We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were  and , respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs.Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R Globally, the leading causes of mortality and disability are ischemic heart disease and stroke . The preThe current era of digital health may provide a means to lessen the burden of the reduced physician-to-patient ratio. eHealth  and mHealth  have been extensive research topics over the past two decades. In 2016 alone, global smartphone sales reached close to 1.5 billion, one for every fifth person on earth . There aThere is a critical need for devices including software decision support tools that are non-inferior to traditional medical devices used in hospital settings. Advances in this area will play a key role in boosting healthcare providers’ capacity to meet the projected CVD management needs.The standard clinical version of the ECG is the 12-lead ECG consisting of Lead I, II, and III, which are bipolar, aVR, aVL, and aVF, which are augmented unipolar, and V1 through V6, which are unipolar. This system requires the placement of 10 electrodes on the patient’s skin. Vectorcardiography (VCG)  is an esLong-term Monitoring (LTM) utilizes a class of devices that consist of single to multi-lead adhesive patch integrated devices, Holter monitors, event recorders, and implantable loop recorders. They are used for ECG monitoring while the patient is ambulatory and have a reduced set of leads ranging from a single-lead patch to a seven-lead recorder. Many current devices have wireless connectivity and upload event-related data in real-time. LTM is primarily used for the detection of transient rhythm abnormalities such as atrial fibrillation, premature ventricular contractions (PVCs), pauses and tachy-and bradycardia, and they have been proven to be more effective compared to Short-term Monitoring (STM) for this purpose ,11. STM While STM predominantly uses traditional bedside monitors, LTM has seen several advances in recent decades. The devices that have proven to be most effective have an internet-connected architecture . Among tFrom a clinical perspective, the trends toward remote monitoring and diagnostics powered by digital health have created a greater demand for tools with high diagnostic value for home use. These tools must be comparable to the tools used in hospitals. In many cases, data from devices such as wearable ECGs with higher specificity and sensitivity can help save time and costs of hospital visits and minimize the number of tests required to arrive at a diagnosis. This is evidenced by the number of commercial devices used as the standard of care as described earlier. The standard 12-lead (S12) is still the gold standard for diagnosis, so a blend of the diagnostic specificity of the multi-lead STM and the sensitivity of long recording durations of the LTM is desired. Notably, the putatively best-performing Artificial Intelligence-based methods for diagnostics based on ECG require S12 as the input to achieve high performance ,19,20,21More power is needed, i.e., a larger sized and higher capacity battery to:○Acquire, condition, and store the data on the wearable device. More channels of Analog to Digital Conversion would be needed and the amount of energy needed to write additional data to onboard memory on the wearable device will also increase .○Transfer the data to a smart device or data gateway device.More storage is needed to archive the data that is uploaded to the cloud. Cloud storage increases in cost if retained for long periods.More electrodes need to be placed on the skin, making the device cumbersome.From an engineering perspective, the following constraints exist for wearable ECG device designs: First, standard lead systems have electrodes placed far apart. The greater the separation between electrodes, the more noise is likely introduced due to motion. All electrodes or sensors need to be electrically connected to electronics, so the wearable device must cover most of the body when sensors are far apart. Second, for remote monitoring, the quality of data connection or connection to the internet must be adequate to support data transfer at higher volumes, or more complex compression algorithms are needed, increasing the computing requirements on the device and the power requirements. Third, as the number of leads recorded increases:These constraints dictate that wearable ECG monitors should be designed with a minimal number of electrodes and a minimal number of leads. A multi-lead ECG, ideally all 15 leads in a wearable form factor, is highly desirable in the new digital health era. The number of electrodes that need to be placed on the skin to acquire these ECGs is depicted in The existing literature does not describe or extensively characterize a methodology to transform a reduced set of ECG leads into a complete set of leads, including Frank XYZ vectorcardiography using an LSTM neural network. A novel deep neural network approach and a detailed validation strategy for the appropriate choice of hyperparameters using Bayesian global optimization are presented.We propose a transfer learning approach to create personalized models for each patient so that the ECG transformations can account for each individual’s unique anatomy. The personalized models were the most accurate based on quantitative and qualitative assessments.A method to compute multiple leads from a reduced lead system is desired to obtain the combined benefit of LTM and STM. This task, in essence, is a function approximation task. The function transforms a reduced set of leads into a larger set of leads. Therefore, a method is presented to reduce the number of electrodes needed so that a wearable device that captures ECG can be designed to capture all information needed for an accurate diagnosis without compromising the quality of life for patients and diagnostic utility. We present this method as a complementary technique to wearable ECG monitoring technology that our research group has previously demonstrated using cloth-based Nanosensor technology . Even thSeveral of the initial research efforts on the transformation of ECGs focused on transformations from S12 to Frank XYZ so that clinicians can tap into the added specificity and sensitivity of VCGs while following the standard of care, which only requires the measurement of the standard 12-lead ECG. From 1986 to 2009, researchers used linear regression to approximate the transformation function. In 2010, the first neural network-based transformation was proposed . Since t2. There is a fundamental limitation to the proposed techniques from 1986 to 2009, which assumed linearity so that the cardiac vector could be projected to the skin to obtain ECG waveforms. The projection of the cardiac vector assumes that the transformation of the electrical activity of the cardiac vector to the surface of the body is a strictly linear operation, which is not true as the human body has various organs and tissue between the heart and the skin with different electrical properties that will effectively result in an arbitrarily complex transformation.Root mean square (RMS) and pearson correlation coefficient are the most reported metrics. R squared, defined as in (3), is used in the literature. Therefore, the following metrics form the most detailed evaluation: RMS error, pearson correlation coefficient, and RTherefore, the goal is to arrive at an arbitrarily complex function that transforms a subset of ECG leads into a larger set of leads. Neural networks are ideally suited for such arbitrary function approximation tasks.We implemented all data analysis programs and applications on MATLAB 2021a Update 5 version 9.10.0.1739362 . The hardware consisted of an Intel processor (i7-7820X), 32 GB of RAM, and an NVIDIA RTX 3090 Graphics Processing Unit (GPU). Since the data used in this study were publicly available, the study was exempt from IRB approval by the Office for Research Protections at the Pennsylvania State University. patient095—record number 291—No V1 lead recordingpatient285—record number 537—Completely corrupted with no visible ECG datapatient220—record number 453—No lead III dataThe PTB database  includesAll 549 recordings were bandpass filtered with a passband of 0.05 Hz to 45 Hz. This passband is acceptable according to long-term monitoring standards. Furthermore, we down sampled the data from 1000 Hz to 200 Hz. Firstly, for adults, most of the ECG signal content is below 100 Hz . SecondlSome patients have several recordings at different times, whereas others only have one recording. A sliding window data augmentation strategy was followed for each recording to increase the number of training samples available per patient. The window size was set to 17 s, and the overlap was 16 s. This sliding window data augmentation approach was followed in similar related work .2. The definitions of the metrics are as follows: All evaluations and measurements of performance are only calculated on the validation dataset for all methods. This is carried out to avoid bias due to expected higher performance, i.e., lower RMSE, of the neural network methods on the training data. We split the complete data set with uniform randomization into 80%/20% (training/testing). We computed the performance of the transformation only on the testing data set for all transformation methods for an unbiased comparison of performance. As mentioned earlier in the related work section, we computed the following metrics: RMS error, pearson correlation coefficient, and RWe used a neural network that we believe is well-suited for time-series data, including the ECG, specifically the Long-Short-term Memory (LSTM) network. The LSTM architecture was proposed in 1997 by Hochreiter and Schmidhuber . Greff elization , where tThe recurrent weights were initialized as ribution . The forThe following is a list of hyperparameters whose values need to be defined to finalize a network architecture before training.Number of layers.Number of hidden units per layer.Learning rate.Minibatch Size (number of training samples per iteration)Learning rate schedule whether no changes or change rules for the learning rate as training progresses. The learning rate can be reduced as training progresses to allow more refined tuning of the network weights closer as the cost function reaches the global minimum.Adam optimizer parameters:2, and pearson correlation coefficient). A grid search is a deterministic method of obtaining the global minimum that a particular set of hyperparameter choices can define. All possible permutations of hyperparameters are used to train several neural networks, and the network that yields the lowest error can then be chosen. Consequently, this network would have the ideal choices for hyperparameters. However, this is a brute force method that is impractical