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Journal articles on the topic 'Data driven frame work'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Gothard, Andrew, Daniel Jones, Andre Green, Michael Torrez, Alessandro Cattaneo, and David Mascareñas. "Digital coded exposure formation of frames from event-based imagery." Neuromorphic Computing and Engineering 2, no. 1 (2022): 014005. http://dx.doi.org/10.1088/2634-4386/ac4917.

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Abstract Event-driven neuromorphic imagers have a number of attractive properties including low-power consumption, high dynamic range, the ability to detect fast events, low memory consumption and low band-width requirements. One of the biggest challenges with using event-driven imagery is that the field of event data processing is still embryonic. In contrast, decades worth of effort have been invested in the analysis of frame-based imagery. Hybrid approaches for applying established frame-based analysis techniques to event-driven imagery have been studied since event-driven imagers came into

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Yu, Siwei, Jianwei Ma, and Stanley Osher. "Monte Carlo data-driven tight frame for seismic data recovery." GEOPHYSICS 81, no. 4 (2016): V327—V340. http://dx.doi.org/10.1190/geo2015-0343.1.

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Seismic data denoising and interpolation are essential preprocessing steps in any seismic data processing chain. Sparse transforms with a fixed basis are often used in these two steps. Recently, we have developed an adaptive learning method, the data-driven tight frame (DDTF) method, for seismic data denoising and interpolation. With its adaptability to seismic data, the DDTF method achieves high-quality recovery. For 2D seismic data, the DDTF method is much more efficient than traditional dictionary learning methods. But for 3D or 5D seismic data, the DDTF method results in a high computation

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Vegesna, Dr Vinod. "AI-Driven Personalized Medicine: A Frame Work for Tailored Cancer Treatment." International Journal of Innovative Research in Advanced Engineering 11, no. 06 (2024): 747–52. http://dx.doi.org/10.26562/ijirae.2024.v1106.06.

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Regardless of the variability of the disease and the individual variances among patients, cancer treatment has historically taken a standardised approach, which frequently leads to less than ideal results. By customising treatment plans to each patient's distinct genetic, phenotypic, and clinical characteristics, AI-driven personalised medicine has the promise of completely changing the way cancer is treated. This study suggests a thorough framework for predicting the best course of treatment for each patient by combining multi-omics data, electronic health records (EHRs), and empirical eviden

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Tumpa, Tasmia Rahman, Shelley N. Acuff, Jens Gregor, Yong Bradley, Yitong Fu, and Dustin R. Osborne. "Data-driven head motion correction for PET using time-of-flight and positron emission particle tracking techniques." PLOS ONE 17, no. 8 (2022): e0272768. http://dx.doi.org/10.1371/journal.pone.0272768.

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Objectives Positron emission tomography (PET) is susceptible to patient movement during a scan. Head motion is a continuing problem for brain PET imaging and diagnostic assessments. Physical head restraints and external motion tracking systems are most commonly used to address to this issue. Data-driven methods offer substantial advantages, such as retroactive processing but typically require manual interaction for robustness. In this work, we introduce a time-of-flight (TOF) weighted positron emission particle tracking (PEPT) algorithm that facilitates fully automated, data-driven head motion

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Howard-Grenville, Jennifer, Andrew J. Nelson, Andrew G. Earle, Julie A. Haack, and Douglas M. Young. "“If Chemists Don’t Do It, Who Is Going To?” Peer-driven Occupational Change and the Emergence of Green Chemistry." Administrative Science Quarterly 62, no. 3 (2017): 524–60. http://dx.doi.org/10.1177/0001839217690530.

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We investigate the emergence and growth of “green chemistry”—an effort by chemists to encourage other chemists to reduce the health, safety, and environmental impacts of chemical products and processes—to explore how occupational members, absent external triggers for change, influence how their peers do their work. Using extensive interviews, archival data, and observations, we find that advocates simultaneously advanced different frames that specified the utility of making the change: (1) a normalizing frame, positioning green chemistry as consistent with mainstream chemistry innovation; (2)

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Thajchayapong, Ploy, and Ashok K. Goel. "Personalized Learning through AI-Driven Data Pipeline." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 111–14. https://doi.org/10.1609/aaaiss.v5i1.35572.

