the examined space. In the case of patients’ thorax exams, it is unrealistic to collect a large amount of training data in the early stages of the study. Due to the large size of the training set, we decided to generate data using numerical simulation.The ECTsim software package, dedicated to the Matlab environment, was used to generate training data . The ECTe method  from theA model of a 2D slice of the human thorax, proposed in , was useWhen generating the training set, possible individual changes were taken into account, i.e., the location and size of the lungs, the location and size of the heart, aorta, and spine. A certain range of change in the center position and the angle of rotation was adopted for all organs. All organs could also change their size, but the change in both axes was independent—all organs were modeled using ellipses. A uniform probability distribution was used for all ranges when selecting parameters from the range. Whether the lungs were healthy or affected by diseases A or B was randomized. The size and location of the illness-affected regions were randomized, with pneumothorax appearing only at the top (front of the supine patient) and effusion at the bottom of the image (back of the supine patient).In ECTsim, the numerical simulation is performed on a dense, non-uniform mesh, but the reconstruction is conducted on a reduced mesh. In this work, the matrix of the reconstructed image was 64 × 64 pixels. Since only pixels in the field of view of the tomographic probe are reconstructed, the number of pixels that are reconstructed is smaller and equal to 1856 for the adopted model. This significantly reduces the size of the output data vector of the neural network.By solving a forward problem in CCEIT, i.e., solving the equation for the electric field, it is possible to calculate capacitance measurements between electrodes based on the known distribution of complex electrical permittivity in the examined volume. The forward problem can be written as:ittivity . By haviurements . If the The decision on the number of samples in the training dataset is one of the most difficult, as there is no accurate method to estimate the necessary dataset size. However, our experiments show that a model with an input size of 992 and an output size of 1856 trained on a dataset containing around 200,000 samples provides good results. The dataset should include randomly generated samples representing healthy lungs and lungs partially affected by the illnesses. Because lungs can be affected by one or both conditions, it is possible to have 16 different cases depending on which lung is affected by which condition. We created a dataset that evenly represented all 16 cases. Additionally, to mitigate overfitting, we introduced samples with random ellipses.The training set contained 9375 samples from each class and 44,000 samples with random ellipses, for a total of 194,000 samples. During training, this set was randomly split on the fly into learning and validation subsets with a ratio of 75:25.The test set contained 2500 samples from each class and 2500 samples with random ellipses, for a total of 42,500 samples.For ANN training, it is necessary to divide the whole dataset into training, validation, and testing subsets. The training subset is used as the source of experience during model training. The validation subset is used to monitor the main metric while training, and the testing subset is used for the final model performance evaluation. Two datasets were generated with the model shown in Each sample of the dataset generated by ECTsim contains a complex capacitance measurements vector of size 992 and a complex electrical permittivity values vector for 1856 pixels from the field of view. Due to the limitations of the PyTorch framework, which is capable only of ANN training on real numbers, only the imaginary components of capacitance measurements and electrical permittivity were taken for ANN training purposes. Capacitance measurements depend on the electrode’s relative position and the electrical properties of the material inside the sensor. To mitigate mutual electrode arrangement influence, one can normalize measurements by calculating capacitance for the empty sensor and the sensor filled with a high-conductivity material. Capacitance measurements can be stored as a 2D matrix where the column and row indices correspond, respectively, to the excitation and sensing electrodes. Example color maps of conductivity distributions and resulting capacitance measurements (imaginary component) are presented in Two different neural network architectures were used in the experiments: a fully connected neural network (FCNN) similar to EIT-4LDNN  and a coAs a fully connected NN (FCNN), we used the network architecture shown in A different model used in this work was a convolutional network based on the cGAN architecture . It consWe divided the whole dataset into training and testing subsets to train the reconstruction model as described above. A binary cross-entropy was used as a loss function while training the discriminator, and the AdAM was used as an optimizer.To correctly assess ANN training quality, it is necessary to divide the dataset into three parts: training, validation, and test subsets. The training subset is used for the actual ANN training. The validation subset allows adopted metrics to be calculated on the fly during training, and the test dataset is necessary for the final evaluation of the training result.A widespread quality evaluation strategy in EIT image reconstruction mainly focuses on adopting simple pixel-to-pixel metrics such as average relative error, L2 norm (RMSE), or 2D correlation coefficient ,70. SuchIn this work, we use the following pixel-based metrics to evaluate reconstruction quality: root mean square error (RMSE), 2D correlation coefficient (CC), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). These metrics are defined as follows:Considering the medical applications of EIT, it is implied to evaluate its model diagnostic value. For this purpose, in this work, we proposed the following procedure: Labeled samples corresponding to different medical conditions are entered into the training dataset. The classifying neural network is trained on the generated image data (true images). The classifying neural network is verified on reconstructed images. Comparison of the classification results