Real-time Processing Tasks Research Articles

Task offloading method based on CNN-LSTM-attention for cloud–edge–end collaboration system

Most of the existing task offloading methods in edge computing environments do not fully utilize the processing capabilities of cloud servers and have high computational complexity, making them unsuitable for real-time processing tasks. To address this problem, we propose a cloud–edge–end collaborative task offloading method based on deep learning methods, combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and attention mechanisms. This method models the total cost of the cloud–edge–end collaboration system as a weighted sum function of the time delay and energy consumption of task execution. Then, with the objective of minimizing the total cost of the system, the task offloading problem is formulated as a mixed-integer joint optimization problem with offloading decision and bandwidth allocation, and two subproblems are decomposed from the optimization problem: one focuses on offloading decision and the other on bandwidth allocation. A CNN-LSTM-Attention neural network-based method is proposed to solve the optimal offloading decision efficiently. Based on this, the differential evolution algorithm is used to generate the optimal bandwidth allocation, resulting in efficient task offloading. The simulation experiment results demonstrate that our method enhances system performance and reduces the overall cost of the system, which is significantly better than existing methods.

Streaming seismic attributes

Local seismic attributes play an important role in seismic processing and interpretation. However, the iterative regularized inversion required by the calculation of local seismic attributes makes it prohibitively expensive for real-time processing tasks. In this paper, we present an efficient method for estimating local seismic attributes, such as local frequency and local spectrum, using streaming computation. In our approach, the local attributes can be computed by updating the previously calculated attribute value using one new data point at a time, making it unnecessary to conduct the iterative inversion and thus significantly speeding up the computation for online usage. We apply our method to synthetic and field data to demonstrate its efficiency and effectiveness in accurately characterizing nonstationary seismic signals.

Network information security and legal management based on embedded real-time task processing and high-frequency acquisition system

Network information security and legal management based on embedded real-time task processing and high-frequency acquisition system

Memory Allocation Strategy in Edge Programmable Logic Controllers Based on Dynamic Programming and Fixed-Size Allocation

With the explosive growth of data at the edge in the Industrial Internet of Things (IIoT), edge devices are increasingly performing more data processing tasks to alleviate the load on cloud servers. To achieve this goal, Programmable Logic Controllers (PLCs) are gradually transitioning into edge PLCs. However, efficiently executing a large number of computational tasks in memory-limited edge PLCs is a significant challenge. Therefore, there is a need to design an efficient memory allocation strategy for edge PLCs. This paper proposes a dynamic memory allocation strategy for edge PLCs. It adopts an approach of organizing memory into small blocks to handle memory requests from real-time tasks and utilizes a well-performing dynamic programming method for resource allocation problems to handle memory requests from non-real-time tasks. This approach ensures real-time performance while improving the efficiency of non-real-time task processing. In the simulation experiments, the algorithm implemented based on this allocation strategy is compared with the default method and several open-source memory allocators. The experimental results demonstrate that the proposed algorithm, on average, improves the speed of real-time task processing by 13.7% and achieves a maximum speed improvement of 17.0% for non-real-time task processing. The experimental results show that the allocation strategy effectively improves memory allocation efficiency in memory-limited environments.

A Q-Learning-Based Load Balancing Method for Real-Time Task Processing in Edge-Cloud Networks

Edge computing has emerged as a promising solution to reduce communication delays and enhance the performance of real-time applications. However, due to the limited processing power of edge servers, it is challenging to achieve efficient task processing. In this paper, we propose a Q-learning-based load-balancing method that optimizes the distribution of real-time tasks between edge servers and cloud servers to reduce processing time. The proposed method is dynamic and adaptive, taking into account the constantly changing network status and server usage. To evaluate the effectiveness of the proposed load-balancing method, extensive simulations are conducted in an Edge-Cloud network environment. The simulation results demonstrate that the proposed method significantly reduces processing time compared to traditional static load-balancing methods. The Q-learning algorithm enables the load-balancing system to dynamically learn the optimal decision-making strategy to allocate tasks to the most appropriate server. Overall, the proposed Q-learning-based load-balancing method provides a dynamic and efficient solution to balance the workload between edge servers and cloud servers. The proposed method effectively achieves real-time task processing in edge computing environments and can contribute to the development of high-performance edge computing systems.

Few-shot learning for facial expression recognition: a comprehensive survey

Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in improving the convenience in human life. It is widely applied in human–computer interaction tasks. However, recently, FER tasks have encountered certain data and training issues. To address these issues in FER, few-shot learning (FSL) has been researched as a new approach. In this paper, we focus on analyzing FER techniques based on FSL and consider the computational complexity and processing time in these models. FSL has been researched as it can solve the problems of training with few datasets and generalizing in a wild-environmental condition. Based on our analysis, we describe certain existing challenges in the use of FSL in FER systems and suggest research directions to resolve these issues. FER using FSL can be time efficient and reduce the complexity in many other real-time processing tasks and is an important area for further research.

A Survey on Collaborative DNN Inference for Edge Intelligence

With the vigorous development of artificial intelligence (AI), intelligence applications based on deep neural networks (DNNs) have changed people’s lifestyles and production efficiency. However, the large amount of computation and data generated from the network edge becomes the major bottleneck, and the traditional cloud-based computing mode has been unable to meet the requirements of realtime processing tasks. To solve the above problems, by embedding AI model training and inference capabilities into the network edge, edge intelligence (EI) becomes a cutting-edge direction in the field of AI. Furthermore, collaborative DNN inference among the cloud, edge, and end devices provides a promising way to boost EI. Nevertheless, at present, EI oriented collaborative DNN inference is still in its early stage, lacking systematic classification and discussion of existing research efforts. Motivated by it, we have comprehensively investigated recent studies on EI-oriented collaborative DNN inference. In this paper, we first review the background and motivation of EI. Then, we classify four typical collaborative DNN inference paradigms for EI, and analyse their characteristics and key technologies. Finally, we summarize the current challenges of collaborative DNN inference, discuss future development trends and provide future research directions.

Joint task offloading and resource optimization in NOMA-based vehicular edge computing: A game-theoretic DRL approach

Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. This work presents a non-orthogonal multiple access (NOMA) based architecture in VEC, where heterogeneous edge nodes are cooperated for real-time task processing. We derive a vehicle-to-infrastructure (V2I) transmission model by considering both intra-edge and inter-edge interferences and formulate a cooperative resource optimization (CRO) problem by jointly optimizing the task offloading and resource allocation, aiming at maximizing the service ratio. Further, we decompose the CRO into two subproblems, namely, task offloading and resource allocation. In particular, the task offloading subproblem is modeled as an exact potential game (EPG), and a multi-agent distributed distributional deep deterministic policy gradient (MAD4PG) is proposed to achieve the Nash equilibrium. The resource allocation subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed by using a gradient-based iterative method and KKT condition. Finally, we build the simulation model based on real-world vehicular trajectories and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solutions.

Legal Management of Network Information Security Based on Embedded Real-Time Task Processing.

Nowadays, with the continuous advancement of globalization, Internet technology has made an unprecedented leap, which makes the amount of data on the Internet grow exponentially. Data are not only diverse and numerous, but also spread fast and of high value. Therefore, the Internet is entering the era of big data. However, the openness of information network itself, the immaturity of technology, and the imperfection of network security law make it vulnerable to network attacks, resulting in the disclosure of network users' information and the theft of their privacy data. The formulation of network information security law to protect network information security is of great significance for the development of multidomain network information security under the background of big data and ensuring users' online security. Firstly, this article introduces the basic information of embedded real-time task system, analyzes the factors of real-time Linux based on kernel preemption, interrupts mechanism and policy planning, and summarizes the process of some embedded real-time task processing. Integrating legal management, this article puts forward three elements of network security control mechanism, namely, network control personnel, environment, and technology. Taking these three elements as the starting point of the construction of information security control mechanism, this article provides the steps of using evaluation model to evaluate network information security. Finally, it identifies the problems existing in the legal management of network information security and formulates the corresponding strategies. By combining embedded technology and real-time task processing technology, this study applies it to the field of network information security legal management, to promote its development.

A hardware and software system for MRI applications requiring external device data.

Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline. The hardware includes a set of external devices-electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc.-using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications. The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction. The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

WITHDRAWN: Microprocessor and Real-Time Task Processing System Application in University Innovation and Entrepreneurship Education Platform

Abstract Entrepreneurship has always been centered on predicting the future, and it has been seen from a corporate management perspective. The world within it is seen as linear teaching of known inputs and outputs. In the entrepreneurial world, a very uncertain environment is usually completely different and requires a skillset type. This article describes how entrepreneurial learning is a concept, and curriculum is developed, designed and designed through a methodology and design-based approach. Some people believe that design ideas and methods can be achieved from the “traverse” learning methods in this work. This kind of learning emphasizes the role of more skills and thoughts and focuses on entrepreneurship. This learning method is student-centric and focuses on better skills for entrepreneurs. It also think that entrepreneurship is not linear, so it is an unstructured process that is a discovery structure centered on creativity. Design philosophy emphasizes a practical way to get students out of the classroom. This is important in combination with real-world experimentation in reflex exercises where users and customers provide real-time feedback. This article emphasizes approaching the “tongs” perspective that can be achieved through entrepreneurial education and design thinking. This is reasonably clarified and shown with models. This accepts that strategies for investigating instructive business are helpful, and configuration thinking can be an important apparatus and technique for educating business ventures.