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The integration of artificial intelligence (AI) in education holds significant promise for transforming personalized learning. By analyzing student learning data, AI systems can adapt instruction to meet individual needs through tailored content, adaptive learning paths, real-time feedback, and continuous improvement loops. However, effective personalization at scale demands not only access to large volumes of learner data but also robust data architectures to collect, organize, standardize, and analyze that data in a secure and meaningful way. However, note that the ability of AI to personali

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Nennuri, Rajasekhar, Atmakuri Krishna Chaitanya, and Lakshmi Prasanthi Malyala. "Implementation of data frame work system based on model driven architecture for MAS and Web based applications." International Journal of Engineering & Technology 7, no. 2.20 (2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.20.11730.

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To utilize those Incomprehensible measures from claiming data effectively on the Web to make the data transforming intelligent, customize Furthermore programmed will be those A large portion vital requisitions of the present information mining innovation organization. Model driven Architecture (MDA) which will be utilized for code era need huge numbers profits In conventional product improvement routines. In this paper, Web information mining transform is acquainted from the see for function, a canny mining framework from claiming data is based for joining together those information mining. Th

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Sánchez-Borrego, Ismael, María del Mar Rueda, and Héctor Mullo. "Estimation of Non-Linear Parameters with Data Collected Using Respondent-Driven Sampling." Mathematics 8, no. 8 (2020): 1315. http://dx.doi.org/10.3390/math8081315.

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Respondent-driven sampling (RDS) is a snowball-type sampling method used to survey hidden populations, that is, those that lack a sampling frame. In this work, we consider the problem of regression modeling and association for continuous RDS data. We propose a new sample weight method for estimating non-linear parameters such as the covariance and the correlation coefficient. We also estimate the variances of the proposed estimators. As an illustration, we performed a simulation study and an application to an ethnic example. The proposed estimators are consistent and asymptotically unbiased. W

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Law, James. "Constructional change and frameelement selection." Constructions and Frames 15, no. 1 (2023): 119–44. http://dx.doi.org/10.1075/cf.00069.law.

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Abstract While most work in diachronic construction grammar considers individual constructional changes, these changes share an understudied relationship with broader changes at the level of the semantic frame. Literary corpus data shows that in the Spending frame in French, between the 12th and 20th centuries, the selection rates of four alternating frame elements shifted: Purpose, Cause of expense, Goods, and Seller. This frame-level shift is examined alongside related constructional changes including lexicalization, pejoration, and constructional loss. The analysis suggests that constructio

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Zhang, Meng, Duygu Ceylan, and Niloy J. Mitra. "Motion Guided Deep Dynamic 3D Garments." ACM Transactions on Graphics 41, no. 6 (2022): 1–12. http://dx.doi.org/10.1145/3550454.3555485.

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Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the pre

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Cedersund, Elisabet, Anna Olaison, and Susanne Kvarnström. "Tensions between institutional and professional frames in team talk in gerontological social work." Communication & Medicine 19, no. 3 (2024): 221–40. https://doi.org/10.3138/cam.25959.

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Team meetings are central to social workers’ decision-making practices. These meetings often function as a forum for collegial consultations, when applications are processed and recommendations on decisions are discussed. In this paper, we present findings from a case study on team talk and decision-making practices in gerontological social work. The data come from a body of material gathered within the framework of a larger project covering the process of assessing elder care for older persons in three Swedish municipalities. The case concerns an application, due to homelessness, from a coupl

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Pistellato, Mara, Filippo Bergamasco, Andrea Torsello, et al. "A Physics-Driven CNN Model for Real-Time Sea Waves 3D Reconstruction." Remote Sensing 13, no. 18 (2021): 3780. http://dx.doi.org/10.3390/rs13183780.

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One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to ite

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Ch. Kodanda Ramu, Et al. "Intelligent FMI-Reduct Ensemble Frame Work for Network Intrusion Detection System (NIDS)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1747–60. http://dx.doi.org/10.17762/ijritcc.v11i9.9162.

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The era of computer networks and information systems includes finance, transport, medicine, and education contains a lot of sensitive and confidential data. With the amount of confidential and sensitive data running over network applications are growing vulnerable to a variety of cyber threats. The manual monitoring of network connections and malicious activities is extremely difficult, leading to an increasing concern for malicious attacks on network-related systems. Network intrusion is an increasing issue in the virtual realm of the internet and computer networks that could harm the network

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Chen, Haipeng, Andrew Duncklee, Sushil Jajodia, Rui Liu, Sean Mcnamara, and V. S. Subrahmanian. "PCAM: A Data-driven Probabilistic Cyber-alert Management Framework." ACM Transactions on Internet Technology 22, no. 3 (2022): 1–24. http://dx.doi.org/10.1145/3511101.