when evaluating computationally expensive functions such as the training of a multilayer neural network. Alternatively, one might randomly choose and evaluate sets of hyperparameters, but this method may not be reproducible and could lead to optimal results only by chance. A superior approach is to use a guided search method in the space of hyperparameters. Bayesian optimization (BOpt) is an approach that is best suited for computationally expensive functions [These hyperparameter values influence the performance obtained from the networks in terms of error . For the same 20 patients, data derived using their personalized and generalized models was also charted similarly. These charts were assigned I.D.s referred to as Chart ID, random numbers from 1 through 90. The association between the source of the data, whether actual or derived, and the I.D. numbers assigned were maintained. Cardiologists were presented with 12-lead ECG waveforms from the actual data, as well as the data derived from a subset of leads using the L2V2V6 → S15 models while they were blinded to the source of the waveforms. They were requested to provide interpretation for each of the presented charts. The interpretations were then compared between the actual and derived ECG waveforms to determine the level of agreement. Due to potential intra-observer variations, all sets of charts with any mismatches in the interpretation were simultaneously charted to analyze whether the differences are evident from the waveforms. The qualitative blinded assessment results are presented as quantitative measures of mismatches by a direct comparison of the number of mismatches that were counted for each of the four interpretation types. Namely, rhythm, conduction blocks, Anatomical findings , ST-T abnormalities, and benign findings. The total number of differences in the interpretation, including missing or additional interpretations, are counted as errors. The total number of errors is then compared between the Personalized Model (PM)-ECG and Generalized model (G.M.)-ECG. The charts where discrepancies were found and resolved are included in the We trained 250 neural networks as part of the BOpt experiments: 50 networks as part of each evaluation for 1-, 2-, 3-, 4-, and 5-Layer networks. The final test set RMSE values for the networks are shown in We observed that the 4-layer network had the lowest test set RMSE value (0.3385 mV). The difference between the best and worst RMSE is ~40 µV. The hyperparameters associated with the optimal model are in 2, and Pearson Correlation Coefficient values for all the derived leads of ECG between the generalized model, the personalized model, and linear regression applied to the personalized models. The boxplots present the median values, lower and upper quartiles, and the minimum and maximum values. The 2, and ρ for the general, personalized linear regression and personalized models.The accuracy of lead transformations to Frank XYZ could be compared to several results reported in the literature . HoweverAs described in the methods section, since the ECG interpretations were not categorical and entered in a free-form text by the cardiologists, the interpretations were grouped by diagnostic criterion. 2, and pearson correlation coefficient. The methodology of applying transfer learning to obtain personalized models from the generalized models shows promising results in terms of quantitative accuracy of derivations. The results obtained through this study show that the personalized models result in a more accurate derivation of the 12-lead ECG waveforms for all patients in terms of three quantifiable measures of error, namely, RMSE, RFor the problem of designing an optimal algorithm for the derivation of all leads from a subset of leads, two broadly defined approaches could be identified. Namely, a hybrid approach, and an end-to-end approach. In the hybrid approach, we would use a priori knowledge regarding linear associations between the leads, which are established through Einthoven’s equations and use a function approximation method to derive the remaining leads. In the end-to-end approach, a single function approximation method may be used to find the relationship between a chosen set of leads and all other leads. The end-to-end approach, which is followed in this paper, selects inputs that resulted in the high-performance transformations in the literature in terms of low errors, Lead II, V2, and V6 . Under tFollowing the hybrid approach could lead to lowered power consumption overall due to lower complexity of the function approximation method, while potentially retaining or lowering the power consumption levels on the electronics that are used to acquire the signal. The end-to-end approach, on the other hand, leads to more complex computation, which could lead to greater power consumption overall. An objective comparison of these two methods is not attempted as part of this work. Such a comparison would have to account for several factors such as hardware design, choice of signal compression methods or their effectiveness, dependent on the choice of leads, implementation of the software libraries that implement the algorithms for transformations and the underlying computational hardware that supports the application, to name a few.The most harm that could result from errors is the misinterpretation of the ECG waveforms, leading to a misdiagnosis or loss of time due to suspicion of pathologies that are not present. These can lead to delays in the appropriate treatment and deterioration of the quality of life for the patient. Therefore, a pilot assessment of the reproducibility of ECG interpretations was conducted in this study. We found that the level of agreement of the interpretations from ECG derived using personalized models and the actual ECGs was significantly higher than with generalized models. There were findings with the GM-ECG that were misleading compared to the actual ECGs and could have led to the loss of time and were potentially harmful to the patient in an actual clinical setting. Based on the findings in this study, personalized models should be the preferred approach. A more extensive study comparing ECG interpretations from a larger sample of patients with different pathologies and more board-certified cardiologists is warranted to validate these results further. The difference in diagnostic yield in terms of detail and specificity of diagnoses from a 12-lead ECG between a GM-ECG and PM-ECG was exposed in this study. The use of more complex deep learning models without accounting for a physiological difference from patient to patient could potentially propagate errors, leading to misdiagnosis. On balance, there are some known limitations to the application of blinded assessment as a qualitative comparison method for the level of agreement between ECG waveforms. Namely, intra-observer variability of ECG interpretations is expected because the cardiologists are, in this case, interpreting the ECG alone without patient history information. Knowledge of patient history has been shown to modulate cardiologists’ attention to specific patterns in the ECG . In clinFor all machine learning and deep learning approaches, the data set utilized to develop the model must be truly representative of the intended patient population. Therefore, the larger and more representative the data set used for the generalized model, the more likely it will generalize well to all patients and be more accurate. Moreover, in this study we have explored LSTM models exclusively due to the best performance reported thus far for the ECG lead transformation problem. It is possible to use more innovative deep learning architectures to discover a generalized model architecture that could provide diagnostically equivalent ECGs compared to actual ECGs, but this remains an active area of research that must validate clinical diagnostic equivalence through qualitative assessments and not just quantitative measures of error. However, the observations from this study suggest that the desired approach should be personalized models and not generalized models. ECGs identical to the actual ECG waveforms could be derived using the data set utilized in this study and the trained personalized models. Whether a larger data set would improve the generalized model accuracy, which would translate to a more accurate personalized model, requires further empirical evaluation. However, clinical equivalence was achievable with this data set as seen by the level of agreement of the ECG interpretations between actual and PM-ECGs.The validity of the transformations for the same patient before and after a major cardiovascular event needs to be evaluated in a longitudinal study. Such a study would help evaluate the hypothesis that the neural network has learned the nonlinear transfer function reflective of the subject’s anatomy rather than overfitting to the data obtained at that moment in time. The nature of data available for this research does not allow that evaluation.Finally, the proposed methodology of personalized transformations has practical limitations that must be addressed from a cost and labor perspective for healthcare providers. A personalized model for each patient will require a controlled clinical measurement of 15-lead ECG, placing all 16 electrodes to obtain the data needed to train a personalized model for each patient. Since there is added cost beyond a routine clinical indication, such a personalized approach would require a risk vs. benefit analysis to determine if the direct and indirect costs for such a procedure would be justified. To reach a stage of adoption, there would also need to be evidence to support an increased diagnostic yield using this method. 2 values higher than the generalized models, establishing that personalized models should be preferred over generalized models from an accuracy of transformation perspective. An LSTM neural network was explored as a transformation method to transform a subset of ECG leads into all the 12-leads. The subset of leads chosen for this study were leads II, V2, and V6. The deep learning model trained on a large corpus of data was fine-tuned with patient-specific ECGs to yield personalized lead transformation models. These personalized transformations were evaluated to determine the extent of performance gain that can be achieved in terms of accuracy of the transformations compared to ground truth . The personalized models showed RMSE values lower than the generalized models and pearson correlation coefficient, and RBlinded assessment of the diagnostic yield of such models was explored to determine the level of agreement between the diagnosis from the actual ECG waveforms and those derived from generalized models and personalized models. The PM-ECGs were found to be diagnostically equivalent to the actual ECGs.


