Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment. Cloud computing (CC) has shown its strength for bootstrapping genomic data analysis in life science, and has unquestionably influenced earth science ,2. CloudTo this end, many of the efforts made to speed up processing systems for RSBD are from the information technology community (ITC) and geoscience community. Most researchers from ITC focus on scaling RS algorithms with big data processing frameworks (BDPF) in diversified cloud deployments . For example, Sun et al. proposed an optimized framework for RSBD processing in CC . They inOther than the above methods, more and more researchers from the geoscience community are adopting the Google Earth Engine (GEE) cloud platform to target the volume challenge of RSBD. GEE is a public cloud platform specialized for planetary-scale geospatial analysis, which improves the analysis of massive RS data that was not feasible using a desktop processing machine. The surging publications have testified to the rapid adoption of GEE . RecentlIn this study, we are the first to introduce the K8s operator model that explores the elastic computing paradigm in cloud platforms. We propose a framework that integrates K8s with containerized Spark, with consideration of their advantage and disadvantages. K8s is the standard for deploying containerized applications at scale in a multi-cloud environment. K8s operators can be considered to be a control loop for K8s applications to obtain customizable resources without an advanced declaration. Starting from version 2.4.0, Spark can run on K8s clusters in the client model. However, the inconvenience is that manual configurations are required in order to build networking channels between Spark diver (SD) hosts and Spark executor containers (SEC). Spark critical performance parameters should be manually predefined, which is a restriction to the use of elastic computing resources. The memory space and instance number of SEC are unchangeable once the algorithms are submitted. Our framework requires less parameter configuration. The framework only requires the hints on the memory allocated to the SD, and can update the resource configuration of SEC. Because the containerized Spark can be booted in a few seconds , the numThe remainder of this paper is as follows. 2/day). While the spatial and temporal resolutions are both high, the images are expensive. Therefore, a precision agriculture application would require the fusion of these images. State-of-art engines adopt HDFS to store unprecedented volume of RS images [As shown in S images . Spark-bS images . In this section, we propose a cloud-based processing framework. As illustrated in The cloud resource layer is the infrastructure as a service layer. This layer consists of storage, computing, and networking resources. The computing resources are virtual machine instances managed by hypervisors, bare-metal machines (BM), and cloud providers such as Alibaba, Amazon, and Google Cloud. The top layer is a unified cloud platform portal which supports user authentication, service accounting, and image and configuration management. The user authentication service is responsible for multi-tenancy access to cloud computing resources. The service accounting is responsible for security, pricing, and other aspects. The image management service manages image registration on the cloud platforms. The configuration management service supports all service configurations, such as defining monitoring strategies and updating resource quotas of applications.https://github.com/operator-framework (accessed on 23 March 2021)). These long-running storage services rely on network virtualization for communication with each other. Typically, network virtualization functions have different features. For instance, Weave Net creates a virtual network that connects containers across multiple hosts and enables their automatic discovery [In the service-oriented computing layer, we adopt a container-based microservices architecture for easily updating the lossely-coupled service components. Lightweight working components include RS file storage services, the RS object storage service, RS key-value storage services, the Prometheus service, the network function virtualization service, and K8s API services. K8s API is an open-source toolkit that manages the container-based applications in an effective, automated, and scalable manner  provides application management services for the users. After receiving the application specifications from the cloud portal layer, the AM registers the submissions as application objects (AO) in order to manage their life cycle. Application specifications are programmable with Yet Another Markup Language (YAML) files, which specify the types of applications, the container images used to run the applications, the restart policy when a failure occurs, and the specification of storage volumes and computing resources. The AM manages application dependency, retrying failed applications, updating application resource requirements, and scheduling task executions based on predefined policies. Once the application object has been built and the resource requirements have been mapped to the custom resources definitions (CRDs), the AM will create an application controller (AC) for the AO. The AC uses a working queue (WQ) to manage the instances of AOs. The WQ watches all types of events related to these AOs. Once created, the WQ records all of the events of the instance until its completion. In the framework, the Prometheus service would collect the failed or successful events, and would send the status and failed reasons to the WQ. The AC continually checks whether the K8s cluster can match the CRD, and uses a reconciling mechanism to request the K8s to create, update and delete AOs if the K8s cannot match the CRDs. For convenience, we call the procedure as the ‘K8s operator model’.We implement the application management service based on the K8s operator model. The Spark AM accepts the submissions of the user’s Spark applications. Then, Spark AM creates a Spark controller for the construction of a K8s namespace used by the Spark application. The Spark controller is a long-living micro-service. Once added to the working queue of the Spark controller, the Spark controller will create a Spark operator for the Spark application. The Spark controller manages the lifecycle of the Spark operator. The Spark operator watches the pods of the Spark driver and the executors of the application instance. We extend the original Spark operator to collect metrics such as the running counts, the failed counts, and the total number of pods. These metrics will be exports to the Prometheus monitoring service. The Spark controller can use the metrics to schedule the tasks of Spark applications.In order to improve the efficiency of Spark applications on K8s clusters, we propose a task scheduling mechanism (TSM) in this work. The key idea is to dynamically change the task affinities of the Spark application with the computing nodes. This is the only practical solution for Spark on the K8s model. We use the mutating admission webhook (MAW) of K8s to control the Spark pod assignments. MAW intercepts the resources request of the Spark pods and updates the pod specifications before these pods are scheduled to the computing node. The TSM considers the following constraints and conditions.Each computing node has a region label. Typically, the network communication cost in the same region is smaller than the inter-region communication. The Spark executor replicates the blocks of resilient distributed datasets and transmits them to other Spark executors for fault tolerance. Thus, the TSM preferably assigns Spark executor pods to the nodes in the same region. As we know, running too many pods on the same node would cause intense competition among the pods. Therefore, our scheduling algorithm avoids assigning many pods to the same node in order to mitigate the unbalanced workload. Currently, there are many workload prediction algorithms in the cloud. However, the adaptive workload prediction systems are dependent on sophisticated system architecture. Users may have no right to access these systems. Normally, the ratio of the failed counts of the task pods to the successful counts of task pods is a good indicator to reflect the workload characteristics. The newly added computing resources are typically less busy. As mentioned earlier, the Spark AM can update the resource requirement specification, and the Spark controller can therefore change the task pod assignment if this information is used in this context.ij means that hi has j cores, and Memij means that host hi has j megabytes of memory. Assume that the cores and memory requirements of the Spark driver pods (SDP) of the Spark applications are not changeable, and are defined by users. Spark application objects (SAOs) are added to the working queue (WQ), and the WQ will assign the priority (PRI) for each SAO. The spark executor pods (SEPs) execute parallel tasks during their life cycle. Because the parallel tasks are similar in the transformation chain of Spark applications, we assume that each pod executes only one task for discussion convenience. Here, the Core and Mem represent the number of cores and the amount of memory storage used by each SEP, respectively. AFij represents the degree of affinities of the SEPi with the host hj. AFij = 0 means that the i-th pod should not run on the hj. AFij = 1 means that the j-th pod should run on the hj. SEP has an age attribute in terms of the total running time of the pod in a host. The constraints are formally expressed as follows.Based on the above condition, the TSM for the Spark controller should operate under the constraints of the following