Task Offloading and Serving Handover of Vehicular Edge Computing Networks Based on Trajectory Prediction

Vehicular edge computing (VEC) has emerged as a promising paradigm to ensure the real-time task processing caused by the emerging 5G or high level intelligent assisted driving applications. The computing tasks can be processed via the edge services deployed as the roadside units (RSUs) or moving vehicles. However, the high dynamic topology of the vehicular communication system and the time-varying available computing resources in RSUs make a challenge of the efficient task offloading of vehicles. In this paper, we consider an efficient task offloading scheme for VEC networks based on trajectory prediction, we focus on the serving handover between the adjacent RSUs. The moving vehicles can cooperate with RSUs or the surrounding vehicles for task processing. To reduce the latency of task transmission between vehicles, we present a cooperative vehicle selection method based on trajectory prediction. Then, we propose an efficient task offloading scheme based on deep reinforcement learning (DRL), while the dynamically available computing and communication resources are considered jointly. The simulation results show that the proposed task offloading scheme has great advantages in improving the utility of vehicles.

Real-time task processing for spinning cyber-physical production systems based on edge computing

With a high-speed, dynamic and continuous yarn manufacturing process, spinning production suffers from different problems of dynamic disturbances such as yarn breakage, machine breakdown, and yarn quality. Processing real-time tasks is critical for tackling these problems, except for satisfying the requirements of mass production. The existing spinning cyber-physical production systems (CPPS), however, rely on a cloud center for centralized processing of real-time tasks. Thus, it becomes increasingly difficult for them to meet real-time requirements. As such, this paper proposes a novel real-time task processing method for spinning CPPS based on edge computing. First, a new hybrid structure of edge computing nodes (ECN) that consists of both 1-1 and N-1 modes is introduced for different types of tasks in spinning CPPS such as fixed tasks, decision-intensive tasks, and data-intensive tasks. Second, a collaboration mechanism is developed for collaborations between ECNs. The mathematical model and algorithms for real-time task processing are provided for a single ECN. Finally, a case study on a real spinning production is conducted. The results of this case study have demonstrated that the proposed method can significantly reduce the processing time of real-time tasks, as well as improve the production flexibility and production efficiency in spinning CPPS. The proposed method could be applied to continuous and batch manufacturing fields with high real-time requirements, such as weaving, chemical fiber production, and the pharmaceutical industry.

Energy-Efficient Edge Offloading in Heterogeneous Industrial IoT Networks for Factory of Future

The ultra-reliable and low latency communication (URLLC) and massive machine type communication (mMTC) in 5G are envisioned to support intelligent automation in the heterogeneous Factory of Future (FoF) networks, and Mobile-edge computing (MEC) is considered to be a promising system for enabling real-time task processing at the edge of the network. In the future factory, production machines, and environmental monitoring devices will be endowed with the wireless connecting for mobility. These devices are deployed for running complicated real-time tasks. To make such mission-critical tasks being processed in time, parts of the tasks should be completed with the assistance of the edge server or even the cloud. In this work, we jointly investigate the partial task offloading, computation, and communication (licensed and unlicensed) resource allocation problem in the trade-off between overall power consumption and quality of service (QoS) satisfaction. A 2-tier MEC-cloud framework is provided, wherein the IoT mobile devices (MDs) are able to partition the tasks into segments and offload them to the MEC and the cloud server. Considering the limits of communication and computation resources, we proposed a mechanism call 5G and NR-U opportunity-cost-based offloading algorithm (5G/NR-U OCBOA) to optimize resource allocation. Within the mechanism, there are two proposed algorithms, 5G OCBOA is for the licensed-only case, and NR-U OCBOA dedicates on unlicensed one. We iteratively perform the two algorithms to get the final solution. The simulation results show that our low-complexity algorithms almost outperform the other benchmark greedy algorithms. The proposed algorithm is up to 59.3% MD blocking probability less, up to 58.7% power saving gain, and up to 47.6% more QoS gain.