The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users' academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users' explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users' academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations. With the continuous advancement of the digital construction of nursing teaching knowledge points, the number of electronic nursing teaching knowledge points has increased sharply, resulting in problems such as information overload and cognitive loss when users search for nursing teaching knowledge points . TherefoA nursing knowledge map is a specific knowledge base for the nursing field, which includes a series of entities in the nursing field and their associations. There is a precedent for the construction of a nursing knowledge map . Using twij, set a large neighborhood radius n, and set the number of neurons that are learning t. Randomly select a training mode x(t) = (x1(t), x2(t),…, xn(t)) to provide to the input layer of the network. Select the neuron matching the input vector as the winning neuron c. If the Euclidean distance is adopted, C isA SOM network is a tissue feature mapping network. Its basic principle is that for each input vector, a neuron in the output layer has the closest value to the input vector and wins by receiving the maximum stimulation. Some neurons around the winning neuron are also greatly stimulated due to lateral action. At this time, the network performs a learning operation, and the winning neuron and its surrounding neurons modify their own weight vector to move to the input vector . Each nex = ∈Rn, yi as the target prediction value corresponding to the input eigenvector, FM can use the decomposition interaction parameters to model all nested interactions of n input variables of x in d-dimension . The larger the value, the more consistent the sorting results are with the user's interests and preferences. Their entity information comes from the nursing guide. By grasping the nursing guide, we extract different sets of nursing entities and the contact edge sets between entities and construct the entity network in the nursing field. The extracted main entity information and the association information between some entities are shown in Where P represents the position of the item in the list, The traditional algorithm cannot be directly applied to the research object of this paper. In order to verify the effectiveness of the improved algorithm, this section will compare the clustering quality of the improved k-medoids algorithm with that of the improved SOM algorithm. The interclass distance and intraclass distance of the SOM improved algorithm and the k-medoids algorithm are shown in the following k-medoids improved algorithm are compared. Here, eleven experiments are also carried out on different algorithms to calculate the average repetition rate of different algorithms.From the perspective of stability, the repetition rates in the clustering results of the SOM improved algorithm and the k-medoids improved algorithm, which proves that the SOM improved algorithm in this paper also has good stability. The following figure shows the proposed SOM algorithm and the comparison algorithm MAP@10 and NDCG@10 results on evaluation indicators.In many experiments and simulations, the clustering results of the SOM improved algorithm can obtain a 67.0% repetition rate, which is similar to the clustering effect of the K. When k = 15, the performance of the four algorithms is the best. At the same time, it can be seen that SOM obtains the best performance under different K values. This is because, compared with FM, SOM adopts a pairwise sorting learning algorithm, so its performance is better than FM; compared with BPRMF, SOM can not only integrate the explicit and implicit feedback information of users but also integrate the text information of the borrowing nursing teaching introduction and the borrowing log information, so its performance is also better than the traditional methods. At the same time, SOM's sorting function FM uses interaction parameters rather than independent parameters to model the interaction between features, so SOM can obtain better performance. Especially in the case of sparse data, its performance is better.It can be seen from In the courses that need a lot of practice, the implementation of a project-based teaching method can quickly improve students' practical operation abilities. At the same time, the establishment of students' theoretical knowledge systems cannot be ignored. The nursing specialty is a highly practical specialty. It adheres to the project-based teaching method without making students with weak theoretical basic knowledge more backward. Cultivating practical talents is one of the ways to realize this. The cultivation of operation skills is an important task in nursing teaching. Students' nursing operation level directly affects the effects of clinical practice and the future development of nursing specialties, as well as the quality of their practical talents. Years of nursing practice show that nursing teaching management is a systematic, phased, complex, and carefully organized process. In order to ensure the effect of nursing operation teaching, this paper uses a SOM neural network to cluster users according to the users' academic background information, analyzes the explicit and implicit web access behavior of nursing teaching knowledge points, constructs the partial order relationship of nursing teaching knowledge points, classifies users' academic background web access behavior, and uses a point-to-point sorting learning algorithm to accurately recommend nursing teaching knowledge points. Different teaching purposes can be implemented for each type of students. Students with weak theoretical knowledge can purposefully integrate, so that excellent students can help weak students learn from each other, effectively improve the project teaching level, help students, promote students' healthy growth, and learn professional nursing skills.


Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity  of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs. As one type of structural variation, copy number variation (CNV) plays an important role in the formation and development of human cancers and diseases . GeneralA large number of CNV detection methods have been developed around NGS data, and the vast majority of them are based on the read depth (RD) method. The basic principle of RD-based CNV detection methods is that the number of reads aligned at each position in the reference genome is proportional to the copy number at that position . CompareThe basic process of RD-based methods for detecting CNVs includes: 1) Reading the alignment and extracting RDs; 2) Preprocessing the RDs; 3) Building the detection model ; 4) Selecting a reasonable threshold strategy and predicting CNVs. Based on the above workflow, some well-known RD-based CNV methods have been proposed, mainly including FREEC , CNV-LOFWith consideration of the above issues, we propose a new approach in this work to accurately detect CNVs using NGS data from the whole genome. The method is called shortest path-based CNV (SPCNV). The SPCNV calculates the k nearest neighbors of each RD and defines the shortest path, the shortest path relation, and the shortest path cost sets. Based on these three types of shortest path sets, we calculate the mean shortest path cost of each RD and its k nearest neighbors. A relative shortest path score equation is then built using the ratio between the mean shortest path cost of each RD and the mean of mean shortest path cost of its k nearest neighbors, which can calculate a score for each RD . Based oThe remainder of this work is organized as follows. https://github.com/gj-123/SPCNV/releases and is easy to install and use after reading the user manual. In the following section, each step in the workflow of SPCNV is analyzed and discussed in detail.SPCNV is an RD-based CNV detection method that is suitable for the detection of a single sample. The sequenced donor samples are aligned to the reference genome using the BWA tool, which generates sequence alignment files in the SAM format. The SAM files are further converted into binary sequence alignment files in BAM format using SAMtools. The read count (RC) profiles are then extracted with SAMtools from the BAM files. We define a sliding window procedure (bin) , with whsequence . Due to sequence . Factorssequence  is used  profile , which iThis transformation process can detect differences in RD from two perspectives. The first dimension can approximately reflect the copy number status corresponding to each RD from a global perspective. The second dimension can approximately reflect the difference between an RDS and its adjacent RDSs from a local perspective. By extracting two features of RD, the proposed method can more easily discover globally isolated and local small cluster variants. At the same time, this step also provides an effective data platform for constructing the relative shortest path score in the next section.o. The RSPS fully reflects the closeness between an object and its surrounding objects and is highly suitable for application in CNV detection scenarios. The RSPS of an object depends on the ratio between the object’s shortest path and the mean of its k nearest neighbors’ shortest paths. Some related basic concepts and definitions must be introduced before giving the definition of RSPS, which mainly include the k-distance of an object, the k-distance neighborhood of an object  of an object o is defined as the edge between two objects on the shortest path. The shortest path cost set (SPCS) of an object o is defined as the distance between two objects on the shortest path. o), SPRS(o), and SPCS(o) in detail.The shortest path set (SPS) of an object The following example is used to clearly explain the SPS, SPRS, and SPCS calculation process of an object. As shown in Algorithm 1o. Calculate SPS, SPRS and SPCS of object o  to measure the degree of deviation between object Although we have evaluated the relative shortest path score for each object, it is not yet possible to distinguish abnormal objects from normal