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We propose PCAM , a Probabilistic Cyber-Alert Management framework, that enables chief information security officers to better manage cyber-alerts. Workers in Cyber Security Operation Centers usually work in 8- or 12-hour shifts. Before a shift, PCAM analyzes data about all past alerts and true alerts during the shift time-frame to schedule a given set of analysts in accordance with workplace constraints so that the expected number of “uncovered” true alerts (i.e., true alerts not shown to an analyst) is minimized. PCAM achieves this by formulating the problem as a bi-level non-linear optimiza

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Cazzato, Dario, and Flavio Bono. "An Application-Driven Survey on Event-Based Neuromorphic Computer Vision." Information 15, no. 8 (2024): 472. http://dx.doi.org/10.3390/info15080472.

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Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of

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Kostryba, Olha V. "COGNITIVE SEMANTICS OF NOUNS IN DENUMERATIVE WORD FORMATION (BASED ON UKRAINIAN LANGUAGE)." Alfred Nobel University Journal of Philology 1, no. 29 (2025): 328–48. https://doi.org/10.32342/3041-217x-2025-1-29-19.

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Revival of interest in the linguistic-cognitive interpretation of the deep level of language, as a means of facilitating the computerization of the semantic continuum of derived words, is driven by the schemati- zation of both implicit and explicit data concerning these words. The mental interpretation of word-forma- tive semantics, framed by the word-formative nest as a structured framework of knowledge, underscores the significance of this scientific inquiry. The goal of the work is to model natural language through the propositional semantics of derived words, which are expressions of seman

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Lazaar, Nabil. "Les Big Data A L’emergence Des Data-Driven- Business-Models: Une Synthese De La Litterature." European Scientific Journal, ESJ 14, no. 28 (2018): 211. http://dx.doi.org/10.19044/esj.2018.v14n28p211.

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This paper focuses on presenting a first step in a more in-depth literature review. In the border frame of the ‘systemic’ approach (Donnadieu & Karsky, 2002), the study focused on the exploration of ‘Big Data’ value. It follows the process of the formation of the tax decision (Bensouda, 2009) using some properties of the new network apprehension paradigm (Penard, 2014). This is in line with many approaches of identification of the taxable substance of possession and the transmission of ‘value’ (Jemmar, 2010). Also, it is captured exclusively by the ‘shareholder power’ (Vatteville, 2008), u

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Giuliani, Alessandro. "System Science Can Relax the Tension Between Data and Theory." Systems 12, no. 11 (2024): 474. http://dx.doi.org/10.3390/systems12110474.

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The actual hype around machine learning (ML) methods has pushed the old epistemic struggle between data-driven and theory-driven scientific styles well beyond the academic realm. The potential consequences of the widespread adoption of ML in scientific work have fueled a harsh debate between opponents predicting the decay of basic curiosity-driven science and enthusiasts hoping for the advent of a ‘theory-free’ objective science. In this work, I suggest how the system science style of reasoning could drastically de-potentiate this (sometimes deceptive) opposition through the generation of mult

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Miller, Matthew, Louisa Jorm, and Blanca Gallego. "Data-driven identification of urgent surgical procedures for use in trauma outcomes measurement." Trauma Surgery & Acute Care Open 10, no. 2 (2025): e001783. https://doi.org/10.1136/tsaco-2025-001783.

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BackgroundNo standardized list of urgent-trauma-surgery exists for analysis in injury studies. If coded by a standard classification system, such a list could facilitate the standard evaluation and comparison of trauma systems. Solving this problem using Delphi methods or expert opinion incorporating all surgical specialties would be resource-intensive. Instead, we describe a flexible data-driven method for generating a list of urgent surgical procedures from routine administrative data.MethodsWe linked perioperative and inpatient data for trauma patients with procedures booked within 24 hours

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Islam, Tariqul, Md. Hafizul Imran, Md. Ramim Hossain, et al. "Deep Learning Approaches to Predict Future Frames in Videos." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 10, no. 03 (2022): 63–79. http://dx.doi.org/10.3991/ijes.v10i03.33893.