allows us to assess the diagnostic value of the reconstructed images.A well-known approach to the comparison of classification models is the use of receiver operating characteristic curves (ROC). The ROC curve demonstrates the diagnostic ability of a binary classifier depending on changes in the discrimination threshold. The value of the AUC (area under the ROC curve) metric shows the model’s performance and ranges from 0 to 1, where 1 means perfect performance, 0.9 means very good performance, and 0.5 means no performance.For multi-class classification, as in our case, the analysis of the AUC metric needs to be adjusted. In our model, we have 16 classes of lung status, starting with a class when both lungs are healthy and ending with a class when both lungs are affected by both diseases. One additional class (17th) corresponds to random ellipses in the thorax phantom. The OvR (one versus the rest) method was used, which consists of calculating the ROC curve for each of the 17 classes, where in each step, a given class is considered a positive class and the remaining classes are given a negative weight. The average ROC curve is used to evaluate the multi-class classifier .To evaluate reconstruction quality, we used the classifier ANN with the architecture shown in The model was trained on generated images from training and validation subsets and corresponding labels saved during dataset generation. Those samples were divided into training and validation parts in a ratio of 75:25. Binary cross-entropy was used as a loss function, and the AdAM optimizer was used as an optimizer.After training the FCNN and cGAN networks, reconstruction quality evaluations have been carried out. We reconstructed images from the testing dataset containing 42,500 samples using trained ANNs and selected algebraic methods . BecauseThe linear back projection (LBP) given byThe pseudoinverse with Tikhonov regularization (TPINV) given by−9 to achieve the best possible results.The regularization parameter The abovementioned image quality measures, such as RMSE, PSNR, SSIM, and 2D correlation coefficient, were computed for each pair of ground truth and reconstructed images. The mean value, standard deviation, and median over the testing dataset were calculated . Image qThe diagnostic value of reconstructed images was evaluated using the classifier network trained on ground truth images. The OvR (one versus the rest) method was used to assess multi-class classification. We calculated mean ROC curves for each reconstruction method without introducing noise into measurements and then calculated corresponding AUC metric values a. Cases In our work, we have shown the reconstruction of human thorax numerical phantom images in 32-electrode CCEIT. The two artificial neural networks were used to solve an inverse problem. As expected, the more complex NN based on the CGAN architecture achieved better results than the simple, fully connected NN (FCNN). The FCNN produces acceptable results but has reduced generalization ability. This is evidenced by the second peak, for a lower SSIM value, visible in the SSIM measure histogram for the test dataset c. This pDiagnostic ability evaluation showed very good classification performance on ANN-reconstructed images. However, unexpectedly, the reconstruction with the use of simple one-step algebraic methods also showed acceptable performance. That leads to the hypothesis that it is potentially possible to detect illnesses based on simple reconstruction. Also, it may be possible to create a classifier network able to detect illness conditions directly from measurements.Algebraic methods require both real and imaginary parts of measurements represented by complex numbers. However, ANN training on complex numbers is only at its beginning. Therefore, we trained networks only on the imaginary part of the measurements and electrical permittivity. It might be interesting to train a network using complex input and output and compare the robustness of such a complex weighted ANN.The use of CCEIT in wearable solutions requires a fast and computationally efficient reconstruction method. Our work shows that such a solution may be obtained with the use of an artificial neural network. We have developed a numerical phantom modeling the transversal slice of the human thorax, allowing simulation of different illness conditions arising in the regions of the image showing a conductivity distribution in the thorax. Such conditions correspond to the cases of lung reduction on the image and the appearance of regions with extremely high or low conductivity on the image. We have shown that numerical simulation of the reconstruction process with an ANN-based approach shows very promising results: while the reconstruction speed is comparable to a one-step algebraic approach, reconstruction quality, assessed with the use of image distance measures, is much higher in the case of the ANN-based approach.In addition to the evaluation of results based on image quality metrics, an approach based on the assessment of the classifier’s ability to recognize a disease state using reconstructed images was proposed. The assessment using the classifier showed that even the images reconstructed by the simplest reconstruction methods still contained information about the location of the zones with changed conductivity.As a result of the analysis of neural network models, it was concluded that a more complex network based on the cGAN architecture gives better reconstruction results, although the simplest possible network gives relatively good results.Due to the limitations of the tools used, training of deep networks was carried out using only an imaginary part of capacitance and electrical permittivity. However, the real processes occurring during electrical capacitance tomography are better described by complex numbers.Due to the simulation nature of the above work, the obtained results cannot be directly used for medical imaging. It is, therefore, necessary to conduct further research, both in simulation and, if possible, on real data. Based on the obtained results, it can be concluded that there are three directions for further research: clarification and extension of the model based on convolutional networks, ANN training using complex numbers, and examination of the classification possibilities of data reconstructed by classical reconstruction algorithms.