objects. This step is critical in CNV detection, and a reasonable threshold selection strategy will significantly improve the accuracy of the detection results. Traditional threshold selection strategies mainly include: 1) Fitting a statistical model using the score profile to evaluate the significance of each object and using hypothesis testing to predict CNVs; 2) Selecting an empirical value as a threshold to identify CNVs. The limitation of the first strategy is that due to the bias and noise of the sequencing data, the actual distribution of the data and the fitted model are quite different, resulting in inaccurate detection results. The limitation of the second strategy is that a fixed threshold can effectively identify abnormal objects in some scenarios. However, the performance of the method may drop significantly in certain application scenarios. Considering the above two scenarios, we use a boxplot to determine thresholds based on the score files. This method does not require an assumption that the score profile obeys a certain distribution in advance and is able to dynamically determine thresholds according to different score files. Here, we use Eq. Along with the establishment of SPCNV, the design of a reasonable experimental scheme is crucial for verifying the effectiveness of the proposed method. In this study, the experimental component was divided into simulation and real data experiments. In the simulation data experiments, the performance of the proposed method was compared with four well-known similar methods  from five perspectives: recall, precision, F1-score, the number of gain and loss detections, and sensitivity of different size CNV detection. In order to verify the performance of the proposed method in real data applications, the above four comparison methods were also selected for comparison with the proposed method. The experimental data was a set of real human sequencing samples from the 1000 Genomes Project. Some previous studies have tested these samples and saved the test results to the Database of Genomic Variants (DGV), which was used as ground truth to calculate recall, precision, and F1-score for each method.IntSIM  simulatiBased on the simulated datasets, the performance of SPCNV and four other alignment methods  were tested by calculating their recall, precision, and F1-scores. Recall is defined as the number of correctly detected CNV events divided by the total number of simulated CNV events, which can be calculated by the ground truth file . PrecisiBased on the above analysis and discussion, we further analyze the performance of each method in detecting gains and losses. As a supplement to the above experiments, we further analyzed the sensitivity of each method to detect CNV of different lengths. http://www.internationalgenome.org/. Some of the test results for these samples are recorded in the DGV, which is considered the ground truth for calculating the recall, precision, and F1-scores for each method. Similarly, we select the four methods  of the above experiments to compare with the proposed method. The experimental results are shown in In order to verify the performance of the proposed method in real application scenarios, we use six real data samples  from the 1,000 Genomes Project, which can be downloaded for free from In this work, a new method called SPCNV was proposed to detect CNVs using NGS data. SPCNV was developed based on the RD method and could be used to detect a single genome-wide sample. The proposed SPCNV method effectively removed abnormal bins, calibrated GC-content bias, and denoised and transformed the dimension of the read depth. Based on the preprocessed RD profile, it then computed the k nearest neighbors for each object. On this basis, we constructed the shortest path set, the shortest path relation set, and the shortest path cost set for each object, by which the mean shortest path cost was defined. At the same time, we computed the mean shortest path cost for each object and used the ratio between the mean shortest path cost of each object and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score. Based on the relative shortest path scores for each object, CNVs were predicted using boxplots. Both simulation and real data experiments were then carried out to verify the performance of the proposed method. In the simulation data experiments, the performance of the proposed method was evaluated from five aspects . The experimental results showed that the proposed method achieved the best balance between recall and precision, gain and loss, as well as CNV of different sizes, respectively. In real data applications, the proposed method achieved the best F1-scores in most samples, indicating that its performance was reliable and effective in real application scenarios.p-value for each read depth, and use hypothesis testing methods or select a fixed threshold to predict CNVs. Compared with the traditional methods, the proposed method has three different characteristics, which are summarized as follows. 1) Compared with traditional density-based methods, using objects to construct shortest paths can effectively identify a small cluster of local variants, which have little difference but are isolated relative to all objects. 2) We treat a CNV as an outlier and effectively transform the outlier detection method into a CNV detection method. 3) Extracting the RD ratio and difference of the RD ratio provides a global and local perspective to capture the difference of the copy number corresponding to each RD, which is more conducive to the detection of local single isolated CNVs and local small clusters of CNVs.Traditional CNV detection methods generally assume in advance that the RDs obey a statistical model, use the model to calculate a Although the performance of the proposed method meets the detection needs to a certain extent, there are still some shortcomings that require improvement. At this stage, the resolution of the proposed method must be further improved. In the next step, we will extract the read depth and split reads to further enhance its resolution . The sel


The research and development of laser systems for intracavity phase interferometry is described. These systems are based on an intracavity synchronously pumped optical parametric oscillator (OPO), enabling the generation of two trains of picosecond pulses inside a single cavity. In such a configuration, it is possible to measure the beat note frequency between two pulses and to very precisely determine the phase difference between them. The pump source is a diode-pumped passively mode-locked Nd:YVO A review of the research on intracavity phase interferometry (IPI) performed by our group over the past decade is presented. This work complements a report  by—to ouc effect , accelerc effect  or nonlic effect . Examplec effect  and rotac effect . In cont of 1 Hz , while tAll active laser gyroscopes that are currently in commercial use are based on continuous gas lasers with inhomogeneous gain line broadening . If the line is homogeneously broadened, mutual gain saturation will result in unidirectional operation. At small rotation rates, the beat note response may vanish because of coupling between the counter-circulating beams caused by scattering. Back-scattering of one beam into the other causes the two beams to acquire the same frequency through mutual injection . If the The dead band, however, can be totally eliminated by the use of mode-locked lasers where the pulse crossing regions are in a vacuum or in optical elements that do not lead to phase coupling between the circulating pulses in the cavity ,8. AnothThese points were confirmed in experiments conducted using dye or Ti:Sapphire lasers ,5; howevThe total absence of the dead band is realized with optical parametric oscillators (OPO) synchronously pumped by a mode-locked laser . In suchThis setup is applicable in sensor applications where the physical quantity to be measured is converted into a phase difference between the two counter-propagating pulses in the cavity ,16. The The instability created by small beam pointing fluctuations of the pump laser can be prevented when the pump beam is part of the same spatial cavity mode for both directions. This was accomplished by inserting a nonlinear crystal in the resonator of the pump laser  and thusIn contrast to previous works, when the Ti:sapphire laser was used as a pumping source, we decided to use a neodymium mode-locked laser. The Nd:YVOigh gain ,20. The As a gain medium for the parametric oscillator, a periodically poled magnesium-doped lithium niobate crystal (MgO:PPLN) is chosen. Quasi-phase matching guaranteed by periodic poling of the PPLN crystal provides fulfillment of the phase-matching condition, which makes the OPO design simpler and less sensitive to precise setting and reduces the lasing threshold. To eliminate any potential dead band, the PPLN crystal is placed inside the pump laser cavity. In the PPLN crystal, on each passage, one signal pulse is generated or amplified by a pump pulse twice per round trip. This provides the same path for the signal and pump pulse through the PPLN crystal and high pump intensity inside the cavity. Nevertheless, as this is quite a complicated setup, it is difficult to achieve a perfect spatial overlap of the signal and pump beam in the PPLN crystal and with a stable mode locking of the pump laser. Insufficient overlap is a problem not only because it can decrease conversion efficiency, but it is also one of the parameters that influences double-pulse operation and may be the cause of a single-pulse regime.We have developed an intracavity synchronously pumped OPO, generating two independent trains of picosecond pulses ,22,23 suAll systems presented here consist of two main parts: the quantum-well mode-locked Nd:YVOr = 500 mm), pair of concave mirrors M3 and M6 (r = 300 mm) focusing the radiation inside the PPLN crystal, and concave mirror M7 (r = 300 mm) focusing the radiation on the M8 of the end mirror with a saturable absorber . All the mirrors are highly reflective (HR) @ 1064 nm. The cavity is designed to be insensitive to the thermal lens effect in the active medium for a wide range of pumping power. The design is described in detail in references [The pump laser cavity is designed so that the resonator mode diameter at the location of the active laser crystal matches the laser diode beam, the beam radius at the nonlinear crystal is as small as possible, and the highest possible radiation intensity is achieved at the QW. The active medium of the pump laser is anti-reflection coated 0.5 at.