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Deep neural networks are becoming central in several areas of computer vision. While there have been a lot of studies regarding the classification of images and videos, future frame prediction is still a rarely investigated approach, and even some applications could make good use of the knowledge regarding the next frame of an image sequence in pixel-space. Examples include video compression and autonomous agents in robotics that have to act in natural environments. Learning how to forecast the future of an image sequence requires the system to understand and efficiently encode the content and

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Tulunay, Yurdanur, and Ersin Tulunay. "METU Data Driven Forecast Models: From the Window of Space Weather IAU Symposium 335." Proceedings of the International Astronomical Union 13, S335 (2017): 328–30. http://dx.doi.org/10.1017/s1743921318000613.

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AbstractSpace weather processes, in general, are non-linear and time-varying. In such cases ‘data driven models’ such as Neural Network, Fuzzy Logic and Genetic Algorithm based models were proved promising to be used in parallel with the mathematical models based on first physical principles. In particular, with the recent developments in ‘big data’ systems, one of the urgent issues is the development of new signal processing techniques to extract manageable, representative data out of the ‘relevant big data’ to be employed in ‘training’, ‘testing’ and validation phases of model construction.

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Meisina, Bordoni, Lucchelli, et al. "Integrating Satellite Soil Moisture and Rainfall Data on a Data-Driven Model for the Assessment of Shallow Landslides Hazard." Proceedings 30, no. 1 (2019): 42. http://dx.doi.org/10.3390/proceedings2019030042.

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Shallow landslides are very dangerous phenomena, widespread all over the world, which could provoke significant damages to buildings, roads, facilities, cultivations and, sometimes, loss of human lives. It is then necessary assessing the most prone zones in a territory which is particularly susceptible to these phenomena and the frequency of the events, according to the return time of the triggering events, which generally correspond to intense and concentrated rainfalls. Susceptibility and hazard of a territory are usually assessed by means of physically-based models, that quantify the hydrol

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Xu, Zhujun, and Damien Vivet. "Instance Sequence Queries for Video Instance Segmentation with Transformers." Sensors 21, no. 13 (2021): 4507. http://dx.doi.org/10.3390/s21134507.

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Existing methods for video instance segmentation (VIS) mostly rely on two strategies: (1) building a sophisticated post-processing to associate frame level segmentation results and (2) modeling a video clip as a 3D spatial-temporal volume with a limit of resolution and length due to memory constraints. In this work, we propose a frame-to-frame method built upon transformers. We use a set of queries, called instance sequence queries (ISQs), to drive the transformer decoder and produce results at each frame. Each query represents one instance in a video clip. By extending the bipartite matching

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Alemu, Yohannes L., Tom Lahmer, and Christian Walther. "Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure." Eng 5, no. 2 (2024): 629–56. http://dx.doi.org/10.3390/eng5020036.

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Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, th

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Srivastava, Diksha, Sudhanand Prasad Lal, and Gyan Shukla. "Visualizing the Shift: Word Cloud Analysis of Pradhan Mantri Jan Aushadhi Suvidha Sanitary Napkin." Indian Journal of Extension Education 60, no. 3 (2024): 18–22. http://dx.doi.org/10.48165/ijee.2024.60304.

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Word cloud is a graphical representation of knowledge that lets a viewer acquire a quick and intuitive impression of a text. The study was conducted in Muzaffarpur and Samastipur districts of Bihar to find out the frame of mind of 160 respondents regarding menstruation and menstrual-related products before and after the commencement of Jan Aushadhi Suvidha Sanitary Napkin (launched by the Government of India in 2018) through word cloud. Analysis was done for both positive and negative frame of mind. High Price, embarrassment, uncomfortable, social restriction, isolation, dependence, stigma, ta

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Qu, Da Long, and Qiu Lei Du. "The Research of Free Pendulum Flat Control System Based on MCU." Advanced Materials Research 945-949 (June 2014): 2661–64. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2661.

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Free pendulum plate control system controlled by MCU, the system is mainly composed of a swing frame, MCU control module, detection module, motor drive module,display module. Mainly introduces the hardware and software composition of the control system, and the control circuit principle of work, the data acquisition with high accuracy and low range acceleration angle sensor. Free pendulum plate control system using STC12C5A60S2 microcontroller as the main control system, L298N as the motor drive module, STC12C5A60S2 MCU through the L298N drive module drives the PK545-NAC-C29 stepper, making st

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McCoy, Connor O., Trevor Bedford, Vladimir N. Minin, Philip Bradley, Harlan Robins, and Frederick A. Matsen. "Quantifying evolutionary constraints on B-cell affinity maturation." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1676 (2015): 20140244. http://dx.doi.org/10.1098/rstb.2014.0244.