Studying the gravity-dependent characteristics of regolith, fine-grained granular media covering extra-terrestrial bodies is essential for the reliable design and analysis of landers and rovers for space exploration. In this study, we propose an experimental approach to examine a granular flow under stable artificial gravity conditions for a long duration generated by a centrifuge at the International Space Station. We also perform a discrete element simulation of the granular flow in both artificial and natural gravity environments. The simulation results verify that the granular flows in artificial and natural gravity are consistent. Further, regression analysis of the experimental results reveals that the mass flow rate of granular flow quantitatively follows a well-known physics-based law with some deviations under low-gravity conditions, implying that the bulk density of the granular media decreases with gravity. This insight also indicates that the bulk density considered in simulation studies of space probes under low-gravity conditions needs to be tuned for their reliable design and analysis. Solar rocky bodies, including the Moon and asteroids, are covered by fragmental debris called regolith. Regolith, which is a potentially fluffy and powdery granular material, is a primary concern for the lander or rover; landing on such loose soil is a critical phase during exploration as the footpad of the landing gear may bury into the regolith. Therefore, the mechanical interaction of the footpad plays a key role in the landing dynamics6. The importance thereof is borne out by the fact that one mission involving Spirit, one of the twin Mars exploration rovers, had to be terminated because the wheel of the rover were trapped by loose regolith7. This issue has clarified the importance of the wheel–soil interaction mechanics9. These mechanical interaction models of machines  on such granular media under various levels of gravitational acceleration are key to their reliable design and analysis. For instance, the pressure–sinkage relationship10, often seen in a wheel–soil interaction model, considers the weight density of granular media for determining sinkage with respect to the pressure applied. The alternative models proposed in the last decade, such as the empirical resistive force theory13, a local friction force model14, a unified force model for the drag15, and a modified Archimedes law16, also implicitly include gravitational acceleration because they assume hydrostatic-like force in accordance with the penetration depth of solid objects into granular media.Technologies for landing and roving on extra-terrestrial bodies are fundamental for pursuing scientific missions and human exploration in space, as reported in the roadmaps published by several space agencies18. In the past, several studies using DEM focused on predicting vehicle mobility in regolith sediments on the Earth, Moon, or Mars23 as they explored the effect of gravity on sinkage and motion resistance of vehicle wheels. The DEM has also been employed to address granular media in low-gravity environments24.The discrete element method (DEM) is a numerical approach that precisely simulates the dynamic response of granular media in mechanical interaction26, any simulation model and its output must be quantitatively evaluated and validated by comparing them with the corresponding experimental results. Therefore, the DEM analysis of granular dynamics in an extra-terrestrial environment also needs benchmark data obtained at the corresponding test environment under varied gravity and high-vacuum conditions to be verified.Such a computer-aided approach often requires a reliable scheme for the accuracy evaluation of the simulated output. According to verification-and-validation (V&V) standards27. The angle of repose is constant regardless of the gravitational acceleration29, while the friction angle slightly increases as the gravity decreases30. The wheel mobility performance under reduced gravity has also been investigated31. The abovementioned studies31 employed parabolic flights or drop tower facilities for simulating reduced gravity environments on Earth. These facilities, however, can simulate only limited campaigns: short duration , fewer test trials, and unstable gravity conditions32.The mechanical characteristics of granular media under different gravity conditions have been experimentally investigated, and it was found that their flow dynamics explicitly depend on the gravitational acceleration33, the most commonly used law for flow rate analysis of granular media, postulates that the steady-state mass flow rate is proportional to the square root of the gravitational acceleration. The law has been modified for wider outlet sizes34 and validated under high-gravity conditions37. However, its applicability under low-gravity conditions is still uncertain because of the lack of experimental data under the low-gravity condition as noted above.While the experimental approach limits the number of trials and gravity conditions, dimensional analysis is a typical approach used for discussing the gravity-dependent characteristics of the granular flow. Beverloo’s law39. We employed a wide range of AG levels, from 0.063 G to 2.0 G, over seven hours using several granular media. A DEM simulation study also supported the consistency of the granular dynamics in the AG and natural gravity fields. We found that the mass flow rate of the granular media measured at the experiments quantitatively follows Beverloo’s law, while some deviations are found under low-gravity conditions. The statistical and dimensional analysis of the deviation implies that the low-gravity decreases the bulk density of the granular media.The abovementioned discussion clarifies the scientific purpose of this study: to propose an experimental approach to examine a granular flow under stable artificial gravity conditions for a long duration, to reveal the gravity-dependent characteristics of granular flow under low-gravity conditions, and to examine the characteristics according to the mechanical phenomena and Beverloo’s law. Further, publicly available data obtained in the low-gravity experiment may be scientifically beneficial for the research community. Therefore, we conducted granular flow experiments under low-gravity conditions by using hourglass-shaped devices in the Japanese Experimental Module (JEM) at the International Space Station (ISS). The JEM is equipped with a centrifuge providing high-quality, long-term, and stable artificial gravity (AG) conditionsOur experiments provide further insights for future experiments on granular media using an artificial gravity generator and/or hopper-shaped experimental apparatus. The result of the mass flow rate is a valuable benchmark for data correlation or assimilation for future experiments. Additionally, the size of the orifice diameter relative to the particle diameter, the amount of granular media packed in the hopper, and camera settings  are important for developing a similar experimental test bench.G). Our results can be used to improve dynamic analyses using numerical simulations for these landers/rovers by tuning the bulk density to lower values. Without the tuning of the bulk density, existing simulation techniques overestimate the traction performance of the rover and underestimate the sinking of the landing footpad, whereas improved simulations based on our findings could result in more accurate values for these dynamic analyses. This outlook provides useful insights into the design and analysis of space probes under low-gravity conditions.There are several lunar/planetary exploration missions planned for the coming decades: CLPS  offered by NASA accelerates the lunar exploration and in situ resource utilisation as part of the Artemis programme where several companies