Light-weight adaptive task offloading from smartphones to nearby computational resources

Electrical submersible pump (ESP) operation, like many other segments of the oil industry, is recovering from having been drowned in large number of data, the aftermath of information technology revolution, and refocusing on effective utilization of reservoir and production data. This paper presents results of the application of real-time data processing and interpretation in a mature field (Permian basin) through operating and service company alliances. The process involves application of production system efficiency management coupled with newly developed advanced ESP technology and analysis tools-computer simulation software packages that could simulate actual ESP and downhole performance. Data are acquired from the wellsite using data acquisition panels and are sent over a secure network connection to a data hub. The data are also sent for intelligent real-time ESP data processing and interpretation. Well and reservoir monitoring, surveillance, analysis, and diagnosis are also performed. Active collaboration with producing companies at every stage of the process was encouraged through the formation of a new alliance. Finally, various recommendations and suggestions that could operate, optimize the ESP system, reduce well downtime, and prevent catastrophic and premature ESP failures were made. This new approach to ESP operation reduces daily challenges in operating brownfields, such as keeping wells economically viable; solving production problems associated with operating mature assets in today's oil industry that is plagued with continuous declining of manpower resources. As might be expected, lease operating expense (LOE) and/or deferred oil production is reduced. Operational case studies and individual benefits are highlighted and included in this paper. This new approach is a significant departure from the conventional ways of operating ESP where service companies traditionally supply ESPs to producing companies to operate. This new alliance will have a decided influence on future ESP operation and the survival of many brownfields worldwide. Introduction ESP applications as a form of artificial lift method have continued to increase through the years, and significant growth is expected in the next decades as artificial lift requirements increase to a greater extent than current usage. ESP application covers all aspects of hydrocarbon exploitation. Its usage ranges from mature marginal onshore wells to high profile, deep-water subsea wells.

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.

The aim of this research is to find the best scenario to optimize the queuing system so that it can reduce waiting time during customer service at KFC Pontianak. Queue systems are prevalent in public services. The research employs Quality Function Deployment (QFD) and Discrete Event Simulation (DES) methodologies. Alternatives generated are simulated using ARENA Simulation. 70 respondents participated in the KFC Pontianak customer questionnaire, comprising 48.6% females and 51.4% males aged between 18-45 years. The results of the questionnaire showed that 51.4% of respondents were dissatisfied with the speed of service at KFC Pontianak. Customer needs identified through QFD include service speed, order accuracy, ease of use, customer satisfaction, queue comfort, food quality, and payment integration. Analysis reveals three queue system scenarios, two of which propose improvements. The second scenario involves 2 cashiers and 2 self-service cashiers. Self-service allows customers to order by scanning a QR code integrated with the service provider's application. Arrival type employs an exponential distribution with an average of 61 seconds, unlimited max arrival, and first creation with a value of 0. In self-service, there is a value difference of 1 and a standard deviation of 0.2. Simulation results indicate that 51 customers choose cashier 1, 41 choose cashier 2, 39 opt for self-service cashier 1, and 53 prefer self-service cashier 2. The total successfully served customers are 94, with a success rate of 51%. In the third scenario involved 4 self-service cashiers only, the values of 1 and standard deviation of 0.2. The total number of successfully served customers is 173, with a success rate of 80%. In conclusion, this research offers valuable insights for optimizing KFC Pontianak's queue system, addressing customer dissatisfaction with service speed. The proposed scenarios, especially those involving self-service options, demonstrate potential improvements in customer satisfaction and operational efficiency.

The radiation tolerant intelligent memory stack (RTIMS), suitable for both geostationary and low Earth orbit missions, has been developed. The memory module is fully functional and undergoing environmental and radiation characterization. A self-contained "flight-like" module is expected to be completed in 2006. RTIMS provides reconfigurable circuitry and 2 gigabits of error corrected or 1 gigabit of triple redundant digital memory in a small package. RTIMS utilizes circuit stacking of heterogeneous components and radiation shielding technologies. A reprogrammable field programmable gate array (FPGA), six synchronous dynamic random access memories, linear regulator, and the radiation mitigation circuitries are stacked into a module of 42.7mm /spl times/ 42.7mm /spl times/13.00mm. Triple module redundancy, current limiting, configuration scrubbing, and single event function interrupt detection are employed to mitigate radiation effects. The mitigation techniques significantly simplify system design. RTIMS is well suited for deployment in real-time data processing, reconfigurable computing, and memory intensive applications.