equations. Suppose there are H hosts in R regions, where each host has several cores, and memory space. Coreµ, the probability of the number of completed SEPs satisfies the Poisson distribution. The larger the probability is, the more stable the host is. Here, the x is the actual number of completed pods; e is approximately equal to 2.718. The goal is to minimize the time cost, given the dynamic nature of the SEPs.We use CP and FP to represent the number of successes and failures of the executed tasks of a SEP, respectively. Once a SEP has finished a batch of tasks successfully, it would continue processing the next batch. Given a period 2) to a value range from 0 to 1. The TSM will rank the affinities and generate the scheduling policy in order to assign each SEP to a computing host. The TSM also dynamically changes the priority of each SAO in the WQ; if the failure rate of an SAO is too large, it will be rescheduled with a higher priority. Finally, the TSM will notify K8s to update the resource requirement and wait a short period.TSM works as follows. First, TSM intercepts the resource specification of Spark applications. Push them to the WQ and initialize a default priority for each SAO. Then, the TSM checks the total CPU core requirement of the SDPs in order to decide whether to request new computing resources from the cloud platforms. The region of an SDP determines the assignment of SEPs of the same SAO. For each SAO, TSM assigns the SEPs to that region with sufficient memory storage. If the memory storage is insufficient, the TSM assigns the SEPs to other regions with small networking communication latency with the SDP. Otherwise, it would traverse the hosts of the region and choose the least recently used computing host. In the reconciling stage, the TSM dequeues the SAO that has the highest priority. Typically, the number of SEP pods is large, while the number of computing hosts is small. Therefore, dynamically changing the affinities of the SEP pods with the computing hosts is beneficial for the improvement of the efficiency of the SAO. The TSM employs a user-friendly strategy to satisfy their expectation. As shown in Now that the framework has been built for Spark applications on cloud platforms with the scheduling mechanism, another question is how to design efficient parallel fusion algorithms for big RS images. Currently, most Spark-based RS algorithms are implemented with low-level resilient distributed datasets (RDDs) . ConsideIn order to solve the problem, we incorporate the Spark DataFrame abstraction for the mapping of RS big images to the resilient distributed tables. The Spark Dataframe supports the window operation on the big tables with user-defined aggregation functions (UDAF) . The winp, the algorithm searches ‘similar pixels’  with the same land cover in the window from the coarse-resolution image at time T0, and leverages the reflectance changes of all of the similar pixels within the window of the coarse-resolution image from times T0 and Tp. As shown in We present the parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) based on the above model by analyzing the procedure of the ESTRAFM and comparing the novities of the model to the strip-oriented programming model (SPM). ESTARFM is the most widely-used fusion algorithm that assumes the spectral reflectance difference of the RS images derived from different satellite sensors capturing the same area in the same day can be modeled by linear functions . SpecifiFirst, the searching of similar neighbor pixels (SSP) is conducted. The input of the algorithm consists of the following five scenes of RS images: the fine-resolution images at times tm and tn, and the coarse-resolution images at tm, tn, and tp. The output of the algorithm is the estimated value for each pixel of the fine-resolution image at tp. In order to distinguish the pixels of the input images, the algorithm uses the center pixel abstraction to represent the pixel in the output image. The SSP identifies the similar pixels for each pixel within a predefined window of the fine-resolution images at times tm and tn, respectively. The output of the SSP is the intersection of the two results to obtain an accurate set of similar pixels. Supposing that we are searching similar pixels within a 5 × 5 window from the two fine-resolution images, the implementation of PESTARFM using SPM requires fives filtering and mapping operations and four joining operations. Second, ESTARFM needs to predict the reflectance changes by calculating the weighted contribution of similar pixels. The contribution of each similar pixel is weighted by the location of the pixel and the spectral similarity between the fine- and coarse-resolution pixels. Intuitively, the higher similarity and smaller distance of the similar pixel to the central pixel has a higher weight. In order to obtain a good representation of the spectral similarity, ESTARFM combines the spectral reflectance of the coarse and the fine resolution images of the two different dates and calculates the correlation coefficient between each similar pixel and its corresponding coarse-resolution pixel of the two images. Therefore, the second stage will require three joining operations to obtain the reflectance changes. In the third stage, ESTARFM calculates the conversion coefficient in order to determine the changing rate for each similar pixel in a search window. This stage is rather similar to the second stage, which also requires three joining operations with SPM. Finally, in order to calculate the reflectance for each center pixel, the final stage requires two joining operations with the SPM. The first operation is to calculate the temporal weight, and the second operation is to calculate the final reflectance from the two estimations according to the temporal weights.https://github.com/huangwei2913/RsImageFusion (accessed on 23 March 2021)). First, the four joining operations map the five images to a distributed table; second, the window operation calculates the standard deviations of the images at Tm and Tn. Typically, this stage is quite fast. The deviations can be easily broadcast to the distributed table. We implemented a “RSImageFusionUDAF” class to manage the window operation. The processing chain that includes searching similar pixels (SSP), calculating the weighted contribution of the similar pixels (CWSP), calculating the conversion coefficient, and estimating the reflectance, can be executed along with the window moving operation. We will check that in the latter sections. For simplicity, PESTARFM means the PESTARFM based on the WPM, if it is not specified in the later sections.While PESTARFM based on the SPM requires nine joining operations, the PESTARFM based on the WPM requires only four joining and one window operation. The PESTARFM source code based on the WPM refers to ). In order to simulate an elastic K8s computing cluster, we used the OpenStack cloud to add on-demand computing resources. As demonstrated in our earlier work, we can use an auto-scaling group (ASG) to easily add VMs. More specifically, when a VM boots up, the init daemon of the VM instance will automatically join the K8s cluster by issuing a kubeadm join command with a predefined token to the endpoint of the K8s API server. We then manually boot a VM instance  that connects to the K8s master node. We used a Heat template to define the scaling policy, which makes the OpenStack cloud provision VM instances when the CPU utilization rate is above 80%, and lasts about 1 min. The minimum, the desired, and the maximum of VM instances were set to 1, 5, and 10, respectively. The flavor of these VM instances was the same as the manually booted VM instance. There are many integrated solutions for managing different cloud resources [https://github.com/fluxcd/flux2 (accessed on 23 March 2021)). Flux uses the kubectl command line interface to change the versions in the CRDs of applications. We used a script to update the version of the Spark-operator image in a short period (10 s in the experiment). Note that we did not change the specification of the Spark driver pods; we only changed the specification of the Spark executor pods without disturbing the execution of the Spark algorithms. By using the approach, we can easily change the replications of the Spark executor pods and the memory space size. Although this approach depends on the external tool to change the computing resources, the approach reduces the cost concerning the rewritten Spark AM. We set the maximum memory space for each SEP to 10 GB in order to consider the K8s master node resource constraints.Because our earlier work demonstrated the strength of Spark to process a massive volume of RS data, the primary goal of this study was to investigate the efficiency of the PESTARFM. All five RS images had 24,886 × 23,655 pixels (each pixel takes four bytes). In order to save some space, we will not introduce the images in the following experiments. We first set up a K8s cluster on top of the four nodes. Because K8s can run on almost all cloud platforms, we used the available computing nodes to simulate a resource-limited computing environment. We built a private local repository on the master node to store the relevant Docker images. We used an NFS client provisioner to ingress the external storage for the containers. We used Flannel to fabricate the container network. The Docker version was 18.06.1-ce, which was installed on every host. We used the Apache Ambari Blueprint (latest version 2.7.5) to construct a Hadoop cluster to store the RS images. Specifically, the containerized Ambari provided shell commands to easily create a containerized Hadoop cluster. All of the RS images were of Geotiff format, and were stored in the Hadoop cluster. The Spark operator consisted