shall deliver landers and rovers to the moon. The Mars sample return mission requires soft and accurate landing and fast roving on Mars. The Martian moons exploration (MMX) by JAXA also requires a high-fidelity landing simulation under low-gravity conditions . Note that the inside of each hourglass is vacuum. The typical properties of the granular media, such as particle size distribution, shear strength, and particle and bulk densities, are available in Supplementary Methods. The experimental campaign continued for seven hours. Each hourglass continued flipping every 60 s to change the flow direction to different hopper angles (60° and 120°). The total number of flipping was more than 400 during the experimental campaign. Supplementary Methods describes the geometric layout of the experimental apparatus, specifications, and the angular velocity of the centrifuge according to varied AG levels. The acceleration profile of each AG is also summarised in Supplementary Methods. The Data Availability section provides the raw movies (MP4 files) of the flows of the granular media.The centrifuge Fig.  in the emin Fig. . We deve40, particularly observed under the condition of relatively low-gravity (see Data Availability section). The intermittency is due to the partially generated arch above the orifice40, which is formed by the solid friction forces between the particles and/or walls but never leads to permanent clogging. The arch collapses as the partial flow continues because the bearing force at the arch generated between the particles becomes smaller than the arch weights , and the stick-slip effect occurs between the particles40.Among the granular media listed in Table 43 that varied with the levels of gravity conditions, and they also expressed bridging and rat hole. In addition, these granular media adhered, deposited, or even aggregated because of their wide particle size distributions and non-spherical particle shapes, generating the interlocking force between the particles.By contrast, the other materials flew with multiple modes such as mass/funnel flowsx. In contrast, the Van der Waals force  is proportional to x.Figure μm becomes larger than the particle weight owing to gravity even under 1.0 G. In particular, the granular media . The granular media, alumina beads, silica sand No. 5, and Toyoura sand are above this threshold and have a relatively low internal friction angle (see soil property in the Data Availability section), showing explicit granular flows under varying gravity levels. Thus, the following analysis focuses on these three granular media to discuss the gravity-dependent characteristics of granular flow.In contrast, the particle weight owing to gravity for particle size larger than approximately 200 44. Among the three granular media, the alumina beads have a relatively high mass flow rate because of their spherical particle shapes with uniform diameters. The flow rate of silica sand No. 5 and Toyoura sand are almost equivalent, although their particle sizes and distributions are different. This is discussed in Statistical analysis section based on Beverloos’s law.Figure G. At the hopper angle of 60°, the granular flow under NG is almost unchanged within the range of temporal resolution of the camera used. The mass flow rate at the hopper angle of 120° under NG is faster than that under AG. This may be due to the uniform gravitational field applied by NG to the granular media even after passing through the orifice. On the other hand, the gravity in the AG is inversely proportional to the rotational radius of the apparatus (see Supplementary Methods). Moreover, the granular media passing through the orifice is subject to the constant velocity motion. The DEM-based analysis on the flow modes examines the difference between the AG and NG conditions, as described in the following section.Figure The DEM analysis was performed to examine the difference between the AG environment at the ISS and the NG environment. In the former, the experiment at the ISS was mimicked, i.e., the hourglass was repeatedly flipped while being revolved  of the hourglass. However, for AG, the sedimentary particles in the upper section of the hourglass are in the rotating system just like the hourglass. Figure Figure G under NG) and from the DEM analysis (1.0 G under NG), as shown in the Supplementary Notes. It was confirmed that the granular flow behaviour under 1.0 G (NG) was well reproduced under the present conditions of DEM analysis.Figure Figure y-z plane of the hourglass under the AG condition. Here, we used the initial velocities of the particles obtained via the DEM analyses under the AG condition  direction in the AG environment deviates from that of the NG environment with the falling time, our experiment is appropriate to investigate the dependence of gravity on the granular flow. In particular, for granular media #4–#8 including fine particles, the effects of adhesive forces on the granular flow under low-gravity conditions are interesting ongoing issues as its competition with gravity affects the flow-mode transitions, such as mass/funnel flows or bridging. The challenge is to develop additional experimental methods that can also study granular flow and sedimentation when adhesive forces are dominant .26 of the DEM model under low-gravity conditions. Then, the data can be applied to simulations of more complicated dynamics of granular media and interaction phenomena with solid objects. Note that, in such future work, it is necessary to introduce a Van der Waals force and/or electrostatic force on the DEM analysis model.In addition, as the particles fallen at the bottom belong to the rotating system again, it could also be possible to examine the gravity dependence on static sedimentary phenomena, including the angle of repose and bulk density. Furthermore, the data obtained by the present experiment for eight types of granular media might be used for parametric fitting and V&V33, which postulates the mass flow rate of granular media , and D denotes the variables determined by the orifice diameter and the particle diameter. ξ is the gravity magnitude generated by the centrifuge; in other words, it is the scaling factor of the gravity acceleration with regard to the standard gravity acceleration.The experimental results are discussed based on Beverloo’s lawCf in Eq.   β. Equatp-values of each coefficient using the F-test of the nonlinear regression analysis . The servo motor flips the hourglass 180° with respect to the artificial gravitational direction every minute so that we can observe granular flow from the different hopper angles in every hourglass flip, where the flip takes apploximately 0.6 s to complete. Note that the rotation range (flipping motion) is limited to ±90° because of the size constraints of the experimental apparatus.The CPU with the onboard camera grabs the flow images at 25 fps and stores them in a compressed MP4 format  file every five minutes. These images are resampled from MP4 files at a constant frame rate and undistorted using a calibration template calculated at the pre-flight check. Each image is then correlated with the gravity condition generated by the AG.vm is calculated from the experiment as:m is the mass of the granular media packed in the hourglass; t0 is the time when the flow starts, which is equivalent to the moment when the hourglass is perpendicular to the AG direction during its flip; and te is the time when particles do not move among the consecutive images. The temporal resolution is 0.04 s, which is determined by the camera frame rate (25 fps).The mass flow rate 18. In this study, the commercial software package Rocky DEM49 was used.The DEM is a method in which the analytical object is modelled as an aggregate of discrete particles, and their motions at each instant is analysed considering contact