Recent advances in mobile technologies have enabled a plethora of new applications. The hardware capabilities of mobile devices, however, are still insufficient for real-time stream data processing (e.g., real-time video stream). In order to process real-time streaming data, most existing applications offload the data and computation to a remote cloud service, such as Apache Storm or Apache Spark Streaming. Offloading streaming data, however, has high costs for users, e.g., significant service fees and battery consumption. To address these challenges, we design, implement and evaluate Mobile Storm, the first stream processing platform for mobile clouds, leveraging clusters of local mobile devices to process real-time stream data. In Mobile Storm, we model the workflow of a real-time stream processing job and decompose it into several tasks so that the job can be executed concurrently and in a distributed manner on multiple mobile devices. Mobile Storm was implemented on Android phones and evaluated extensively through a real-time HD video processing application. The result shows that Mobile Storm effectively processes HD Video Stream in a mobile cloud, which would be impossible on a single mobile device.

Statistical methods are fundamental to designing and executing cancer clinical trials, which present unique challenges such as patient diversity, incomplete data, and ethical considerations. This article underscores the pivotal role of advanced statistical techniques in ensuring reliable outcomes, with a focus on sample size estimation, randomization, and endpoint selection. Key methodologies discussed include Cox regression, Bayesian modeling, and machine learning applications for predictive analytics and real-time data processing. Practical solutions to challenges like treatment effect assessment, bias reduction, and ethical compliance are highlighted through real-world examples. The paper concludes by envisioning future innovations that enhance accuracy, efficiency, and accessibility in cancer research. By integrating rigorous statistical methods with clinical relevance, this work aims to propel oncology into a new era of precision and patient-centered care.

In real-time data-intensive multimedia processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space—namely energy consumption, performance, and chip area. This paper presents an electronic design automation (EDA) methodology for the high-level design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of multidimensional signal processing. Different from the previous works, the problems of data assignment to the memory layers, of mapping the signals into the physical memories, and of banking the on-chip memory are addressed in a consistent way, based on the same formal model. This memory management framework employs techniques specific to the integral polyhedra based dependence analysis. The main design target is the reduction of the static and dynamic energy consumption in the hierarchical memory subsystem.

The problems of design, computer simulation, manufacture of the device sample and its characteristics investigation of the acoustooptical scanner based on surface acoustic waves for 2D data processing in real time are discussed. The method is based on waveguide Bragg and radiation acoustooptic interactions with a synthesized optical aperture using a few optical beams. The total information field is divided into subareas, each of which is irradiated using an individual optical beam controlled by the corresponding waveguide acoustooptic Bragg cell and interaction cell. During the work elaboration of unit contraction, computer simulation, fabrication sample on based Y-cut lithium niobate and its characteristics investigated. This unit makes it possible to process in real time 2600times650 scanning points (information bits) representing two-dimensional arrays

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

The closed-open digital microfluidic (DMF) system offers a versatile and powerful platform for various applications by combining the advantages of both closed and open structures. The current closed-open DMF system faces challenges in scaling up due to electrode structural differences between closed and open regions. Here we developed an adjustable closed-open DMF platform by utilizing the modified slippery liquid-infused porous surfaces (SLIPS) with asymmetric electrowetting on dielectric (AEWOD) as a hydrophobic dielectric layer. The consistent electrode structures of the bottom printed circuit board (PCB) electrode array on both the closed and open regions, and the utilization of a transparent acrylic with floating potential as the top plate allow a low-cost and easily scalable closed-open DMF system to be achieved. The impacts of applied voltage, parallel plate spacing, electrode switching interval, and electrode driving strategies on various droplet manipulations were investigated. The results show that the optimal plate spacings range from 340-510 μm within the closed region. Meanwhile, we also studied the influence of the thickness, geometry, and position of the top plate on the droplet movement at the closed-open boundary. Through force analysis and experimentation, it is found that a thin top plate and a bevel of ∼4° can effectively facilitate the movement of droplets at the boundary. Finally, we successfully achieved protein staining experiments on this platform and developed a customized smartphone application for the accurate detection of protein concentration. This innovative closed-open DMF system provides new possibilities for future applications in real-time biological sample processing and detection.

Recent advances in BazookaSPECT: Real-time data processing and the development of a gamma-ray microscope