of the Spark AM, controller, and operator, and was installed by using Helm charts  to improve their efficiency. We compare the efficiency of PESTARFM using only ASG, with the efficiency of PESTARFM using ASG and TSM, to quantify the performance of the TSM. Typically, the partition number P determines the task size of Spark executors; we set the parameter P to 40 in the experiment in order to partition the data into smaller RDD blocks. As mentioned earlier, users can express the task scheduling expectation by using the weights. We set all of the weights to 0.33 in the experiment. We used the same operation in the former section to remove and construct the computing resources. The parameters DM and EM were set to 8096 MB and 2048 MB. As shown in 1 expresses the expectation to the TSM to dispatch tasks to newer computing hosts, and the weight W2 expresses the expectation to the TSM to dispatch tasks to the least overwhelmed computing host. The last W3 can be considered as the expectation to choose a computing host that has the maximum probability to finish tasks in a given period. We use  to represent the weight set. Because we cannot elaborate all of the subsets, we chose the following four weight sets as , ,  and . The period for the statistical calculation of the probability of the Spark executor pods was set to 10s in the experiment. The Spark parameters were the same as were used in the second experiment. As shown in We conducted the third experiment in order to investigate the impact of different weights of the TSM on the efficiency of PESTARFM. As discussed earlier, the weight WAs it is known, the Hadoop yet another resource negotiator (YARN) has been widely used for cluster resource management, and can also support on-demand containers for BDPFs. We can use a containerized Ambari shell to build a containerized Hadoop cluster. In this section, we investigate the performance of PESTARFM using the containerized Hadoop cluster, and compare the performance with that using the proposed framework. For a fair performance comparison, we removed all OpenStack cloud resources, disabled the TSM, and reserved the K8s cluster before the fourth experiment. We first constructed a containerized Hadoop cluster and uploaded all RS images to the Hadoop cluster. In order to set the maximum memory for the YARN containers to 60 GB, and to restrict the maximum memory of each YARN container to 10 GB, we built a Docker image that used the Hadoop-3.1.2.tar.gz and jdk-8u202-linux-x64 tar.gz. We also packaged the RS fusion class to the Docker image. We used the containerized Ambari to construct the Hadoop cluster with the Docker image. As shown in The Spark driver memory size and Spark executor size were the same as used in the second experiment. Because Hadoop has no such mechanism to scale containers for Spark-based algorithms, we manually ran the following tests to execute the PESTARFM and calculated the averaged time cost of the algorithm. As shown in High-performance computing (HPC) for RS big data on cloud platforms is still a hot topic in the Geoscience community . State-oThere are too many parameters that influence the performance of the PESTARFM. Recently, the authors conducted a comprehensive survey on automatic parameter tuning for big data processing systems . AccordiIn this study, we proposed a lightweight cloud-native framework of RS big data processing. The framework incorporated the Spark-on-K8s operator to improve the efficiency of a parallel RS image fusion algorithm. In the cloud-native computing layer, we adopted a container-based microservice architecture in order to easily update these loss-coupled service components by using K8s. Not only the IaaS scaling mechanism but also the PaaS scaling policy were used in the framework. Moreover, the parallel RS image fusion algorithms, PESTARFM, using Spark SQL was quite efficient for the fusion of RS big images according to our experiment. Additionally, we proposed a task scheduling mechanism to dispatch Spark executor pods to newer and less-overwhelmed computing hosts, thus reducing the time cost of PESTARFM in the framework. According to the experiments, the fusion of five big RS images completes in less than 4 min, while traditional ESTARFM uses multiple hours or even days. We believe that the computing architecture and the parallel RS image fusion algorithm are useful for the Geoscience community. Our future work will investigate factors such as network virtualization technologies, cloud object storage, and other scaling methods to boost the performance of cloud-native applications. Additionally, we will explore other science cloud computing environments to enhance the usability of the proposed framework.


Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%. Wireless Sensor Networks (WSNs) set a very real benchmark for the Internet of Things (IoT) by using the recent technologies to optimize communication and safety systems in hazardous environments ,2,3. ThaThe cost-effectiveness and energy efficiency could still pose great research challenges in the coming years. Moreover, the exponential complexity of the applications creates a technological gap with the precariousness of the amount of energy available in the sensor nodes. As a result, due to the sensor node’s limited computing capabilities, its load is often confined to trivial calculation. Thus, energy savings can be achieved by using a processing unit with variable-speed processors ,8,9. VarA novel WSN energy saving model, called HEEPS, that combines power management and scheduling techniques is developed to schedule real-time tasks. The particularity of HEEPS is its ability to provide a customized energy management algorithm, among time-out 2 DPM and intertask DVFS driven by the GEDF  scheduler, to meet the needs of the application. The implemented power model relies on power states as a methodology for power consumption modeling.A comprehensive review of a variety of saving techniques for WSNs is provided. Each technique target to improve the efficiency of a sensor node such as the energy harvesting and clock gating. Moreover, a discussion to demonstrate the efficiency of the proposed algorithm and to compare it with similar works in the literature is carried out.Specification and verification of time constraints through the GEDF scheduler are given combining two conditions of scheduling to avoid the Dhall effect and non-exploited resources:(a)Condition of Goossens, Funk, and Baruah(b)Condition of Srinivasan and BelkadiEvaluation of the influences of the number of tasks, number of CPU, Worst-Case Execution Time (WCET), Average Execution Time (AET), workload, etc., on saving energy by HEEPS through the STORM simulator. Detailed performance analysis and experimental results are provided showing more energy compared to other existing methods.This paper outlines how the combination of mixed techniques within scheduling algorithms can enrich the current research studies on WSNs and highlight the sometimes ignored insight of these algorithms. However, considering the DVFS and DPM mechanisms for reducing the dissipated power can result in overruns of job deadlines. As a result, the developed Hybrid Energy-Efficient Power manager Scheduling (HEEPS) has integrated a hybrid approach that combines several multi-objective techniques. It relies on task migration techniques, DPM time-out2, where a task is idle for a given time duration switches to the “DeepSleep” state and the DVFS intertask where the speed of each task is fixed and cannot be changed between its different instances. To this end, a global scheduling is deployed through the Earliest Deadline First (EDF) known for its robustness. In addition, we have made some original modifications, which is the combination of Goossens, Funk, and Baruah (GFB)  conditioThe outline of the paper is described as follows. The high energy dependence weighs on the performance of the WSN, so various techniques have been designed to save the energy. Each unit of the sensor node contributes to the power of the node, as shown in In the physical layer, the energy can be minimized by reducing the data rate, packet size, and an efficient throughput and through an optimal energy model . In the Furthermore, in the network layer, novel routing protocols contribute significantly to reduce the energy . ClusterIn the transport layer, the control of congestion and the possibility of its prevention contribute jointly to load-sharing mechanisms to extend the network’s lifetime . TransmiEnergy-saving techniques at the software level include OS-based scheduling, duty cycling, batching, parallel thread processing, and low-power software. Markov Decision Process (MDP) is widely used for resource and power optimization in WSN . It can The data processing unit consists of a memory, microcontroller, and specific operating system. Due to memory limitations, packets can be deleted before the node can send them to the destination node. Connectivity problems, therefore, arise over time, which can lead to a high latency and subsequently low Quality of Service (QoS). As a result, the power cost of the activation of its processing unit during the operations performed or when reading/writing in memory must be optimized .Dargie  studied The system level policy covers the hardware level devices where the power can be changed by either varying some parameters or by varying the operating mode. A compromise between energy reduction and performance is achieved by using several operating modes ranging from high performance, high power to low power, low performance. CPU settings include CPU voltage, frequency and power mode.The undervolting technique was the basis of the work of  where thAuthors in  describeThe paper in  aims to While the frequency (F) is proportional