and sliding between the particles or particle and object50. Rolling resistance is considered when a moment opposing the rolling motion of a particle is introduced into the modelling. This moment is usually incorporated for practical representation of the effect of non-sphericity on rolling spheres or the effect of surface irregularities on other types of particles. The rolling resistance contributes to the dissipation of energy during relative rotation, which is important for dynamic flow conditions. In this study, we focused on the dynamic flow of granular media that are relatively sparse; therefore, we incorporated the effects of sand particle shapes using the rolling resistance model. Notably, the particle shape is important in the DEM analysis for problems with prominent dilatancy51. To thoroughly examine the relationship between dilatancy trends, bulk density, and gravity of granular media except for alumina beads in the future, it is necessary to newly identify the parameters of the DEM model using non-spherical particles.In the analyses, each granular media was represented by an aggregate of spherical particles according to their particle size distributions . Here, the centre and radius of the evolution and the respective angular velocities were set to the same values as those in the experiment (see Supplementary Fig. Further information on research design is available in the Supplementary InformationSupplementary Movies 1Supplementary Movies 2Supplementary Movies 3Reporting Summary


With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%. Automatic fingerprint identification systems (AFIS) have been widely used in personal identification and authentication for their high reliability, strong versatility and low cost. Due to the challenge of fingerprint spoofing attacks, the security of AFISs has received more and more attention. In practice, it is likely to be attacked by artificial fake fingerprints . ArtificHence, fingerprint liveness detection is of great significance for the further security of fingerprint applications. The various anti-spoofing approaches can be broadly classified into hardware-based and software-based methods . HardwarA fingerprint liveness detection network with lightweight and high generalization based on ResNet and Transformer is proposed in this paper. The foreground of the fingerprint image is extracted by image processing methods such as adaptive thresholding, erosion and dilation to eliminate the influence of surrounding areas. Then, the image center point is chosen as the reference point for partitioning the image into local patches for training and testing of the network. This partition method is simple and effective, and it can generate small-area fingerprint images. More importantly, this method avoids directly extracting fingerprint minutiae and achieves an end-to-end effect. The combination of Residual Network and Transformer is used to construct a classification network architecture for fingerprint liveness detection, in which the 3 × 3 convolution kernels in the last block of the original Residual Network are replaced with MHSA blocks. Convolution can effectively learn abstract and low-resolution feature maps in large-scale images, and self-attention can process and summarize the detail information contained in feature maps. To improve the generalization ability of liveness detection on unknown materials, we used the style transfer CycleGan to fuse fingerprint images of different forged materials to simulate the generation of fake fingerprints of unknown materials.End-to-end data structure. Different from most of the existing methods that randomly select fingerprint local patches, the fingerprint image center is used to guide the selection of local image patches to form the dataset, which can be used for direct training of the network without other transformations and avoiding the phenomenon of overfitting. The controllable patches partition does not change the size of the image and is an end-to-end operation that does not depend on minutiae. Moreover, the size of image patches can be adjusted to accommodate small-area fingerprint liveness detection.The improvement of network generalization ability. CycleGan is used to generate fingerprint images of unknown materials, expanding the dataset and improving the generalization ability of the network. The style migration can be carried out on the fingerprint image of known materials to generate a synthetic forged fingerprint image corresponding to unknown forged materials. From the LivDet2015 dataset, we can get two synthetic forged fingerprint images of ecoflex texture + gelatin style and gelatin texture + ecoflex style at the same time by training the pseudo fingerprint images of ecoflex and gelatin materials through CycleGan.Lightweight network structure. A new lightweight network architecture is proposed based on ResNet and Transformer. The network introduces the MHSA module in BotNet (a backbone network based on Transformer) and improves the residual structure in ResNet. The self-attention mechanism is introduced to integrate the global information on the high-level feature map, so that the network can pay more attention to the overall ridge structure characteristics of the fingerprint image. The proposed network structure greatly reduces the network depth and the number of convolution cores used in each layer, thereby effectively reducing the network parameters.This paper mainly aims to improve the generalization ability of the model for false fingerprints generated with different materials, and to reduce the computational burden with the goal of ensuring better fingerprint detection performance. The main contributions of this paper are as follows:Feature extraction is an important research topic in software-based liveness detection. The texture features of live fingerprints and fake ones differ in continuity, sharpness and ductility, so texture-based feature extraction methods have become the most commonly used methods among static feature-based methods.Nikam  first prThese methods can be targeted at specific applications, and they have significant advantages such as rotation invariance and gray invariance. They can achieve high classification accuracy on some standard databases, but the overall generalization ability and robustness are unsatisfying, and it may be difficult to produce certain effects on images produced in some complex environments.In addition to the handcrafted feature extraction mentioned above, more and more studies have used deep learning, such as MobileNet-v1, VGG-19, ResNet and GoogLeNet, to design highly robust and interpretable fingerprint liveness detection algorithms. Most of the existing methods based on CNN transfer the model pre-trained on natural images instead of redesigning a new network structure. However, there is a huge difference in complexity between fingerprint images and natural images, the network models are mostly prone to overfitting and they fail to achieve the expected effect on fingerprint liveness detection. Nogueira et al.  introducAt present, a large number of researchers have begun to use deep learning methods for fingerprint liveness detection. Such a method can save researchers a significant amount of manual design on the algorithm, and the model can learn autonomously from the data. Eliminating many preprocessing and intermediate steps, the learned model has a certain generalization ability, and many studies have shown that deep learning methods have certain advantages in performance on fingerprint live detection tasks, with indicators leading most traditional algorithms, lower average detection time consumption and better overall performance.The pre-processing of fingerprint images includes two steps: foreground segmentation and local patches extraction. In the whole fingerprint image, the