% Nd:YVOrors see : flat puferences ,28. The r = 150 mm, HR @ 1.5 The experimental setup is shown in The second tested crystal had dimensions of The results obtained using those crystals were similar; however, the configuration with the Brewster angle cut crystal was harder to set up because of the more complicated alignment (different paths for different wavelengths). The generated wavelength of the OPO was tuned by changing the PPLN crystal’s temperature. The crystal’s temperature was controlled using a special copper mount and could be set in the range from 25 to 80 r = 3 m, HR @ 1.5 We continued with the knowledge obtained during the implementation of the linear OPO cavity and realized the ring OPO cavity. This optical parametric resonator was designed as a ring, slightly focusing stable cavity, and included a curved long-radius mirror M9 , the sum of the pump and signal (628 nm), the pump (1064 nm), the signal (1534 nm), and the second harmonic of the signal wavelength (767 nm). It was possible to tune the signal wavelength by changing the PPLN crystal temperature. The sum of the pump and the signal was used as a visible sign of parametric generation and the quality of the beam.Pump pulse duration was measured by a laboratory-designed noncollinear second-harmonic-generation-based autocorrelator and was determined to be 15 ps (FWHM). For the long term stability measurements, an EOT photodetector ET-3000  in the vicinity of This setup was even more tricky to adjust. The OPO resonator arm length detuning also changed the angle of incidence on the mirrors and the adjusting caused further changes of the length. System operation was very sensitive to any perturbations; we observed fluctuations in signal and pump pulse amplitude as seen in In the cavity of synchronously intracavity-pumped OPO, it was possible to achieve the regime of pulse multiplication. This so-called quasi-synchronous pumping enabled not only the generation of the OPO pulse with a repetition rate corresponding to the pump pulse repetition rate but also its higher harmonics.n is an integer, the synchronous pumping occurs after n round trips of the OPO cavity that corresponds to n-times higher than that of the pump [The principle is as follows: In the synchronously pumped OPO, the length of the linear OPO cavity the pump . The prin. We have successfully proved the pump multiplication of our linear OPO for factor n from 1 to 15. The basic setup of the cavity is shown in n is in n can be explained by the change of effective OPO outcoupling with the increasing n. From this, it follows that the increase of n decreases the amplitude of the signal pulse by interacting with the pump pulse inside the nonlinear medium. This caused a smaller depletion of pump pulse amplitude, which reduced the nonlinear coupling between pump and OPO cavities, leading to better stability of the laser system. A similar effect should be obtained by changing the OPO output coupler losses, but changing the n is more flexible. An example of measured long-time stability is presented in The value of the cavity lifetime limited the possible multiplication factor The quasi-synchronous pumping of the OPO, based on the precise mutual length change of the pump and the OPO cavity length has been already demonstrated. Using the Ti:Sapphire femtosecond laser extra-cavity-pumped linear OPO 1 GHz pulses up to the 14th harmonic were generated . We demon is an integer, the synchronous pumping occurred after n round trips of the OPO cavity corresponding to n-times higher than the pump repetition rate [The frequency multiplication principle in synchronously pumped ring OPO is analogous to that described in the previous section. The mutual cavities length difference (shortening in our case) was ion rate ,33,34.n from 2 to 28. Oscillograms of the signal OPO trains for pulse multiplication factor 7 and 27 are shown in We have successfully achieved OPO repetition rate frequency multiplication for factor n is shown in n is presented in n, the tolerance is smaller and the setting must be more accurate. In this case, only small fluctuations of the OPO signal were present and the modulation of the pump pulse amplitude was negligible. The OPO signal behind mirror M10 was detected simultaneously in both directions, confirming the bidirectional operation of the OPO. The possible detuning allowing parametric generation was significantly shorter than for Dependence of the OPO output power behind mirror M10 on the factor of multiplication We can conclude that we have successfully demonstrated the multiplication of the pump repetition rate inside the ring synchronously intracavity-pumped OPO induced by precise mutual change of the pump and OPO cavity length and this system could be used as a widely tunable high repetition rate pulse generator. We have also observed that, by appropriate selection of the multiplication factor, the coupling between both cavities could be controlled, leading to an increase in overall laser system stability.In The method to overcome some of these limitations, at least for the linear OPO configuration, was demonstrated for extracavity pumping by A. Velten . In thisThe arrangement was based on the previous linear setup see . In thisom noise ,33,37, sTo prove the applicability and to measure the capabilities of the presented intracavity quasi-synchronously pumped OPO, we modulated the phase of one of the two pulse trains and monitored the beat note frequency between them. For this purpose, a laboratory-built electro-optic modulator (EOM) based on an LiNbOribed in .The beat note measurement was performed with a slow photodiode. Both pulse trains were extracted from the OPO by a beam splitter (semitransparent mirror M11) and directed to the slow photodiode D2 ΔφB=2d distance between electrodes, and l is the length of the crystal in the direction of laser beam propagation. For the calculations of For comparison, the theoretical phase shift difference in V0 by :(3)ΔφM=V0−10 m/V . In the We have demonstrated the achievement of two independent combs generation in a single cavity of synchronously intracavity-pumped optical parametric oscillator in linear and ring geometry of the signal cavity. The systems were based on the SESAM-modelocked, picosecond, diode-pumped Nd:YVONext, we have shown that with these systems, it is possible to realize quasi-synchronous intracavity pumping in such OPOs. This was achieved by multiplication of the pump repetition rate inside the ring intracavity synchronously pumped OPO by the precise changing of the mutual lengths of the pump and OPO cavity. In the linear configuration, the generated OPO pulse repetition rate was adjustable from 80 MHz up to 1.2 GHz. In the ring configuration, the generated pulse repetition rate was adjustable up to 2.3 GHz. These systems could be used as a widely tunable high repetition rate pulse generators or could be useful for applications such as pump-probe spectroscopy.Another modification of this system based on the generation of subharmonic frequencies enabled us to generate two independent pulse trains in the OPO and phase shift difference associated beat note frequency caused was measured. The applicability of this system for IPI was thus successfully demonstrated. The bandwidth of the observed beat note was less than 0.9 Hz (FWHM) without any active stabilization. This arrangement can be used to measure the frequency dependence of nonlinearities.


Short-range biomimetic covert communications have been developed using dolphin whistles for underwater acoustic covert communications. Due to a channel characteristics difference by range, the conventional short-range methods cannot be directly applied to long-range communications. To enable long-range biomimicking communication, overcoming the large multipath delay and a high degree of mimic (DoM) in the low-frequency band is required. This paper proposes a novel biomimetic communication method that preserves a low bit-error rate (BER) with a large DoM in the low-frequency band. For the transmission, the proposed method utilizes the time-dependent frequency change of the whistle, and its receiver obtains additional SNR gain from the multipath delay. Computer simulations and practical ocean experiments were executed to demonstrate that the BER performance of the proposed method is better than the conventional methods. For the DoM assessment, the novel machine learning-based method was utilized, and the result shows that the whistles generated by the proposed method were recognized as the actual whistle of the right humpback whale. Recently, a biomimetic communication method has been studied for military purposes. Biomimetic communication methods make the enemy confuse our communication signals with dolphin or whale sounds to achieve covertness ,12,13,14Conventional biomimetic communication methods utilize various modulation methods such as chirp spread spectrum (CSS), frequency shift keying (FSK), phase shift keying (PSK), and time-frequency shift keying (TFSK) to modulate bits into whistles ,12,13,14The first problem is an increase in BER due to a large multipath delay. As the range between transmitter and receiver increases to tens of kilometers, the multipath delay also increases up to a few seconds ,23,24,25Since the whistle is a non-linear chirp signal, it has strong robustness against the distortion of large multipath delay. Thus, if the bit can be modulated while maintaining the shape of the whistle, the modulated signal has tolerance to the large multipath delay and the high DoM performance. Whales generate whistles with various frequency contours, but they have a habit when generating the sounds of the whistle. The whistle-generating habit is that the following frequency values are limited by the frequency value of the previous time. The proposed biomimicking