to the voltage (V), the dynamic power is proportional to the square of the voltage. Thus, lowering both  induces a cubical drop in power . This DVThe EA-DVFS algorithm of  delays tIn , DVFS isEnergy efficiency can be maximized by applying techniques. As energy is available, the system must also be able to adapt its consumption, if necessary by shifting the most energy-intensive tasks to more convenient periods, or even by switching the system to a less energy-intensive state when they are idle or at standby or partially used, while limiting the impact on performance .DPM to reduce the static voltage and DVFS to reduce the dynamic power have been applied together in ,37,38. IFor the reason of simplicity, many researchers used off-line techniques . This opThe emerging trend whereby demand for energy is satisfied, is deploying energy harvesting solutions ,41. The The contributions of  focus onThe strong impact of weather conditions, such as changing from night to day or vice versa, limits the performance of the energy harvesting. Typically, the relay node maintains a high QoS throughout the day, but extremely low QoS at night. The problems associated to those techniques range from erroneous hypotheses to unforeseen difficulties and unpredictable environmental dynamics. In conclusion, the non-stability of these systems dependent on climate and on the environment. The importance of wake-up time interval from Many research works have tackled the matter of energy minimization . Some ofThe outcome of this comparison is both DPM and DVFS have a great impact on power saving while respecting the scheduling constraints mainly for real-time multiprocessor systems . In thisTo meet the performance constraints and workload variations, DVFS tends to be combined with scheduling ,57. TherIn Applying several energy management strategies at once provides more energy benefits than settling on one method. Therefore, we propose an online HEEPS power manager that incorporates three energy management strategies. This results from the trade-off between time constraints and power-modes.By providing a functional modeling of a low power energy manager, the need for manual intervention is minimized. The advent of functional and structural failure risks is eliminated through the use of a robust verification methodology. A low consumption system level model is presented that acts on the wireless sensor network both at the local node level and at the global level, i.e., at the network level. The development process is described in This model is built on low-power typical processor characteristics such as frequency, voltage, and timing constraints. The backend design of HEEPS model is illustrated in The on-line characterization allows to predict the mode of consumption and the appropriate voltage/frequency couple at a given moment. The increase in the execution time when tasks are active, through the DVFS, contributes in particular to the minimization of the period of inactivity, allowing more efficient use of low consumption states. DPM is applied during idle intervals while DVFS is applied when the task is running as in Algorithm 1: HEEPS ImplementationAlgorithm 1 gives a formal description of both DPM and DVFS strategies with the simulator STORM when the scheduling constraints are satisfied.The choice of the Actual execution Time (AET) in STORM is made according to the Real Execution Time (AETRule). It can be equal to WECT or random comprised between the BCET, and the WCET selected after each new activation. WCET means “Worst Case Execution Time” and BCET stands for “Best Case Execution Time”.To extend the node lifetime, new utilities are needed to facilitate the development of sensor networks. Pre-prototyping tools, for example, must provide fast and reliable results at a low cost to guide the design or the validation choices of the appropriate hardware. The simulator must not only imitate the real world, but also take into account certain metrics that certainly have significant impacts on network characteristics. These metrics include the time , throughput, power consumption, and quality of service . The simulation models the node’s behavior since they use profiling rules and describe the node as a finite state machine. The feedback received contributes to the energy efficiency upgrade.The study of more than 25 simulation environments in  conducteAlthough the transitions add extra costs and risk tasks missing their execution, they are rarely invested in the research work. Additionally, each power-mode refers to a particular energy and delay, overlooking them brings error, hence, we need a precise modeling and not approximation. For this reason, the multitasking system used promotes the use of scheduling, otherwise it lengthens the ET in case of DVFS. Predetermination of system behavior is an onerous task, which is why we chose the online scheduling.This approach is also important given the evolving nature of energy. Although our simulations are restricted to a periodic model, they can be extended to an aperiodic or sporadic model. The GEDF scheduler is based on a periodic, preemptive task set where the deadline is equal to the period with no task affinity (tasks can change CPU). The GEDF performs both the allocation of tasks to processors and makes scheduling decisions. After each period m is the number of CPU   and Srinah (GFB) :In the Gi and A description of the the simulation metrics is given in To supervise the functionalities of HEEPS, various simulations and measurements are performed. Several parameters are taken into account to measure the power consumption of the CPU.X-axis). Each task is represented by a different color. They are activated simultaneously at m CPU and the m earliest task deadlines ready to be executed on these m processors.Various frequencies and voltages are listed in The variation in the number of tasks has a considerable impact on energy consumption. To do this, we started several simulations by modifying the number of tasks. The primary factor in energy consumption variations is the actual workload. As more tasks are performed, more dynamic latencies are generated and therefore more opportunities for DVFS to regain energy. HEEPS may even result in energy savings more than any stand-alone policy in some cases. Every time the tasks number rises, the power consumption rises since the processors in this case are more solicited. To evaluate the reliability of the process, many tasks are deployed first with an AET equal to the WCET and then with a random value.Interacting at the node level and further upstream at the network level significantly reduces energy. This section assumes the role of highlighting the added value of the developed HEEPS energy manager. Increasing the reliability of the electrical supply contributes to the good functioning of the sensor nodes and to a better quality of service, and stops the network breaking. This fully supports our strategy. Indeed, a one-off power reduction strategy is insufficient to affect significantly a CPU’s power consumption profile (often because of the transition costs). HEEPS crosses over DPM, DVFS, and GEDF to achieve better energy savings.The opportunities offered by HEEPS are numerous. It is able to adapt to all types of processors of sensor nodes and to give results closer to reality. It provides a portable solution on several platforms and simulators by changing some components . After identifying, reviewing, and validating a specific scenario, the solution can be used to explore other instances that fall within the same logic without complicated parameterization. Tracking the node’s behavior would be a way to create a relevant energetic and performance-assured strategy. Therefore, HEEPS is a highly modular system providing utmost scalability and flexibility.Although most power manager are intended for energy harvesting like ,63, theyDVFS alone is not the best strategy to apply to modern processors, but it could be used with other energy management techniques. The DPM algorithm may malfunction if iques of ,37 targeThe comparison of our work with the other proposed solutions allowed us to consolidate our contribution and take into account its limits also in our future work. The energy model is implemented in STORM, encapsulating the power consumption of all operating modes of the CPU component and the transitions between those different power modes. In addition, in order to take advantage of HEEPS, we have developed several simulations based in particular on the variation in the number of tasks, the utilization rate and the best and worst execution time ratio to explore more the reliability of the adopted algorithms. Results show that the power manager proposed effectively schedules tasks to utilize the dynamically available energy estimated gain up to 50%.HEEPS acts on the local and global level when most of the works focuses on the network aspect of the WSNs. Additionally, more power modes are considered compared to other researches. Similarly, the power manager stands out by its hybrid aspect and its combination of several methodologies DPM (time-out2) DVFS (intertask) and GEDF scheduler contributing to more optimality in terms of scheduling. Two conditions of scheduling are combined, which are Goossens, Funk, and Baruah and Srinivasan and Belkadi and non-exploited mainly under the WSNs context. Besides, the STORM simulator is selected to focus more on its many advantages. Furthermore, the STORM features are extended by improving its performances and functionalities. Finally, in the discussion part, HEEPS is compared with recent energy-aware works to highlight the originality of our work. The simulation validates the effectiveness of the results. HEEPS minimizes the total energy consumption ranging from 50% to 80%.


In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work. For the qualification of welding processes in industrial production, both the quality of the weld seams and the process stability are of essential interest. For evaluating these properties, metallographic methods such as longitudinal or lateral cross-sections of the weld seam samples or X-ray imaging methods are commonly used. The test criteria cover a wide range of aspects, including internal defects such as cracks and pores, as well as the weld interface and the weld seam cross-section . To ensuAt present, no test procedure allows the validation and correlation of the test criteria mentioned above based on one evaluation method. As a result, a combination of different examination methods that require a high empirical expertise has to be applied for a comprehensive evaluation of the weld seams regarding geometric characteristics and internal defects. In particular, for the qualification and parameterization of inline process monitoring systems, which allow an evaluation of the weld seam quality during the production process without a subsequent inspection, temporally and spatially correlated reference data are required . TherefoIn the context of this work, an approach based on industrial microfocus X-ray computed tomography (µCT) for the holistic evaluation of weld seam characteristics concerning all geometrically quantifiable properties of weld seams is presented. In addition, a method for the temporal and spatial correlation of the measured properties with the signals of inline process monitoring systems during deep penetration laser beam welding is proposed. This will provide the basis for a significant increase in the accuracy of inline weld seam evaluation and testing. Thus, the way is paved for the use of Deep Learning data processing approaches with a methodology for the acquisition of high-quality and extensive reference data .Several methods have been established for the offline characterization of weld seams, which differ from each other in the type of quality features, i.e., weld seam defects, that can be detected. In the following, methods for the characterization of metallic weld speci-mens are presented in general, whereby, with regard to their use in industrial production, welding techniques of arc welding, high-energy beam welding and solid-state welding are considered in particular. Metallographic analyses as a class of destructive testing methods offer the possibility of analyzing the microstructure, the weld seam geometry and internal defects in sectional planes through the weld seam . Internaultrasonic testing is a common method for industrial inspection purposes. It can be used to detect internal weld seam defects. While many process variants exist, most aim at volumetric integrity testing, which allows the detection of irregularities such as pores [For non-destructive analyses, as pores . Applicaas pores . Ultrasoas pores .X-ray computed tomography (CT) is well established in various industries for the examination of internal properties of structural components that affect the attenuation of X-rays and is thus a method that can be applied to inspect weld seams . The radFor welding in an overlap configuration with a pronounced air gap between the upper and lower joining partners, the cross-sectional diameter of the joint was determined based on CT scans . X-ray CMicrofocus X-ray computed tomography (µCT) allows resolutions with voxel dimensions down to a few micrometers by reconstructing several thousand radiographic images. Thus, defects in railheads could be visualized and, on this basis, a 3D model of crack structures was created . MicrostAll test methods used for weld seam characterization can provide valuable information about individual quality characteristics but are suitable mostly only to a very limited extent for a comprehensive inspection. With existing approaches to weld seam inspection, only individual quality features can be evaluated. Therefore, the choice of the method is strongly determined by the respective target value and is usually only applicable to other weld seam characteristics within a highly constrained range.weld surface inspection can provide information on the process stability and the superficially detectable weld seam formation. A composite sensor system based on active and passive optical sensors was developed to monitor the weld pool in gas metal arc welding. (Passive sensors can be used only to detect radiant energy if the process being observed is emitting radiant energy. Active sensors provide a dedicated energy source for illuminating the observed process.) The obtained information was used to reconstruct the melt pool surface [For process monitoring during welding, a wide range of target values and methods have been investigated. To a large extent, optical methods, which will be considered and discussed with view to different objectives in detail below, are used . The wel surface . A binoc surface . In gene surface .Low-coherence interferometry also allows for a direct weld depth measurement during laser beam welding. It could be demonstrated that the interferometric measuring principle, applied coaxially to the laser beam, enables a measurement of the capillary depth during deep penetration welding . In addiInternal defect detection is possible only to a limited extent during welding to date. Online X-ray observations can provide insights into the formation of process pores during laser beam welding , though Process monitoring systems are designed very specifically and are usually built individually for a particular application and a specific target variable. The acquisition of temporally and spatially correlated, reliable data for process adjustment and quality assurance is a significant challenge.This paper presents an approach that allows to capture and geometrically quantify all properties required to evaluate the quality of a weld seam. For data acquisition, industrial microfocus X-ray computed tomography is used, whereby the measurement parameters are optimized so that internal defects such as pores and cracks can be resolved. In addition, it is possible to detect changes in the metallurgical microstructure inside the component caused by the melting of the material during deep penetration laser beam welding. With the help of the measuring method based on microfocus X-ray computed tomography, it is possible to obtain a three-dimensional image of the entire weld seam that contains information on the weld seam geometry and the position of defects in the weld seam contour and in the component. The distribution and the sizes of pores and cracks within welds, which in this paper were induced during deep penetration laser beam welding, can be obtained from this information. Additionally, information about the weld depth and the weld seam cross-section can be obtained. The relationships between process instabilities and the weld seam geometry can be investigated by applying subsequent data evaluation steps. For the first time, a complete characterization of weld seams is possible using a 3D data set, which enables novel insights into the relationships between process parameters, process stability and the resulting weld seam properties.The geometric measurement data from the weld seams subsequently can be compared with the data from inline process monitoring systems, which are recorded during the production of the evaluated weld samples. As a first step towards this, a procedure to compile temporally and spatially correlated data sets is presented in this paper. The process monitoring data can then be examined for information on weld defects and process instabilities. These temporally highly resolved data sets form the basis for advanced interpretation methods for sensor signals employing Deep Learning approaches.Aluminum alloys pose a comprehensive challenge with respect to weldability. Due to their physical properties, aluminum materials are generally susceptible to weld seam defects and process instabilities. Therefore, reliable quality assurance methods capable of being integrated into an automated processing system are needed to ensure the required characteristics of high-quality welds for the broad and efficient use of aluminum in lightweight constructions. Thus, the focus of this paper is on laser beam welding of aluminum alloys. For the development of the characterization method, a material was selected that allows the detection of microstructural changes caused by the welding process by means of radiographic testing. Five different aluminum alloys, which differed in their composition, heat treatment and manufacturing process, were investigated within the preliminary experiments . The materials were evaluated to determine the extent to which changes in the microstructure caused by welding resulted in a detectable change in the attenuation of the radiation from the µCT, according to the investigations of Schaff et al. . The allA microfocus X-ray computed tomography (µCT) system of the type Xradia Versa 500 from ZEISS was used for the radiographic inspection of the weld seam samples Preliminary experiments: In a preliminary experimental study, laser beam welding tests are carried out on various aluminum alloys without varying the welding parameters. The aim is to produce specimens for preliminary tests with µCT inspection.(b)Qualification of the materials: The µCT scans of the weld seam samples from the preliminary test series are evaluated and assessed with respect to their information content. For further investigations, the alloy AA 2219 is selected, allowing a comprehensive characterization of the geometric weld seam properties.(c)Study on µCT scan parameters: In this step, an experimental study is carried out to evaluate the influence of the measurement parameters in µCT inspection on the quality of the resulting 3D images. The aim is to determine measurement parameters resulting in high contrast and detail quality of the images while maintaining the largest possible measurement volume.(d)3D characterization of weld seams: Initially, weld seam samples are prepared following a full factorial experimental design. The samples are scanned using µCT with the previously defined measurement parameters. Based on the results of the measurements, the influences of the welding process parameters on the formation of the weld seam contour and on the occurrence of internal seam defects are investigated.(e)Analysis and interpretation: In this step, the individual result components are composed. For the first time, signal characteristics could be correlated with the real-time process parameters and weld seam defects, resulting in extensive information on the weld seam geometry. Extensive knowledge about the phenomena during the welding process can be gained from this.(f)Inline process monitoring: The findings gained from the analysis of the weld seams are compared with measurement signals from inline process monitoring systems. For this, the signals are correlated spatially with the µCT images and examined for distinct features. In addition, a method is developed which allows the entire data set to be decomposed into individual temporal elements to create an extensive database as a platform for Deep Learning approaches for the evaluation of measurement signals.In the following, the particular elements of the methodology are explained:In this chapter, the results of the weld seam characterization and the correlation of process monitoring signals with geometric features of the weld seams are presented.In the first step, the influence of the measurement parameters, which can be varied during the acquisition of the µCT images, on the quality of the reconstructed 3D data sets was examined. The resolution, expressed in the voxel size, could be adjusted by moving the sample on the axis between the X-ray tube and the detector in the µCT system cf. . A high vxd), the condition was well satisfied, allowing a precise differentiation between the base material structure and the weld seam structure. At a resolution of 15 µm and coarser, relevant information about the weld seam geometry was lost as the voxel size is on the order of magnitude of the texture variations. Thus, adjacent areas with different gray values overlapped by a voxel, inducing reconstructed voxels to represent an average gray value of the detected microstructure areas.As an additional criterion for selecting a suitable measurement resolution, the detectability of weld seam defects in the 3D scans was evaluated. The size of the pores induced by the welding process was typically on the order of several hundred microns. Cracks, in contrast, were observed to have dimensions on the order of a few microns. Due to the characteristically large-area impairment of the structural cohesion and the resulting weakening of the weld seam strength by cracks, it is essential that this defect class can be detected accurately. Therefore, despite the reduced spatial coverage of the sample, the highest possible resolution was selected. In the series of measurements presented, a voxel size of 7.7 µm proved to be well suited since all essential geometric characteristics were contained in the reconstructed 3D data. Pores mostly represent gas inclusions that remain in a weld seam after the solidification of the melt. In general, process and hydrogen pores can be distinguished. Process pores are caused by dynamic instabilities of the welding process, due to which gas, that cannot rise to the melt pool surface during solidification, is introduced and trapped in the melt. Characteristic for this type of defect are the heterogeneous shape and strongly fluctuating dimensions. Hydrogen pores are caused by hydrogen outgassing during the cooling of the melt during solidification due to a decrease in solubility in the liquid metal. They typically have much smaller dimensions than process pores and are usually spherical. In general, pores are disruptive points in the microstructure of welds and thus lead to a decrease in the strength of joints. Therefore, one of the objectives in the design of welding processes is to avoid porosity by adjusting the process boundary conditions .The 3D characterization method for weld seams presented within this paper allows for the first time to obtain comprehensive information about the relationships between pores and the weld seam geometry. The weld depth is an essential strength-determining feature in the design of welding processes. Particularly in deep penetration laser beam welding, high weld depths of several millimeters can be achieved. Many technical applications require the high stability of the weld depth since fluctuations can lead to damage of the joining partners or an insufficient connection. Destructive metallographic test methods such as transversal and longitudinal cross-sections are usually used to determine the weld depth. The test results are often statistically verified to more deeply consider fluctuations due to process dynamics and disturbing influences. With the help of the presented characterization method, a complete 3D profile of a weld seam can be assessed for the first time. The generated data can be evaluated, for example, with respect to weld depth, which provides a continuous depth profile in the feed direction. Thus, errors, inaccuracies and missing information that cannot be avoided in characterizing the penetration depth by punctually applying metallographic methods can be excluded. With the help of the presented approach, it was possible to characterize welds in AA 2219 samples three-dimensionally and to gain information about the weld seam geometry as well as internal defects in the sample. In addition, a laser welding system was developed that allowed to temporally correlate measurement signals from inline process monitoring sensors with the welding process. The following section discusses how the generated measurement data sets can be spatially correlated with the results of the weld seam characterization. This makes it possible for the first time to obtain completely coherent data sets that can be used to significantly increase the accuracy and information density in the interpretation of process monitoring signals. Machine Learning methods are often used to evaluate signals such as those recorded by process monitoring systems. However, to achieve high prediction accuracies, the methods have to be trained using accurate reference data, which are often difficult to obtain. In particular, the temporal and spatial correlation of samples and data sets is challenging given the inaccuracies of commonly used metallographic methods. In the presented approach, this challenge was mastered by using the measurement signals as a reference. The machine and sensor data were each provided with a time stamp. In order to correlate the µCT images, the welding samples were given a defined notch in the test preparation. During the welding process, this notch led to a characteristic change in the OCT signal of the keyhole depth measurement. On the basis of the measurement signal, the time at which the welding process passes through and exits the notch could thus be determined. This also allowed the time stamp to be assigned to the µCT scans and the data obtained from them. The recorded feed rate could also be used in combination with the data time stamps to calculate the position where the data was measured along the weld seam. This method allows high-quality and precise data sets to be obtained for training Machine Learning methods. The limitation in the temporal resolution is determined only by the frequency of the data recording, which was 1000 Hz for the presented laser welding system. This paves the way for data-driven approaches such as Deep Learning, which can achieve new performance levels using large amounts of training data compared to data processing approaches available for the evaluation of inline process monitoring data during welding so far.An evaluation method based on microfocus X-ray computed tomography (µCT) for the inspection of metallic specimens was developed to acquire a three-dimensional representation of welds, including all geometrically quantifiable specimen-internal features. As a result, extensive information on the weld geometry and internal defects could be obtained from the scans. By building on this, it is possible to reconstruct relationships between process parameters, dynamic fluctuations during the welding process and the weld seam quality utilizing a data set with unprecedented comprehensiveness and accuracy in future studies. The approach for the development of a 3D weld characterization method was divided into three main steps. In the first step, an X-ray-based measurement method was qualified for the acquisition of radiographic raw data. Subsequently, the results were used to design an evaluation routine for the three-dimensional assessment of the geometric properties of welds. Finally, the acquired geometric features were evaluated with respect to their relationship to process parameters and correlated with sensor signals from inline process monitoring systems. Welding tests on aluminum alloys with different microstructures, material compositions and manufacturing processes were carried out to qualify the measurement system. Based on the investigations of Schaff et al. , it was A methodology based on the 3D weld seam characterization results was established for the local and temporal correlation of the geometric weld seam features with the measurement signals from inline process monitoring systems such as an OCT sensor. In order to correlate the signals, a reference notch was added to the weld specimens before conducting the experiments. In the keyhole depth measurement signal of the OCT sensor, the notch yielded a characteristic pattern. Furthermore, due to the complete and temporally correlated closed-loop acquisition of all sensor signals and all axis positions and velocities, the notch, which was detectable in the OCT signal and the µCT images, could be used for referencing. Thus, comprehensive reference data for the weld depth and the characteristic geometric properties of the weld were obtained for the evaluation of sensor signals.The presented measurement method can be used in subsequent investigations to obtain high-resolution and comprehensive measurement data on weld seams. Such data form the basis for the employment of Deep Learning approaches to interpret and evaluate measurement signals from inline process monitoring systems in laser material processing. The procedure and the potentials are illustrated in


In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learning network (DLN), which yields better classification results than conventional methods. In this paper, a novel deep learning method with a hybrid routing network is considered, which can abstract the features of time-domain signals. The used network comprises multiple routing structures and several options for the auxiliary branch, which promotes impressive effects as a result of exchanging the learned features of different branches. The experiment shows that the used network possesses more advantages in the underwater signal classification task. In the complicated and volatile ocean, underwater acoustic target recognition is considered as the most challenging task, and the objective conditions of the marine environment seriously interfere with the recognition accuracy, which mainly includes transmission attenuation, multi-path effects, and ocean environmental noise. Underwater signals contain various forms, such as ships, marine organisms, oceanic turbulence and internal waves, etc. The instruments detect very faint sound waves, and there are various target acoustic features in the collected signals that are quite similar to the auditory system of humans used to recognize different spectrogram characteristics. In external circumstances, objective factors dramatically affect the ability to audibly interpret the signals, and it is difficult to precisely classify acoustic signals even by well-trained people.In underwater sound signal identification, one of the most valuable subject areas is ship-type recognition. Signal classification technologies based on ship features have been gaining significantly increased interest over the years, resulting in a demand for an increase in complexity and precision of the corresponding state-of-the-art algorithms. There are various factors that present a significant obstacle to underwater acoustic signal recognition. The vast majority of methods include two steps: characteristic extraction and a recognition algorithm. There are many types of extraction method, such