surrounding area of the fingerprint does not contain any useful information and is removed by foreground segmentation, and then the center of the fingerprint image is used to locate local patches from the foreground area, which reduces the network execution time and model parameters. The fingerprint image foreground segmentation mainly includes the following steps:According to the gray distribution of pixels in different regions of the image, local thresholds are adaptively calculated by a local mean or Gaussian weighted average for foreground or background segmentation of each pixel. In this paper, the segmentation threshold of each pixel is determined by the 3 × 3 neighborhood mean. Given For the fingerprint image segmented by adaptive threshold, we expect the fingerprint area to be as separate from the background as possible, so we use the dilation method to expand the highlighted white part of the image. Due to the noise interference of the original image, there may be small white patches around the fingerprint in the image after the above two processes, so the largest white block is selected as a more accurate fingerprint area. After foreground extraction, to unify the size of the fingerprint image, we partition a local patch of w × w (w = 112) from the foreground region of the fingerprint in the center of the fingerprint image. We also select four points at the upper left, lower left, upper right and lower right in steps of 56 pixels of the center point. Then, we cut a w × w patch centered on each of these four points. This blocking method takes full advantage of the entire fingerprint image information. However, some of these selected patches might include little fingerprint information, which is not conducive to training the network. Hence, they must be excluded from the training set. Color reversal and normalization are performed on each w × w local patch, and then the maximum closure of the binary patch is obtained. When the maximum closure area of local patch is more than 60% of the local patch area, it will be selected as one sample, otherwise it will be excluded. Furthermore, the extracted local patches will be rotated at four different angles, 0°, 90°, 180° and 270°, to deal with the problem of insufficient fingerprint samples, as shown in Most of the existing fingerprint liveness detection networks use the residual network as the skeleton and make modifications based on this. However, it is easy to cause network overfitting in small databases, especially on grayscale images with relatively simple structural features such as fingerprints, which is not directly suitable for fingerprint liveness detection tasks. We propose a lightweight fingerprint liveness detection network based on ResNet and Transformer. In terms of residual blocks, we borrowed some ideas from the Slim-ResCNN network  and a drThe multi-head self-attention layer is shown in Following the principles of neural network structure design, a binary classification network structure was constructed for spoof presentation attacks detection. The proposed network consists of seven parts: Conv1, Conv2, Conv3, Conv4, Conv5, global average pooling layer and final classification layer. The overall framework of the proposed fingerprint liveness detection network is shown in The structure of the network is shown in By referring to the relevant literature, and based on our own experiments, we obtained the optimized relevant parameters for the size of the image being 112 × 112. We trained our models using stochastic gradient descent with batch size 32 for 200,000 iterations. The initial learning rate was set to 0.01, initial momentum was 0.9 and it was reduced by 20% per 50,000 iterations. A dropout layer was added after each convolutional layer (except the first one) and the dropout rates were set to 0.2.One of the major limitations of current spoof detection methods is their unsatisfying generalization performance across “unknown” or novel spoof materials that were not used during training of the spoof detector. It has been shown that the selection of spoof materials used in training (known spoofs) directly impacts the performance against unknown spoofs. In particular, Chugh and Jain ,19 analyWe use a CycleGAN-based  style trThe loss function of CycleGAN mainly includes three parts: adversarial loss function, cycle-consistent loss function and identity loss function. The fingerprint image has many details in structure, such as minutiae, ridges and pores, etc. The role of the adversarial loss function is mainly to make the generated image more realistic. The cycle-consistent loss function ensures that the generated image retains the content part of X and only changes the style part of it. Identity loss function maintains the original fingerprint features during the migration process, preventing the generator from adjusting the features autonomously and changing the overall image. The loss function is shown in Equations (1)–(5):Equations (1) and (2) refer to the cross-entropy loss function, and they are dual relations. Taking Equation (1) as an example, like the loss function of the conventional GAN network, it is a process of mutual game between During the training process, our sample The cyclic consistency loss adopts L1 loss, in order to constrain The total loss function of the network is shown in Equation (5):The weight ratio of adversarial loss function, cycle-consistent loss function and authentication loss function is set to 1:10:10. During training, the batch size is set to 1, the number of training rounds is 100, the Adam optimizer is used, the initial learning rate is set to 0.0002, the learning rate is fixed for the first 50 rounds, and the learning rate is decayed to 0 in equal parts for the last 50 rounds. The result of CycleGan style transfer is shown in The leftmost column of The following datasets were used in this research:LivDet Dataset: To evaluate the performance of the proposed method, we used the LivDet 2011 , LivDet In all the experiments for this paper, we followed the metrics used in LivDet:Classification accuracy (Accuracy) was defined as the ratio of the number of samples correctly classified by the classifier to the total number of samples for a given test dataset. te (ACE) ,16.The output of the Softmax layer of the trained model was in the range [0–1], and we averaged all patch outputs of the fingerprint to get the score. The threshold for determining the liveness of fingerprints was set to 0.5. The fingerprint image with a liveness score over 0.5 was considered as an “alive” entity, otherwise it was considered as a “spoof” artifact. The proposed method was tested in the following two scenarios to evaluate the effectiveness of the algorithm.Known Sensors and Known Materials Scenario: In this case, all images were captured using the same sensor for training and testing, and all the materials used to make the deceptions in the test set were known. The ACE of the proposed network was compared with that of several existing works. The results are shown in Most of the current SOTAs in LivDet2011 and LivDet2013 used MobileNet-V1 for training and testing on local patches based on fingerprint minutiae. The method proposed in this paper showed a slight improvement for the data of the Digital reader and the Italdata reader in the LivDet 2011 dataset and for the data of the Italdata reader in the LivDet 2013 dataset, and the overall average accuracy rate was not far from the best result. The method of  needs toKnown Sensor and Unknown Material Scenario: In this case, the fingerprint images in the training and test sets were captured by the same sensor. But new materials that were not known during training were used in the test set. The detailed performance comparison between the proposed algorithm and other algorithms is shown in The network model obtained by the fingerprint