modulation method utilizes this frequency-change habit of the whistle.We modeled the time-dependent frequency change of the whistle of the right humpback whale (RHW) using the Markov chain (MC). The modeled results can be employed to select the frequency values used by the whistle. Note that we considered the time-frequency orthogonality of the symbol to preserve low BER when modeling the frequency change of the whistle.The proposed modulation method preserves the signal shape of the whistle because it modulates bits using frequency values that whales use to generate a whistle. Thus, the whistle-mimicking communication signal generated by the proposed modulation method shows improved performance of the DoM and tolerance of large multipath delay.The proposed demodulation method estimates the multipath delay profile using a preamble and increases received signal gain using the estimated profile. Thus, the proposed method achieves low BER in a long-range underwater environment with a large multipath delay.Computer simulations and practical ocean experiments were conducted and demonstrated that the proposed method had a lower BER than conventional covert communication methods.Since the proposed modulation method makes a whistle using modeling results, a machine learning-based DoM assessment was conducted. The assessment results show that the trained machine learning classifier recognized the whistle-mimicking signal generated by the proposed modulation method as the whistle of the RHW. This result shows that machine learning can be used as an effective evaluation method for the DoM performance of biomimetic communication.The main contributions of the paper are summarized as follows:This paper is organized as follows. Whales communicate with each other using whistles. For the long-range biomimicking communication method, we selected the RHW as a mimicking model. The RHW is about 15 m long and makes sounds from tens to hundreds of hertz. One of the simple examples of the proposed method is shown in The whistle-generating habit of the whale is that the subsequent frequency values are restricted by the frequency value of the previous time ,28,29,30For the modeling, the frequency values of a whistle over time were extracted by short-time Fourier transform (STFT). When the time window length of STFT is It is assumed that there are In Equation (1), In In The proposed method utilizes the model of the whistle-generating habit of the RHW when it modulates/de-modulates bits. The transmitter and receiver share these modeling results. The proposed transmitter uses a pseudo-random number generator and randomly selects one model among the models with various lengths of the window. It is assumed that the receiver also selects the same model using the same pseudo-random number generator. Firstly, the proposed biomimicking modulation method is described.The time-frequency units of the model (In In Equation (2), when In Equation (3), the biomimicking communication signal in the The block diagram of the proposed biomimicking modulation is shown in A preamble is inserted in front of the biomimicking communication signal to find the received signal and estimate the channel delay at the receiver. This preamble is the original whistle of the whale. Let us assume that the receiver also has the same preamble. The frame structure of the biomimicking communication signal is shown in The proposed biomimicking whistle is the summation of several LFMs. Therefore, like LFM, the auto-correlation function becomes a Kronecker delta function. If the channel delay can be estimated, an additional signal gain can be obtained using signals received through multiple paths, reducing bit error. The following section describes the reception method.The transmitted signals generated using Equation (4) pass through the underwater channel and are received at the receiver. The received signal In Equation (5), To find a symbol sequence that satisfies Equation (6), the proposed demodulation method calculates a correlation between the transmitted whistle and all whistles that the model can generate. When the generated whistle is As derived from Equation (7), the correlation between If The block diagram of the proposed biomimicking demodulation method is depicted in This section evaluates the degree-of-mimic (DoM) performance of the proposed method. Quantitative  and qualitative methods  have been proposed to measure the degree of mimic in conventional biomimetic studies ,12,13,14The proposed DoM evaluation method discriminates even if the biomimicking whistle is the sound of a specific whale species. The proposed method not only judges whether a biomimicking whistle sounds like a whale, but also determines whether it simply sounds like a specific species . Determining a species of whale with only whistles is a professional task and requires much experience. Thus, to complete this task effectively, whistle classifiers based on machine learning have been studied ,40. In tThe whistle in In The BER performance of the proposed biomimicking method was compared with that of conventional methods  in the long-range underwater environment. The modulation order and parameter were selected for the fair BER comparison to yield a similar data rate. These values are given in In There are many simulators for modeling underwater acoustic sound propagation. However, we referred to the measured channel from the experiments of long-range  underwater acoustic communication for realistic simulation. In −2~10−1. When SNR was lower than −10 dB, TFSK showed the lowest BER. However, the data rate of the TFSK was also half that of the others. When proposed without multipath gain, the BER performance of the proposed method was similar to that of TFSK. Please note that, as shown in In Practical ocean experiments with the proposed and conventional methods were executed 60 km away, eastbound from Pohang city, on 18 May 2022. The range between the transmitter and the receiver was over 60 km. The average depth of the ocean was about 1100 m. The transmitter was located at 295 m with a depth of 850 m. The bandwidth of the transmitter was 2.6 kHz to 4.6 kHz, and we upconverted the center frequency of the whistle to 3 kHz. The three-channel receiver was utilized, and each sensor was positioned at 205 m, 230 m, and 255 m below the ocean surface. The depth of the receiver was about 1200 m. The parameters of the proposed and conventional methods were the same as those of computer simulations in In The average BER of the proposed method was 0.0057, whereas the conventional method exhibited 0.079 and 0.1165. The BER of the proposed method without the multipath was 0.009, which is also lower than conventional methods. This result shows that the proposed method obtained gains from the multipath. In the ocean experiments, the SNR of the received signal was estimated at about 5 dB, and the multipath delays of the second and third sensors were similar to those of the simulation experiment in This paper proposes a biomimicking modulation method for long-range underwater acoustic communication. The proposed method mimicked the whistle of a large-size whale  and modeled the time-dependent frequency change of the whistles. This model was utilized to overcome the large multipath delay in long-range underwater acoustic communication. Results of the computer simulations and the ocean experiment demonstrate that the BER performance of the proposed method is better than that of conventional studies. In this paper, the novel DoM assessment based on a machine-learning classifier was also utilized to demonstrate that whistles generated by the proposed method are the same as those of the RHW. The assessments showed that the whistles generated with the proposed method were determined as a natural RHW sound, whereas the conventional methods were not. Thus, the proposed method shows better BER and DoM performance than that of conventional methods.


Mobile Edge Computing (MEC) technology and Simultaneous Wireless Information and Power Transfer (SWIPT) technology are important ones to improve the computing rate and the sustainability of devices in the Internet of things (IoT). However, the system models of most relevant papers only considered multi-terminal, excluding multi-server. Therefore, this paper aims at the scenario of IoT with multi-terminal, multi-server and multi-relay, in which can optimize the computing rate and computing cost by using deep reinforcement learning (DRL) algorithm. Firstly, the formulas of computing rate and computing cost in proposed scenario are derived. Secondly, by introducing the modified Actor-Critic (AC) algorithm and convex optimization algorithm, we get the offloading scheme and time allocation that maximize the computing rate. Finally, the selection scheme of minimizing the computing cost is obtained by AC algorithm. The simulation results verify the theoretical analysis. The algorithm proposed in this paper not only achieves a near-optimal computing rate and computing cost while significantly reducing the program execution delay, but also makes full use of the energy collected by the SWIPT technology to improve energy utilization. In the 5G era, the unprecedented growth of real-time Internet of Things (IoT) applications can generate a vast volume and variety of data . It not Considering the characteristics of MEC’s data processing capacity and SWIPT’s energy collection, a multi-terminal, multi-relay, and multi-server edge offloading and selection architecture with the advantages of MEC and SWIPT is designed. The MEC server can provide high-speed computing services, but it also has computing costs;Under a time-varying environment and the time and energy consumption constraints of the IoT, we propose two non-convex problems related to computing rate and cost. Each non-convex problem is decomposed into two subproblems;In contrast to other static optimization method, we propose an AC algorithm of online dynamic optimization by combining the system model. The improved actor module and the critic module are updated iteratively. By the adaptive setting method of k, we can quickly find an offloading scheme that maximizes the computing rate and a selection scheme that minimizes the computing cost.The simulation results verify the effectiveness of the AC scheme.The main contributions of this paper are summarized as follows:In In recent years, edge computing has been extensively studied and implemented in various frameworks. The authors of ,13,14,15The way we can interact with the environment by learning is called dynamic optimization. Scheduling methods based on reinforcement learning or deep learning are an important approach for system dynamic optimization . ReinforThe DRL algorithm solves the dimensional disaster of traditional reinforcement learning by using a neural network storage strategy set, which makes it easier to deal with complex environments and realize the optimal processing of edge computing . It is aIn order to solve the above problems, a multi-terminal, multi-relay and multi-server SWIPT system model is designed. This model can comprehensively show the scenario of devices connected to the IoT. Aiming at the problem of edge network computing under time-varying channels and time-varying prices, two non-convex problems are proposed to maximize the computing rate and minimize the computing cost under the constraints of delay and energy consumption. This paper converts every problem into two subproblems. In terms of computing rate, the first subproblem is solved by the actor module, and the AC offloading scheme is obtained. To solve the second subproblem, we allocate a time slot using a convex optimization algorithm to maximize the computing rate in the critic module. For the computing cost, the actor module is used to solve the first subproblem and the AC selection scheme with the smallest computing cost is obtained. Then, we substitute the AC selection scheme into the critic module to solve the second subproblem and calculate the minimum computing cost. Finally, we find the optimal AC offloading algorithm and AC selection algorithm by iterating and updating continuously. In this model, we adopt a binary task offloading policy . All thej-th server to the relay, and i-th terminal, i∈  signals. The relay receives the signal, amplifies and forwards it, and then transmits it to N terminals. The terminal uses time switching (TS) mode to divide the received RF signals to collect energy and decode information. The collected energy is stored in a rechargeable battery to provide energy for the calculation of the terminal. The decoded information contains servers’ offloading price list within this time frame. We ignore the mutual interference between offloading data and downlink data. Time allocation refers to the reasonable division between SWIPT time We have assumed a real situation in IoT. Terminals such as smart phones or computers need the help of edge servers to process data. Users will pay for the offloading as the system cost. We need to maximize the data processing and minimize the cost. The MEC relay system with SWIPT designed in this paper is shown in We can use a traversal algorithm to obtain the maximum rate and minimum cost, which is known as the benchmark rate and benchmark cost. However, a traversal algorithm needs to go through The server transmits a RF signal with normalized power Then, the transmitted power at the relay is: i-th device is: The energy collected by the There are two computing modes for all terminals: local computing or offloading. The computing rates in the two states are given below.Terminals in a local computing state can collect energy and compute tasks at the same time . Therefopment is : (3)Eloctructure . There aThe local computing rate i-th terminal. There are time constraints: Since the computing rate of the server is much higher than the rate of the terminal, we ignore the time of task calculation and task receiving. In order to improve energy utilization, this section will consume all the collected energy, but the energy consumption cannot exceed the energy collected. Thus, the terminal’s optimal transmit power It can be deduced that the signal and interference to noise ratio (SINR) in the offloading process is : (6)SINRB is the channel bandwidth.Thus, the offloading computing rate The total offloading data flow of the system can be expressed asl: the number of servers allowed to process. The total cost of the system j-th server to process the offloading information flow. j-th server. The total offloading data flow is evenly divided according to Q can be expressed as: i-th terminal in offloading and i-th terminal in local computing.The total computing rate The problem of the maximum computing rate can be expressed as: The location , as showWhat is desired is an offloading scheme close to the benchmark rate. That is, in a time-varying IoT, we need to design a policy function that will be updated gradually to take action quickly and accurately. Therefore, we choose the online algorithm based on the policy gradient. The deep neural networks (DNN) structure is simple, but it can gradually improve the policy function from experience to solve complex decision-making problems. It is suitable for a changeable and unknown IoT. Therefore, we take DNN as the actor module to optimize the network parameters for the purpose of maximizing the rate. The offloading policy part uses the actor module to find the AC offloading scheme. This will be explained in detail in After the AC offloading scheme is obtained by the actor module, P1 becomes a solvable convex problem P2. At the same time, we need a standard to judge whether the action is good or bad. Therefore, we use the convex optimization algorithm of one-dimensional double-section search  to allocThe problem of minimum computing cost can be expressed as: Combinatorial mode selection variable After the AC selection scheme is obtained via an actor module, P3 becomes the solvable problem P4. We substitute the AC selection scheme into the P4 problem to calculate the cost to judge the quality of the scheme. We call it the critic module. The critic module we use only needs to analyze and solve the cost problem to make an accurate evaluation.Then, a small number of comparisons can complete the optimization goal. Compared with traditional critic module, there is no need to compare many value functions, and the small error brought by the value function does not affect the system. The two modules obtained repeatedly and alternately to adapt to the change in the environment are called the AC selection algorithm. The common use of the actor module and the critic module makes the updating of the actions of the edge system more accurate and faster than other DRL methods.AC algorithms perform transitions from input states to output actions in uncertain edge environments . The trat-th time frame. The purpose is to find the offloading action that maximizes the computing rate of the system. Therefore, the algorithm structure is shown in Two time-varying channel gains are used as the input of the offloading scheme in the t-th time frame and get the relaxed offloading action We input two channel gains at the N. Quantize k quantized action sets i-th terminal. In general, k can be any integer within .Greedy offloading scheme: each terminal only completes the computing task by offloading. The offloading scheme is fixed as .Random offloading scheme: Each terminal chooses to complete the task by using local computing or offloading randomly.AC offloading scheme: Each terminal chooses to complete the task by local computing or offloading by training the AC algorithm. The goal of each terminal is to maximize the computing rate.DQN offloading scheme: Each terminal chooses to complete the task by local computing or offloading by training the DQN algorithm. The goal of each terminal is also to maximize the computing rate.It can be clearly seen that the AC offloading scheme is significantly better than the other schemes in First, we need to verify the effectiveness of the DNN.Equation  for normGreedy selection scheme: The offloading data stream is equally distributed to each server for calculation processing, and the selection scheme is fixed as .Random selection scheme: The offloaded data stream is randomly distributed. There is no case where the selection scheme is . We must select at least one server for data processing.AC selection scheme: We control servers to offload or not to offload by training the AC algorithm. The goal of each terminal is to minimize the computing cost.DQN selection scheme: We control servers to offload or not to offload by training the DQN algorithm. The goal of each terminal is also to minimize the computing cost.It can be clearly seen that the AC selection scheme and the DQN selection scheme are significantly better than the other schemes in Compared with the traditional traversal algorithm, the program execution delay of the AC algorithm and DQN algorithm is reduced significantly in This paper combines two advanced technologies, MEC and SWIPT, to simulate a multi-relay, multi-terminal, and multi-server SWIPT edge computing model. Firstly, we derive the system computing rate and computing cost. Secondly, under the time-varying price of edge server processing data and two time-varying channels, a DRL algorithms is used to obtain an offloading scheme and selection scheme that maximize the computing rate and minimize the computing cost. Finally, we come to a conclusion. We can obtain learning results close to the benchmark rate and benchmark cost in a shorter time when using DRL algorithm. Compared with the traversal algorithm, the AC algorithm can achieve the same excellent performance and reduce the program execution delay greatly. Due to the value function network parameters and exhaustive search when selecting actions, the performance and program execution delay of the DQN algorithm are slightly worse than that of the AC algorithm, but it still has advantages compared with other algorithms. The superiority of the DRL algorithm is verified. In the future, we think edge cache technology will also be a good choice. It can store the resources that are often used in the memory of the device or server, which will send data quickly and reduce the computing cost when the terminals need them. At the same time, we will continue to pay attention to the application of the DRL algorithm.


Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms. Bottleneck detection on motorways is a known and investigated research topic owing to the increased development of urban areas and intense transport service increase due to globalization . The resConnected Vehicles (CVs) have emerged recently due to the development of communication technologies and Intelligent Transport Systems (ITS) services. Recently, many researchers are covering the CVs topic with the research regarding collaborative machine learning , CVs in The traffic bottleneck is a phenomenon that can occur in urban and motorway roads. According to Ref. , the mosAs discussed in , standar-Proposed method for the spatiotemporal motorway traffic patterns extraction which includes STM-based model;-Proposed bottleneck detection method based on computation of parameters extracted from the STM;-Bottleneck detection results evaluated using different motorway traffic scenarios that include collision site, recurrent, and moving bottlenecks prove the effectiveness of the proposed method.In this context, the contributions of this paper are as follows:This paper is organized as follows. m represents time intervals and n motorway segments. Matrix data representation can represent spatiotemporal data but requires more computing power and advanced analysis techniques. Speed and density are the most common parameters used in the research for bottleneck detection. In [n varies between 10 and 90.The first step in bottleneck detection is choosing the traffic parameter that will be used. Many researchers use traditional traffic parameters to extract the traffic congestion areas and identify the bottleneck. The most common traffic parameters used for traffic data analysis and the road traffic scenario creation are speed, traffic flow, and density . The obsrval i∈m . The maiheatmaps . Here, ahere A=,…,ation. In , authorsTransition models can be seen in 90 papers within Daganzo’s research . The autIn this paper, the speed transition model is proposed by using the STMs. STM does not suffer from data loss due to averaging the values into narrow intervals because speed data are not averaged in such a way. Average values of the speed at single road segments are computed using all collected speed data on the observed road segments. Thus, there are two challenges related to classic averaging that need to be addressed: (i) averaging thousands of scalar values often results in wide confidence ranges, and (ii) values represent the speed only on one road segment, with no data related to the interactions with the consecutive road segment. The STM averages the speed values not on one road segment, but on the transition point between two consecutive segments, which includes pair of two speeds that show the interaction between two adjacent road segments. By using the Center of Mass (CoM) measure, pairs of origin and destination speed values are averaged using the harmonic mean. Thus, speed for one vehicle is computed using harmonic mean, on origin and destination road segments for one transition, and placed into the STM. With this procedure, we weighted the speed average with the CoM, and spatiotemporal relations of the transition were captured. Thus, the change of the traffic pattern position in the matrix is observed during the transition, which provides a more visually interpretable way of representing and detecting the bottlenecks.The authors of  analyzedIn , the autThe main limitation of mentioned motorway bottleneck detection approaches is the inability to detect an already existing bottleneck on the observed road segments. The flexibility of the proposed STM-based approach in this paper fills the gap, and it can detect several different bottleneck scenarios, including an already existing bottleneck, moving bottleneck, and recurrent bottleneck. The second limitation of the observed approaches relates to the flexibility of the methods to consider a bottleneck’s length and duration. With the usage of the STM-based approach, this is achievable with counting the motorway segments affected by a bottleneck.Definition 1.Motorway road network: Formally, road networks are represented as directed graphs Definition 2.Spatial transition: Movement of one vehicle between two edges Definition 3.Speed transition: Vehicle’s travel throughout a transition in interval Definition 4.Road traffic states: In this paper, three road traffic states categories are defined based on ,19: (i) Definition 5.Motorway bottleneck: Normal traffic conditions can be described with traffic flow Definition 6.The STM captures the vehicle’s speed at the spatial transition to represent the speed probability change, and therefore it represents the speed probability distribution at one spatial transition in The STM can be represented with the expression:where There are five examples of the characteristic STMs on the motorway visually represented in From the examples in This paper aims to propose a methodology for motorway bottleneck probability estimation using traffic patterns extracted from the STMs. The overview of the methodology is presented in The bottleneck probability estimation is further explained in depth with the example in To amplify the importance of the location of the patterns, the method for the motorway bottleneck detection consists of three parts: (i) estimation of the CoM for every traffic pattern represented with STM, (ii) computation of the distance between CoM and the diagonal of the STM, and (iii) computation of the distance between CoM and the origin of the STM. The next subsections explain each of the parts in detail.To successfully detect the motorway bottleneck caused by the congestion using the STM, the position of the traffic pattern represented by the STM is crucial. The position of the traffic pattern is used as the main feature in estimating the traffic state on the motorway. In this paper, the CoM, based on the computation of the expected value, adopted from , is chosus works ,21.When the CoM is computed, appropriate features must be extracted to quantify the bottleneck probability. In this paper, two features are extracted from the computed CoM: (i) distance from the origin of the STM labelled as The first feature, However, the second feature must be introduced to represent other possible positions of the CoMs, for example, at points As features Every variable is represented using three linguistic expressions, “small”, “medium”, and “large”, representing the possible states of the variables. The term “small” is represented using the Z-type membership function, “medium” is represented by Gaussian function, and “large” is represented by S-type membership function. The bottleneck probability variable has an offset of 0.1 to the left to allow the system to be more sensitive to speed changes.The framework for motorway bottleneck estimation was made in SUMO traffic simulator  coupled To evaluate the proposed method, four traffic scenarios for simulation were created as shown in The second and third scenarios b were crThe fourth scenario simulates a high inflow of Heavy-Duty Vehicles (HDVs). The inflow of vehicles on the mainstream and on-ramp was set to be the same as for the first scenario. With this scenario, the proposed method is tested against the detection of moving bottleneck caused by slow HDVs.The proposed bottleneck probability estimation method was evaluated on a synthetic dataset extracted from the SUMO traffic simulation framework. The method was evaluated on four different possible motorway congestion scenarios that originate from different sources of congestion, namely: traffic accident, short recurrent congestion due to high on-ramp inflow, long recurrent congestion due to high on-ramp inflow, and moving bottleneck originating from a substantial amount of slow HDVs on the motorway.The most important parameters extracted from the simulation are vehicle speed, traffic density on the observed link, and the location of the vehicles on the motorway. Two types of datasets were extracted from the simulation: edge data and vehicle routes data. Traffic parameters for every observed motorway road segment in the edges dataset are collected in 5 min time intervals. For every edge, harmonic vehicle speed and density is computed. The density is computed by using the number of vehicles by an edge. STMs cannot be computed from this dataset because there are no vehicle routes information. Therefore, this dataset is used for testing and validation of the method. The second dataset presents vehicles’ routes recorded and collected in 5 min time intervals. The vehicle route is extracted for every vehicle on the observed motorway containing speed and location parameters. From this dataset, STMs are computed by counting the speed transitions between consecutive motorway segments.The results of the bottleneck probability estimation were compared to measured average mean speed values across every scenario. The comparison was made in the spatiotemporal domain presented in In the first example in The second example in The third example in The fourth example in The first step in the validation process was to create the ground truth dataset to compare it with the proposed method. The main goal was to estimate the bottleneck probability using common traffic parameters like speed and density and find a corresponding threshold for the bottleneck detection. For the validation process, the motorway scenario representing the collision site is chosen a.Critical density rding to , where tthors in ,24,25,26thors in , at thism represents the number of time intervals, and n represents the number of motorway segments.Speed and density were measured on all observed 50 m motorway segments for the creation of the ground truth dataset. The harmonic mean speed and density were collected for each road segment. The exact values of parameters are shown in On the other hand, the binary image containing critical densities d can be x will contain values 0 if a bottleneck is not detected, and 1 if the a bottleneck is detected. The matrix Finally, The next challenge was to adopt the results of the proposed method for the comparison with the ground truth data. As the result of a bottleneck probability estimation is a decimal number in the range With the defined threshold, any value of the bottleneck probability greater than 50% will be considered a detected bottleneck and represented with the value of 1. Bottleneck probabilities for the observed validation scenario are presented in This paper presents a novel motorway bottleneck detection approach based on the STM-based traffic data model. This section aims to describe the method’s application possibilities and address the disadvantages and considerations for using the method. The emphasis of the proposed method is given on the impact of future academic research related to traffic control strategies on urban motorways and the usage of artificial intelligence methods.Alongside the advantages and presented application of the proposed method, some considerations must be addressed. The method is evaluated on a dataset produced with simulated CVs on the motorway. Currently, all vehicles are not connected and integrated into one communication system. Accordingly, the method could be evaluated on a dataset, which contains mixed traffic flow (containing CVs and not connected vehicles) to be applicable to the current motorway scenarios. On the other hand, speed is used as the only traffic parameter for bottleneck detection. Introduction of several more transition matrices containing parameters like density or traffic flow could increase the accuracy and reliability of the method’s result in more versatile motorway traffic scenarios. Final consideration when using STMs is the trade-of between the time interval of data collection for one matrix The STM represents the speed change in one spatial transition observed on the motorway. In the process of recording the speed transition, a spatiotemporal correlation between consecutive road segments in the form of the speed pair is captured. In this way, the speed transition will show the weighted average of the speed pairs rather than a simple averaging technique. The proposed STM-based approach can be compared with the methods that rely on a combination of multiple parameters to get more correct values by combining two or more parameters or sensor values like Kalman filter or Hough transform.Extracted attributes There are two most researched motorway traffic control systems, namely, Variable Speed Limit (VSL)  and RampRecent studies ,30 are pThe authors in  summarizEmerging technologies like CVs present challenges for developing new methods for traffic data analysis, traffic state estimation, and control strategies. Thus, CVs can be mobile sensors and actuators. In this paper, the method for motorway traffic bottlenecks probability estimation is proposed. The method is based on the STM computation and extraction of the represented traffic parameter position as an input to the fuzzy-based algorithm. The result of the method was the bottleneck probability estimated on the simulated motorway traffic scenarios. The method showed comparable results on four different traffic scenarios including traffic incident, recurrent traffic, and the moving bottleneck, with a validation accuracy of over 92%. The proposed method can be used in an environment with high penetration rate of the CVs and as an input to the motorway traffic control algorithms.Future work relates to the implementation of the STM-based bottleneck detection method as an input to classical motorway traffic control systems like VSL. The bottleneck probability and the bottleneck length will be used as a reward function for training the reinforcement learning algorithm for controlling the speed limits at VSL signs. Here, the agent will be rewarded if the bottleneck length decreases or the bottleneck probability drops due to a more efficient control strategy.