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The antibody repertoire of each individual is continuously updated by the evolutionary process of B-cell receptor (BCR) mutation and selection. It has recently become possible to gain detailed information concerning this process through high-throughput sequencing. Here, we develop modern statistical molecular evolution methods for the analysis of B-cell sequence data, and then apply them to a very deep short-read dataset of BCRs. We find that the substitution process is conserved across individuals but varies significantly across gene segments. We investigate selection on BCRs using a novel me

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Ganorkar, Ashwin Bhimrao, Ramesh R. Lakhe, and Kamalkishor N. Agrawal. "Methodology for application of Maynard Operation Sequence Technique (MOST) for time-driven activity-based costing (TDABC)." International Journal of Productivity and Performance Management 68, no. 1 (2019): 2–25. http://dx.doi.org/10.1108/ijppm-06-2017-0156.

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Purpose The purpose of this paper is to present a procedure to implement time-driven activity-based costing (TDABC) using Maynard Operation Sequence Technique (MOST). In this paper three research questions are addressed: How can MOST be used to frame time equations? How can MOST be used for the improvement of productivity? How can TDABC cost information be used? Design/methodology/approach Case study research was performed at a manufacturing industry. Data have been collected for overhead distribution. The overhead cost was distributed on activity. Time equations are framed using MOST. Cost of

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Curcin, V., A. Barton, M. M. McGilchrist, et al. "Clinical Data Integration Model." Methods of Information in Medicine 54, no. 01 (2015): 16–23. http://dx.doi.org/10.3414/me13-02-0024.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Background: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance.Objectives: TRANSFoRm’s general approach relies on a unified interoperability framework, described in a previous paper. We develope

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Tobyrussell and Octavian Niculita. "A PHM implementation frame work for MASS (Maritime Autonomous Surface Ships) based on RAM (Reliability Availability Maintainability)." PHM Society European Conference 8, no. 1 (2024): 16. http://dx.doi.org/10.36001/phme.2024.v8i1.4073.

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The paper focuses on PHM in the maritime industry, specifically on the maintenance of uncrewed vessels, in contrast to the more commonly discussed navigation. The paper examines the potential challenges of removing the maintenance crew and the potential benefits that can result from this major change in operations.The removal of the primary maintenance team from a vessel necessitates an increase in monitoring and analysis that can be realised by the techniques of PHM. By looking from the perspective of stakeholders, the challenges and opportunities of PHM implementation become clearer. In comp

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Banerjee, Saheli, Alka B. Garg, and Himanshu K. Poswal. "Pressure driven structural phase transition in EuTaO₄: experimental and first principles investigations." Journal of Physics: Condensed Matter 34, no. 13 (2022): 135401. http://dx.doi.org/10.1088/1361-648x/ac484f.

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Abstract In this article we report the synthesis, characterization and high pressure (HP) investigation on technologically important, rare earth orthotantalate, EuTaO4. Single phase polycrystalline sample of EuTaO4 has been synthesized by solid state reaction method adopting monoclinic M′-type fergusonite phase with space group P2/c. Structural and vibrational properties of as synthesized compound are investigated using synchrotron based x-ray powder diffraction, and Raman spectroscopic techniques respectively. Both the techniques show presence of an isostructural, first order, reversible phas

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Sandino, Luis A., Manuel Bejar, Konstantin Kondak, and Anibal Ollero. "Multi-Sensor Data Fusion for a Tethered Unmanned Helicopter Using a Square-Root Unscented Kalman Filter." Unmanned Systems 04, no. 04 (2016): 273–87. http://dx.doi.org/10.1142/s2301385016500114.

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The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. This work addresses the attitude and position estimation of a small-size unmanned helicopter tethered to a moving platform using a multi-sensor data fusion algorithm based on a numerically efficient sigma-point Kalman filter implementation. For that purpose, the state prediction is performed using a kinematic process model driven by measurements of the inertial sensors (accelerometer and gyroscope) onboard the helicopter and the subsequent correction is done using infor

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Mardani, Neda, Mohammadreza Khanarmuei, Kabir Suara, Richard Brown, Adrian McCallum, and Roy C. Sidle. "Lagrangian Data Assimilation for Improving Model Estimates of Velocity Fields and Residual Currents in a Tidal Estuary." Applied Sciences 11, no. 22 (2021): 11006. http://dx.doi.org/10.3390/app112211006.