image training enhanced by style transfer had a significant improvement in the recognition accuracy for forged fingerprints of unknown materials. The hampions . CompareIn addition, in order to verify that the method proposed in this paper can solve the problem of liveness detection for small-area fingerprints, we also tested the accuracy of small-area fingerprint recognition on the LivDet2015 database, and the results are shown in Because there were no related references for small-area fingerprint liveness detection, this paper did not make a comparison with other works. In the test results for small-area fingerprints, the results of the data for the Digital Persona reader were poor, and the overall ACE was 7.27. What was more abnormal was the data tested on the CrossMatch reader. After the fusion of five local patches, a high recognition accuracy could be obtained, but the results of the test on a single small-area fingerprint were not particularly good. The results for the data in the GreenBit reader were the best, with an overall ACE of 3.18 and an overall average ACE of 4.73.Since the running speed of the network model will be affected by many factors such as the experimental environment and the amount of input data each time, we demonstrated the superiority of the network by comparing the sizes of network parameters. We compared the parameters of the network proposed in this paper with those of the Slim-ResCNN used by the champion of the 2017 fingerprint liveness detection competition and those of the commonly used lightweight networks MobileNet-V1 and MobileNet-V2. The network used in  was MobiWe drew the ROC curves for the small-area fingerprint recognition results of LivDet2011, LivDet2015 and LivDet2015, and these are shown in This work studied the problem of fingerprint liveness detection and proposed a simple and effective method for fingerprint foreground and local patch extraction. Furthermore, we designed a lightweight fingerprint liveness detection network based on a multi-head self-attention mechanism, and we enhanced the generalization ability of the model through style transfer. By modifying the convolutional neural network and adding the MHSA mechanism to improve recognition accuracy, we significantly reduced the network parameters. The CycleGAN network was used for the style transfer of fingerprint images, enhancing the generalization ability of the fingerprint live detection system. The proposed method achieved excellent results on the LivDet2011, LivDet2013, and LivDet2015 datasets; on the LivDet 2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, outperforming most of the state-of-the-art methods, while significantly reducing network parameters, with the overall parameter number only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%. In the future, we will explore more effective FLD technology, further improve the accuracy of fingerprint liveness detection on the LivDet datasets and explore other GAN-based models to improve the cross-sensor generalization ability between different sensors to deal with fingerprint deception attacks.


Designing thermal conductivity efficiently is one of the most important study fields for taking the advantages of woven composites. This paper presents an inverse method for the thermal conductivity design of woven composite materials. Based on the multi-scale structure characteristics of woven composites, a multi-scale model of inversing heat conduction coefficient of fibers is established, including a macroscale composite model, mesoscale fiber yarn model, microscale fiber and matrix model. In order to improve computational efficiency, the particle swarm optimization (PSO) algorithm and locally exact homogenization theory (LEHT) are utilized. LEHT is an efficient analytical method for heat conduction analysis. It does not require meshing and preprocessing but obtains analytical expressions of internal temperature and heat flow of materials by solving heat differential equations and combined with Fourier’s formula, relevant thermal conductivity parameters can be obtained. The proposed method is based on the idea of optimum design ideology of material parameters from top to bottom. The optimized parameters of components need to be designed hierarchically, including: (1) combing theoretical model with the particle swarm optimization algorithm at the macroscale to inverse parameters of yarn; (2) combining LEHT with the particle swarm optimization algorithm at the mesoscale to inverse original fiber parameters. To identify the validation of the proposed method, the present results are compared with given definite value, which can be seen that they have a good agreement with errors less than 1%. The proposed optimization method could effectively design thermal conductivity parameters and volume fraction for all components of woven composites. Woven composites have excellent structural and thermal properties due to their complex structural characteristics, which have improved interlaminar properties by interweaving fiber bundles. Moreover, functionally graded materials (FGMs) are the new generation of composite materials which overcome the issue of thermal resistance and also delamination in multilayer structures. Delouei et al. ,2 proposBecause woven composites are composed of interwoven braided structure and matrix, and braided structure is composed of yarn and matrix, the research on the thermal conductivity of woven composites needs to be carried out at different scales. Fourier’s law is an effective method to study thermal conductivity of homogeneous materials. However, woven composite material is anisotropic material. Therefore, homogenizing is necessary at different scales for thermal conductivity of woven composites. In order to study the heat transfer coefficient using Fourier’s law, homogenization is required at each scale. Some scholars have established multi-scale research models for composites to study the heat conductivity from bottom to top ,5,6,7,8.3 and a porosity of 0.94 by numerical simulation according to the Fourier law and deduced the thermal conductivity of nanoparticles by using particle swarm optimization (PSO) method. Da et al. [Although the structural characteristics of woven composites can bring excellent material properties, the complexity of the structure also brings some challenges to the design of the materials. Some experimental results have shown research on the thermal conduction performance is necessary for fully exploiting the advantages of composites ,15. Kim a et al.  obtaineda et al.  studied a et al.  acquireda et al.  studied a et al.  proposeda et al.  researcha et al.  simplifia et al.  researchMost existing research focused on the bottom-up prediction of effective responses of woven composites from known constituents’ properties. However, it is demonstrated that in the experimental measurement that properties of fiber or matrix could be changed due to several factors such as manufacturing defects, residual stresses, etc. In addition, a few microscale parameters, such as fiber’s transverse properties, cannot be easily determined through direct measurement. In addition, accurate parameters cannot be designed according to the thermal conductivity of the target by a bottom-up method, and the component parameters need to be adjusted continuously to achieve the target performance. Therefore, although several papers have already conducted research on the design of woven composites, they require a complex process of parameter adjustment. The wide application of woven composite materials determines that the design and research of them need to be more in-depth. This paper presents a top-down approach to study the thermal conductivity of woven composites. Considering that the fibers and matrix have great influence on the macroscopic properties of woven