as the short Fourier transform (STFT) , low freThe extracted characteristic programs that reflect underwater acoustic signals can be improved to some extent, and the losses of raw data are unavoidable in the processing, which leads to high computational complexity. The classifier techniques need to correspond with signal characteristic dimensions and spectrum features, which create difficulties for the designed algorithm. The characteristic selecting modes more or less result in the missing details of the raw signal; this determines how effective underwater acoustic identification algorithms will be for particular sound data. The recognition classifiers span traditional machine learning ,6,7, theRemarkable achievements have been obtained in the deep learning field, such as image vision, natural language processing, and voice identification ,11,12. DThe hybrid routing network structure invests a simplistic format for the complex routing logic network. After several network units are overlaid structurally, the used network can generate multiple routing modes, which enhances the performance of extracted signal features.The network unit is arranged by the different branches. When the main branch remains unchanged, the auxiliary branch adopts three optional orientations, and the classification ability of the used network is furthered by exchanging advanced signal features in different branches. It enriches the extraction categories of signal features.The hybrid routing network was tested on real underwater ship signals. In the experiment, various multi-routing units were confirmed to illustrate the effectiveness of the used network, and there is also a comparative display of the different routing modes and the selection of auxiliary branches.Currently, CNN and RNN are only employed with a direct hierarchical overlay or a simple combined connection between them, which has not been optimized in terms of the network structure design. The underwater acoustic signals are seriously affected by the harsh underwater environment; therefore, it is important to mitigate these factors in the deep network design. The contributions of this paper are as follows:The remainder of this paper is organized as described below. The signal model is given in In I. The fast Fourier transform (FFT) point number is E. In the energy spectrum, the I outputs. The several I output mel-filters are overlapped in time, which generate the mel-scale acoustic spectrum For general machine learning algorithms, the quality of features has a vital impact on generalization performance. The features extracted by the manual design need to be backed up by professional knowledge for each kind of dataset; therefore, it is not easy to design good features manually. DLN isolates the proper features by itself, and there is no demand to design the special features for all sorts of datasets. Through multi-layer processing, DLN gradually converts the initial low-level features into high-level features, and completes the complex classification of the learning task by the trained network. These layers contain the information of the input data and the features flowing from one layer to another. At the upper layer, there are more features defined by the lower layer, which stands for the abstraction space output of each layer. The benefit of DLN comes from the fact that the deep structure is equal to a kind of factorization. Most randomly selected functions cannot actually be conducted, and many of them can be virtually expressed by the deep structure rather than the shallow structure. The existence of the network structure, which can be presented by depth, means that there is some corresponding structure of the potential representable functions to the source dataset. If there is no structure, it is impossible for the trained network model to be well generalized on a similar dataset. One-dimensional convolution is suited to natural language processing , while tThe deep learning network typically molds a large number of stacked network layers to improve the learning ability and achieve higher classification results. The multi-layer network makes it easy to implement the deep network model. When the trained network model is generalized to a similar dataset, some network hyperparameters need to be adjusted, leading to the scalability problem of sparse network structure, which hinders the application in the UTR task. To ameliorate the problem, the balanced structure is considered by the form of the multi-route sparse network structure, which is combined with the short-connection network method —it is eqFor gaining abundant classification features, the most general style of multi-route sparse network architecture is the intricate structure of the sparse fragmented network, which can extract the diversiform signal data; it is a valid model to improve the classification performance, whereas the sparse fragmented architecture has a poor adaptability to the signal dataset, especially in the various number of signals. Specifically, to benefit from the architecture, the new network is created, and named as the hybrid routing network—it further strengthens the ability to acquire the deep relating information of signals, and is a suitable method to handle the various input signal data.The basic composition of the hybrid routing network is shown in j represents the superimposed units, J represents the maximum number of superimposed units. u represents the alternative mode of different auxiliary branches, U represents the total number of auxiliary branches. The j layer can choose any optional auxiliary branch required from 1 to U. The input layer is fed by the original signal data. The gate recurrent unit (GRU), two-dimensional convolution (Conv_1), and MaxPooling pre-process the signal data. There are 512 cells in GRU, and the convolution kernels are pth. Packets are the signal features, and tracks (purple solid wire frame) are the selected range for the features exchange of each branch at a certain percentage. It is provided to the next unit for further learning by rotating and changing a certain proportion of features between different branches, which can avoid the limitations of different branches to pledge the rich signal features. The general convolution operates on all input feature maps, which is the full-track convolution. It is the track dense connection, which means that the convolution is performed on all tracks. In fact, the The different branches exchange the signal features are shown in Xception , MobileNXception  also useThe information contained different branches may be similar in the same packet. If there is no track exchange, the learned features will be very limited. If some tracks are exchanged after different branches, and the learned information can also be exchanged. Each packet has more information and more features can be extracted. To achieve the features of all other packets in each branch, the form is conducive to better results. For this reason, Xception and MobileNet are equipped with the serried The training setting of the used network is a batch size of 128; the optimizer selects the stochastic gradient descent (SGD) with momentum = 0.9, decay = In real shallow sea water, various forms of ships generate the signal dataset. Human disturbance and underwater environmental impacts are contained in the target signal dataset. The ship acoustic data are collected by an hydrophone, which was laid under water at 144 m. The approximate distances range from 50 m to 150 m, and the signal sampling frequency is 32 kHz. The classes of signal recordings are well annotated, which are conveniently applied to automatic identification algorithms. There are approximately 90 samples in each ship target signal type, which range from 9∼682 s in sample duration.X is the different choice of The validing loss of four multi-routing units is larger than that of two multi-routing units and three multi-routing units, and it is due to the fact that the validing dataset is under-fitted. Although the epochs of four multi-routing units are larger than the previously mentioned units, they cannot learn more hidden signal distinguishing features, resulting in larger validating losses. With the increase in multi-routing units, the ability to obtain signal hidden features is further improved, which can effectively reduce validing losses and achieve better classification results. Six multi-routing units and one multi-routing unit are partially coincident in the initial validing process. One multi-routing unit has a small number of layers, and it is impossible to obtain deeper signal classification information, which stops learning after 13 epoches. Six multi-routing units can dig out the deep distinguishing features of signals, and more epoches can effectively improve the classification effect. The training process with six multi-routing units and one multi-routing unit are partially coincident in the initial validating process. One multi-routing unit has a small number of layers, and it is impossible to obtain deeper signal classification information, which stops learning after 13 epochs. Six multi-routing units can dig out the deep distinguishing features of signals, and more epochs can effectively improve the classification effect, which shows that the used network can work effectively to learn the signal data features. The validating process is compared with the training process, and there is an approximate convergence tendency. The validating process is not the smooth course similar to the training process, and it is due to the fact that the probability distribution of the validating dataset is not entirely consistent with those of the training dataset, which can productively fulfill the validation of the trained network. In the validating process, the epoch number is also decreasing as the amount of multi-routing units increases, which is similar to the training process. It shows that the used network can effectively handle the ship acoustic signals by various multi-routing units.In In this paper, we analyzed the ship classification of the hybrid routing network in an ocean sound environment. The particular conditions found underwater make it difficult to achieve a high classification accuracy.The used network with the multiple routing forms and the optional auxiliary exchanging branches contributes to extracting a plenty of signal features and further boost the classification performance. The used network with the multiple routing forms contributes to extracting plenty of deep signal features, which effectively boost classification performance. The optional auxiliary exchanging branches are an effective mechanism in signal feature mining, which enhances the learning ability of the used network.Through the experimental analysis of the used network, we show that the network structure design is one of the best means to improve the recognition effect. The features learned in the used network can also further enhance the classification effect. It is of more practical significance to raise the efficiency of the underwater target recognition system on harsh terms. The used network method can also be extended to the other signal classification scene. In the future, we will study the hybrid routing method for the communication signal classification in an underwater acoustic environment.


The classical cerebrospinal fluid (CSF) circulation theory has been accepted as an established theory of CSF physiology. It describes bulk CSF flow from production site to absorption site. However, much controversy remains regarding the basic CSF physiology and the mechanisms behind the development of hydrocephalus. In the recent observations made using advanced magnetic resonance imaging (MRI) technique, namely, the time spatial inversion pulse (Time-SLIP) method, CSF was used as internal CSF tracer to trace true CSF movement. Observation of the CSF dynamics using this method reveals aspects of CSF dynamics that are different from those of classical CSF circulation theory. Cerebrospinal fluid shows pulsation but does not show bulk flow from production site to absorption site, a theory that was built upon externally injected tracer studies. Observation of the exogeneous tracer studies were true but misinterpreted. Causes of misinterpretations are the differences between results obtained using the true CSF tracer and exogenous tracers. A better understanding of the real CSF physiology can be significant for the advancement of medical sciences in the future. Revisiting CSF flow physiology is a necessary step toward this goal. The dynamics of cerebrospinal fluid (CSF) can be regarded as one of the basic physiological processes of the body. An accurate account of the CSF physiology can provide new insights for understanding human body homeostasis and excretion of waste products from the brain, for developing new concepts of pharmacokinetics effects, and for devising treatments for CSF-related lesions. Unfortunately, the dynamics of CSF can also be the most misunderstood area of human physiology. After Cushing and Weed proposed the classical CSF circulation theory in 1914 , many qu2O17 study  cisternography. Radio-labeled albumin was injected into CSF intrathecally and intraventricularly in a human model . The lab17 study , and O17ed water  using MRIt has been intensively studied that brain capillaries were the site of CSF exchange with the blood -25. In tWith the advent of the Time-SLIP method, it is now possible for the first time to repeatedly trace the real human CSF pulsations without disturbing the normal physiological condition. In particular, the real-time Time-SLIP method provides continuous high-speed imaging of CSF (48 msec/frame), enabling detailed observation of CSF dynamics in its physiological state.Cerebrospinal fluid shows the characteristic movements particularly at locations where the passage is narrow (bottleneck), such as the foramen of Monro and the cerebral aqueduct of Sylvius. Cerebrospinal fluid moves cranially during diastole phase and caudally during systole phase of a cardiac cycle. It is called “to and fro” motion of the CSF. In the foramen of Monro, the CSF from the third ventricle flows along the lateral wall of the lateral ventricles with pulsation to replace the CSF in the lateral ventricle . It can Previous experiments have clearly shown that tracers injected into the CSF in laboratory animals do not follow imagined CSF flow . In the. This puIn the third ventricle (and the fourth ventricle), turbulent flow of CSF is observed . In the In the fourth ventricle, CSF is basically stirred in the same way as in the third ventricle. In this case, the bottlenecks are the cerebral aqueduct of Sylvius and the foramen of Magendie and Luschka. A depiction of the detailed flow movement of CSF in real time has become possible using the MRI Time-SLIP method because the CSF itself is used as a tracer and the achievement of high-speed continuous imaging. Other methods of MRI CSF observation, such as standard phase contrast (PC) MRI, require synchronization with the cardiac pulse. It is then not possible to observe the pulsations-induced mixing, which does not necessarily correlate in phase with that from the regular cardiac cycle.Pulsation of CSF associated with the cardiac beat and the respiration can be observed during surgery or during spinal tap. The net flow has been calculated from the stroke volume by adding and averaging the CSF pulsation in PC MRI. In PC MRI, the movement of CSF due to respiration is assumed negligible. However, it is well known that even for nominal measurement there are large variations of the deduced quantitative values in the individual measurement -35. ObviAfter the MRI Time-SLIP was developed to visualize the movement of CSF due to respiration , furtherIn addition, biphasic movements are sometimes observed in the cerebral aqueduct of Sylvius. 1-40 may diminish due to aging. This experimental method is generally adopted in the experimental model of the glymphatic system (The ISF of the brain and the CSF have the same composition and can freely exchange. Recently, as the glymphatic system , it has It is possible that shaking the head greatly agitates the ISF and CSF, and accelerates the drainage of the waste products from the brain. The resting state indicates a sleeping state, and the state of shaking the head is related to the states of daily living. Both diagnostic imaging and animal experiments were generally observed in a stationary state or under anesthesia. The Time-SLIP images presented here suggest that there can be completely different CSF dynamics in actual daily living. In the sleeping state, only the CSF adjacent to the CVO shows active movement. The waste product in the brain that cannot be absorbed by the local blood-brain barrier carries more into the CSF by stirring during the day. Some of these waste products drain into the lymphatic system from CSF. Indeed, CSF diversion surgery has been attempted to promote drainage of waste products to treat Alzheimer disease ,49. AlthAcute obstruction of the CSF pathway is a clinically very critical situation. The classical CSF bulk flow theory seems to fit well in explaining the cause of acute obstructive hydrocephalus, in which the so-called upstream ventricles expand with obstruction of the ventricular CSF pathway. For example, in the cases where intra-cerebral or intra-ventricular hemorrhage causes acute obstruction of the cerebral aqueduct of Sylvius, emergency drainage is required. In fact, if you are a neurosurgeon chances are that you would have witnessed such a situation and have performed the emergency drainage many times. There is no doubt that this procedure is effective, but the question is whether the cause of ventricular enlargement is the continuous production of CSF from the choroid plexus in the ventricles, which can cause abnormal accumulation upstream of the site of obstruction. If the cause of ventricular enlargement is the continuously generated CSF from the choroid plexus with no outlet, then ventricular enlargement should have been observed in all cases without exception. However, many neurosurgeons have experienced cases where acute hydrocephalus does not occur even with an obstructed cerebral aqueduct of Sylvius . There wIn cat experiments, a cannula was inserted into the cerebral aqueduct of Sylvius from the fourth ventricle. Cannula was kept in physiological level and to collect CSF from the third ventricle. They found not even a single CSF droplet from the cannula ,51,52. IA better understanding of the real CSF physiology can be significant for the advancement of medical sciences in the future. Revisiting CSF flow physiology is a necessary step toward that goal.