Design and Implementation of a Real-time Publish/Subscribe Middleware

Background:Middleware has been widely adopted as an efficient approach to facilitate the development process of large-scale distributed systems. Although this approach can achieve simplified system design and reduced time consumption, it has a strong demand for real-time task processing and data transmitting. Traditional middleware, which lacks the real-time performance guarantee mechanism, cannot be adopted by the distributed systems with strict time restrictions.Methods:In order to meet the real-time requirements of distributed systems, the functional requirements of middleware are studied, and a real-time publish/subscribe middleware is designed and implemented in this paper. A hierarchical design is adopted to build this middleware from a model layer, a communication layer and a support layer. The middleware has the data synchronization on distributed objects, Ethernet-based publish/subscribe mechanism and the real-time performance guarantee mechanism.Results:Experiments show that the proposed middleware satisfies the needs of distributed applications, and ensures that the end-to-end data communication delay is less than 1 millisecond, which meets the real-time requirements of data transmission in distributed systems.Conclusion:In this paper, we design and implement a real-time middleware based on the publish/ subscribe mechanism.

Real-time task processing method based on edge computing for spinning CPS

Spinning production is a typical continuous manufacturing process characterized by high speed and uncertain dynamics. Each manufacturing unit in spinning production produces various real-time tasks, which may affect production efficiency and yarn quality if not processed in time. This paper presents an edge computing-based method that is different from traditional centralized cloud computation because its decentralization characteristics meet the high-speed and high-response requirements of yarn production. Edge computing nodes, real-time tasks, and edge computing resources are defined. A system model is established, and a real-time task processing method is proposed for the edge computing scenario. Experimental results indicate that the proposed real-time task processing method based on edge computing can effectively solve the delay problem of real-time task processing in spinning cyber-physical systems, save bandwidth, and enhance the security of task transmission.

ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation.

This paper presents a novel and practical convolutional neural network architecture to implement semantic segmentation for side scan sonar (SSS) image. As a widely used sensor for marine survey, SSS provides higher-resolution images of the seafloor and underwater target. However, for a large number of background pixels in SSS image, the imbalance classification remains an issue. What is more, the SSS images contain undesirable speckle noise and intensity inhomogeneity. We define and detail a network and training strategy that tackle these three important issues for SSS images segmentation. Our proposed method performs image-to-image prediction by leveraging fully convolutional neural networks and deeply-supervised nets. The architecture consists of an encoder network to capture context, a corresponding decoder network to restore full input-size resolution feature maps from low-resolution ones for pixel-wise classification and a single stream deep neural network with multiple side-outputs to optimize edge segmentation. We performed prediction time of our network on our dataset, implemented on a NVIDIA Jetson AGX Xavier, and compared it to other similar semantic segmentation networks. The experimental results show that the presented method for SSS image segmentation brings obvious advantages, and is applicable for real-time processing tasks.

Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification.

The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification. All the targets needed to be retrieved may appear only once, and there is no knowledge of target label for averaging. More interestingly, how the features contribute temporally and spatially to single-trial ERP detection has not been fully investigated. In this paper, we propose to implement a local-learning-based (LLB) feature extraction method to investigate the importance of spatial-temporal components of ERP in a task of rapid face identification using single-trial detection. Comparing to previous methods, LLB method preserves the nonlinear structure of EEG signal distribution, and analyze the importance of original spatial-temporal components via optimization in feature space. As a data-driven methods, the weighting of the spatial-temporal component does not depend on the ERP detection method. The importance weights are optimized by making the targets more different from non-targets in feature space, and regularization penalty is introduced in optimization for sparse weights. This spatial-temporal feature extraction method is evaluated on the EEG data of 15 participants in performing a face identification task using rapid serial visual presentation paradigm. Comparing with other methods, the proposed spatial-temporal analysis method uses sparser (only 10% of the total) features, and could achieve comparable performance (98%) of single-trial ERP detection as the whole features across different detection methods. The interesting finding is that the N250 is the earliest temporal component that contributes to single-trial ERP detection in face identification. And the importance of N250 components is more laterally distributed toward the left hemisphere. We show that using only the left N250 component over-performs the right N250 in the face identification task using single-trial ERP detection. The finding is also important in building a fast and efficient (fewer electrodes) BCI system for rapid face identification.

Accelerate bilateral filter using Hermite polynomials

The bilateral filter (BF) as an edge-preserving lowpass filter is a valuable tool in various image processing tasks, including noise reduction and dynamic range compression. However, its computational cost is too high to apply in the real-time processing tasks as the range kernel, which acts on the pixel intensities, making the averaging process nonlinear and computationally intensive, particularly when the spatial filter is large. Using the well-known Hermite polynomials, a BF accelerating method is proposed, which reduces the computational complexity from O(r2n) to O(n), where r denotes the filter size of a BF and n is the total number of pixels in an image.

Collaborative sensor networking towards real-time acoustical beamforming in free-space and limited reverberance

Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.