composites, the meso-scale fiber bundle parameters and micro-scale fiber parameters directly affect the macroscopic thermal conductivity of woven composites. According to the study conducted by Zhao et al. ,13, it iIn this paper, for the optimization of plain woven composite, a PSO-based inverse method is presented by combing PSO with the multi-scale method & homogenization method. The remainder of the papers is organized as follows: Woven composites are reinforced materials formed by interweaving warp and weft yarn, which have obvious periodical characteristics . From th1). There are some known theoretical models for researching the thermal conduction coefficient of woven composites, which have been given in the 2), original fiber parameters can be deduced.Because of the complex structure of woven composites, the effective thermal conduction coefficients of macroscopic woven composites depend on the thermal conduction coefficients of each scale. There are two research methods: the bottom-up method and top to bottom method. Bottom-up design requires a complicated trial and error process. However, the inverse calculation from top to bottom saves a lot of cost and improves the efficiency of calculation. Several optimization techniques can achieve this goal . HoweverThe inverse research includes two inverse processes. The first one is to calculate the effective thermal conduction coefficients of plain-woven composites and backout the thermal conduction coefficients of yarn based on the marriage between theorical model and PSO. The second inverse is to deduce the original fiber thermal conduction coefficients based on the inverse values from the first inverse step.ith particle is Xid =  id, where the parameters are the variables to be optimized. PSO has been used in many engineering fields because of its simple operation and fast convergence.We employed the particle swarm optimization (PSO), first proposed by Kennedy and Eberhart ,31, whicThe PSO algorithm is inspired by the social behavior pattern of birds. This algorithm is based on the idea that the intelligence of each individual in a population will affect the behavior of the whole flock, which can be visually described as: there is only one piece of food in the specific area , the goal of the flock is to find out food source. In the whole searching process, each individual passes their own information to each other to let other particles know their position. They can judge whether the optimal solution is found or not by obtained information from others. At the same time, the information of the optimal solution is transmitted to the whole flock. Finally, all the particles can gather around the food source. It turns the problem of finding the optimal solution into the problem of finding a food source iteratively by a group of particles carrying speed and direction in space. At each iteration, every particle will reassess its position and move towards a global goal. This process continues until the maximum iteration is reached. PSO has been widely applied in the engineering field due to its simple implementation and fast searching speed . The baskth iteration, respectively. kth particle, In the standard PSO algorithm, the direction and velocity of particles are updated and iterated by the following equations. In PSO, Equation (1) is used to generate a new velocity according to its present velocity. The new position is associated with the distance between its current position and its own historical best position and the best position of the whole flock. The particle is then moved to a new position according to Equation (2). The maximum number of iterations can be designed in advance as termination criterion: ability , definedFor all particles carrying different information, “min” means minimizing the target function. The number of iterations needs to be met by constantly updating the particle X carrying optimization parameters until the termination condition is met or the maximum number of iterations is reached. In practical applications, many optimization problems will be encountered, and these optimization problems are generally multi-objective. Taking the optimization problem of thermal conductivity of composite materials as an example, since the research object is anisotropic composite materials, it is necessary to balance the transverse thermal conductivity and axial thermal conductivity to make the best choice. This chapter takes the minimum value problem as an example to carry out the multi-objective optimization.In order to validate the effectiveness of proposed inverse method, this chapter firstly compares the inverse values with the original values in existing literatures , includiBased on the structural hierarchical characteristics of woven composites, the inverse calculation of its thermal conductivity is also required, which is, respectively, the inverse calculation from macroscopic thermal conductivity to yarn scale thermal conductivity and the inverse calculation from yarn scale thermal conductivity to microscopic thermal conductivity.Based on the macroscopic parameters of Carbonized Silica/Phenolic woven composites studied in former literature, this section used particle swarm optimization algorithm to reverse the thermal conduction coefficient of transverse isotropic fibers. The agreement between the theoretical formula and the existing model has been verified in a former study. In order to find the appropriate parameters of original fiber and matrix, the theoretical model is used to reverse the yarn parameters, and then the inverse values are substituted into the LEHT model for the inverse of the fiber and matrix microscopic thermal conductivity. For the inverse of the original thermal conduction coefficients of the fiber, the difference between the existing calculation of the thermal conduction coefficients and the one calculated by the optimized value is minimized as the objective function. The number of iterations in the calculation of this section is 20. Here, the reverse derivation of the fiber and matrix thermal conduction coefficients are performed using the fiber volume fraction in the yarn as 0.6. The selected design variables are: eference :(6)k=12The inverse method can greatly reduce the design cost of woven composites, and the performance advantage of the constituent materials can be fully exploited by top to bottom method.(2)The PSO-based inverse method can calculate the homogenized thermal properties of woven composites at different scales and the thermal conduction coefficients at different scales can be backed out sequentially. In order to verify the feasibility of the proposed method, the thermal conduction coefficients of Carbonized Silica/Phenolic woven composite fiber were inversed, and fiber’s thermal conduction coefficients were compared with that of the original fiber. The comparison results showed that the inverse values were in good agreement with the testing value.(3)LEHT has been formed as a packaged program. When combining LEHT with PSO algorithm, researchers just require minimal effort in constructing input data file to execute the computer program, which can be efficiently used by researchers with little thermotic exposure.In order to design the target thermal conductivity of plain-woven composites, the present work proposed to employ an analytical model and LEHT combined with particle swarm optimization algorithm at a mesoscale and microscale based on the hierarchical and periodic characteristic of woven composites. It should be noticed that LEHT is efficient and accurate in calculating the thermal conductivity of yarn and easy to combine with particle swarm optimization algorithm. Additionally, the quantitative calculation of thermal conductivity is carried out by combining multiscale and homogenization methods. The main conclusions are as follows: