77 research outputs found
Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy
No full textSimultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-speed water-immersible scanners, the two consecutively acquired bidirectional PAM images are misaligned with each other because of unstable performance, which causes a non-uniform time interval between scanning points. Therefore, only one unidirectionally acquired image is typically used; consequently, the imaging speed is reduced by half. Here, we demonstrate a scanning framework based on a deep neural network (DNN) to correct misaligned PAM images acquired via bidirectional raster scanning. The proposed method doubles the imaging speed compared to that of conventional methods by aligning nonlinear mismatched cross-sectional B-scan photoacoustic images during bidirectional raster scanning. Our DNN-assisted raster scanning framework can further potentially be applied to other raster scanning-based biomedical imaging tools, such as optical coherence tomography, ultrasound microscopy, and confocal microscopy
Detection of micro inclusions in steel sheets using high-frequency ultrasound speckle analysis
No full textAbstractWith the increasing need for steel sheet quality assurance, the detection of micro-scaled inclusions in steel sheets has become critical. Many techniques have been explored to detect inclusions, e.g., visual inspection, radiography, magnetic testing, and ultrasound. Among these methods, ultrasound (US) is the most commonly used non-destructive testing (NDT) method due to its ease of use and deep penetration depth. However, ultrasound currently cannot be used for detecting the micro-scaled inclusions due to low spatial resolution, e.g., less than 30 μm, which are the key important factors causing the cracks in the high-quality steel sheets. Here, we demonstrate a high-resolution US imaging (USI) using high-frequency US transducers to image micro inclusions in steel sheets. Our system utilizes through-transmission USI and identifies ultrasound scattering produced by the inclusions. We first ultrasonically imaged the artificial flaws induced by the laser on the steel sheet surface for validating the system. We then imaged the real inclusions in the steel sheets formed during manufacturing processes and analyzed them to derive quantitative parameters related to the number of micro-scaled inclusions. Our results confirm that inclusions less than 30 μm can be identified using our high-resolution USI modality and has the potential to be used as an effective tool for quality assurance of the steel sheets.11Ysciescopu
Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments
No full textThe Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption
Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling
No full textAs Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks
Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students
No full textVarious services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services
An edge cloud–based body data sensing architecture for artificial intelligence computation
No full textAs various applications and workloads move to the cloud computing system, traditional approaches of processing sensor data cannot be applied. Specifically, tenants may experience incompatibility and unpredictable performance variation due to inefficient implementations. In this article, we present an edge cloud–based body data sensing architecture for artificial intelligence computation. The main rationale for designing the edge cloud–based sensing architecture is as follows. By analyzing physical body data on the edge cloud computing system, we can identify the relationship between body activities and health conditions for persons. In addition, we can support real-time applications without catastrophic failures by our efficient and stable implementation of the sensing architecture. Our cloud storage architecture is designed to support both stateful and stateless applications, which are compatible with traditional infrastructures and provide server consolidation with a CPU-aware scheduling of virtual machines. Performance results show that our edge cloud–based architecture outperforms the previous architecture in terms of failures, processing time, and scalability
Designing an Efficient Cloud Management Architecture for Sustainable Online Lifelong Education
No full textAs online learning and e-learning are prevalent and widely used in education, it is important to design an efficient and reliable information system for storing learning data and providing on-demand learning services. In this paper, we design a cloud-based information system architecture for online lifelong education. Since a cloud system is based on virtualization technology, we propose a virtual resource management scheme—virtual machine allocation and monitoring nodes assignment. With the proposed cloud-based architecture, we can build and operate an e-learning information system for online lifelong education, which requires efficiency, reliability, and persistence. The evaluation results show that our proposed method can deal with more tasks for e-learning (requests for learning management system (LMS) navigations, text learning contents, text and media learning contents, and video learning contents) while introducing 48× fewer service level agreement (SLA) violations than the existing method
A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments
No full textAs current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers
A Termination Detection Technique Using Gossip in Cloud Computing Environments
No full textPart 11: Cloud & GridInternational audienceTermination detection is a fundamental problem in distributed systems. In previous research, some structures are used (e.g., spanning tree or computational tree) to detect termination. In this work, we present an unstructured termination detection algorithm, which uses a gossip based algorithm to cope with scalability and fault-tolerance issues. This approach allows the algorithm not to maintain structures during runtime due to node joining and leaving. These dynamic behaviors are prevalent in cloud computing environments and little attention has been paid by existing approaches. To measure the complexity of our proposed algorithm, a new metric, self-centered message complexity is used. Our evaluation over scalable settings shows that the unstructured approach can have a significant merit on performance over existing algorithms
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