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Numerical models are associated with uncertainties that can be reduced through data assimilation (DA). Lower costs have driven a recent tendency to use Lagrangian instruments such as drifters and floats to obtain information about water bodies. However, difficulties emerge in their assimilation, since Lagrangian data are set out in a moving frame of reference and are not compatible with the fixed grid locations used in models to predict flow variables. We applied a pseudo-Lagrangian approach using OpenDA, an open-source DA tool to assimilate Lagrangian drifter data into an estuarine hydrodynam

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Inam, Saba, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari, and Nazia Nawaz. "Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction." Sustainability 14, no. 12 (2022): 7317. http://dx.doi.org/10.3390/su14127317.

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Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has

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Kirtac, Kadir, Nizamettin Aydin, Joël L. Lavanchy, et al. "Surgical Phase Recognition: From Public Datasets to Real-World Data." Applied Sciences 12, no. 17 (2022): 8746. http://dx.doi.org/10.3390/app12178746.

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Automated recognition of surgical phases is a prerequisite for computer-assisted analysis of surgeries. The research on phase recognition has been mostly driven by publicly available datasets of laparoscopic cholecystectomy (Lap Chole) videos. Yet, videos observed in real-world settings might contain challenges, such as additional phases and longer videos, which may be missing in curated public datasets. In this work, we study (i) the possible data distribution discrepancy between videos observed in a given medical center and videos from existing public datasets, and (ii) the potential impact

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Song, Hyeonik, Jacob Evans, and Katherine Fu. "An exploration-based approach to computationally supported design-by-analogy using D3." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 4 (2020): 444–57. http://dx.doi.org/10.1017/s0890060420000220.

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AbstractComputational support for design-by-analogy (DbA) is a growing field, as it aids the process for designers looking to draw inspiration from external sources by harnessing the power of data mining and data visualization. This study presents a unique exploration-based approach for the analogical retrieval process using a computational tool called VISION (Visual Interaction tool for Seeking Inspiration based On Nonnegative Matrix Factorization). Leveraging the U.S. patent database as a source of inspiration, VISION enables designers to visualize a patent repository and explore for analogi

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Suganthi, K., M. Arun Kumar, N. Harish, S. HariKrishnan, G. Rajesh, and S. Sofana Reka. "Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability." Sensors 23, no. 7 (2023): 3423. http://dx.doi.org/10.3390/s23073423.

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In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to t

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Liu, Qi, Siyu Liu, Tianning Guan, et al. "Digital Empowerment, Novel Productive Forces, and Regional Green Innovation Efficiency: Causal Inference Based on Spatial Difference-in-Differences and Double Machine Learning Approaches." Information 16, no. 7 (2025): 578. https://doi.org/10.3390/info16070578.

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Amidst the dual challenges of escalating ecological environmental pressures and economic transformation globally, green innovation emerges as a pivotal pathway toward achieving high-quality sustainable development. To elucidate how digitalization and novel productive forces synergistically drive the green transition, the research utilizes panel data from 30 provincial-level administrative regions in China spanning 2009 to 2022, constructing a green innovation efficiency measurement frame-work grounded in the Super Slack-Based Measure (Super-SBM)model, alongside a novel productive forces evalua

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Arora, Payal. "General Data Protection Regulation—A Global Standard? Privacy Futures, Digital Activism, and Surveillance Cultures in the Global South." Surveillance & Society 17, no. 5 (2019): 717–25. http://dx.doi.org/10.24908/ss.v17i5.13307.

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This paper asks the question whether current data regulations designed to curb digital surveillance are enabling for Global South activists seeking to create systemic social change in today’s data-driven societies. This text proposes five measures to answer this question and devise a decolonial pathway to improve the human condition, namely: a) Recognize the long legacy of distrust of the law among activists; b) Channel our energies more on local governance and less on (trans)national regulation; c) Shift focus from the individual to the collective rights approach; d) Attend to motivations for

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Calder, Muffy, Simon Dobson, Michael Fisher, and Julie McCann. "Making Sense of the World: Framing Models for Trustworthy Sensor-Driven Systems." Computers 7, no. 4 (2018): 62. http://dx.doi.org/10.3390/computers7040062.

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Sensor-driven systems provide data and information that facilitate real-time decision-making and autonomous actuation, as well as enable informed policy choices. However, can we be sure that these systems work as expected? Can we model them in a way that captures all the key issues? We define two concepts: frames of reference and frames of function that help us organise models of sensor-based systems and their purpose. Examples from a smart water distribution network illustrate how frames offer a lens through which to organise and balance multiple views of the system. Frames aid communication

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Zhang, L., E. M. Cruda, C.-P. Chao, et al. "A 20 Gbps PAM4 data transmitter ASIC for particle physics experiments." Journal of Instrumentation 17, no. 03 (2022): C03011. http://dx.doi.org/10.1088/1748-0221/17/03/c03011.

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Abstract We present the design and test results of a novel data transmitter ASIC operating up to 20.48 Gbps with 4-level Pulse-Amplitude-Modulation (PAM4) for particle physics experiments. This ASIC, named GBS20, is fabricated in a 65 nm CMOS technology. Two serializers share a 5.12 GHz Phase Locked Loop (PLL) clock. The outputs from the serializers are combined into a PAM4 signal that directly drives a Vertical-Cavity-Surface-Emitting-Laser (VCSEL). The input data channels, each at 1.28 Gbps, are scrambled with an internal 27-1 Pseudo-Random Binary Sequence (PRBS), which also serves as a fram

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Wickramasinghe, G. L. D., and Vathsala Wickramasinghe. "Technical and Vocational Education and Training in Asia and the Pacific –It’s Matter for Economic Performance with the 4th Industrial Revolution." Journal of Economic Analysis 4, no. 1 (2024): 170–91. http://dx.doi.org/10.58567/jea04010009.

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The very nature of work, labor market composition, and skill requirements are severely affected by technology-driven transformations currently underway with the 4th industrial revolution (4IR). The effect of 4IR technologies on technical and vocational education and training (TVET) and the contribution of TVET to the labor market is explicit. The paper presents the findings of a study that investigated whether TVET institutions are prepared to face the challenges of 4IR. The data for the study were collected by surveying 428 teachers from 15 countries in Asia and the Pacific engaged in science

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Barretto, Caroline, Cristian Rincón, Anne-Catherine Portmann, and Catherine Ngom-Bru. "Whole Genome Sequencing Applied to Pathogen Source Tracking in Food Industry: Key Considerations for Robust Bioinformatics Data Analysis and Reliable Results Interpretation." Genes 12, no. 2 (2021): 275. http://dx.doi.org/10.3390/genes12020275.

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Whole genome sequencing (WGS) has arisen as a powerful tool to perform pathogen source tracking in the food industry thanks to several developments in recent years. However, the cost associated to this technology and the degree of expertise required to accurately process and understand the data has limited its adoption at a wider scale. Additionally, the time needed to obtain actionable information is often seen as an impairment for the application and use of the information generated via WGS. Ongoing work towards standardization of wet lab including sequencing protocols, following guidelines

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Jamil, Redouane, Franck Mauconduit, Caroline Le Ster, et al. "Temporal SNR optimization through RF coil combination in fMRI: The more, the better?" PLOS ONE 16, no. 11 (2021): e0259592. http://dx.doi.org/10.1371/journal.pone.0259592.

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For functional MRI with a multi-channel receiver RF coil, images are often reconstructed channel by channel, resulting into multiple images per time frame. The final image to analyze usually is the result of the covariance Sum-of-Squares (covSoS) combination across these channels. Although this reconstruction is quasi-optimal in SNR, it is not necessarily the case in terms of temporal SNR (tSNR) of the time series, which is yet a more relevant metric for fMRI data quality. In this work, we investigated tSNR optimality through voxel-wise RF coil combination and its effects on BOLD sensitivity.

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Kolios, Vasileios, Ioannis Templalexis, Ioannis Lionis, Emmanouil Antonogiannakis, and Petros Kotsiopoulos. "F100-PW-229 Engine Fault Detection Based on Real Time Data." MATEC Web of Conferences 304 (2019): 03006. http://dx.doi.org/10.1051/matecconf/201930403006.

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Gas turbine engines exhibit very high maintenance costs. Moreover, in the case of aero applications an in-flight engine incidence, shall, by all means, be avoided, a condition that drives total maintenance costs even higher. A measure in favor of balancing these costs is to monitor continuously the variation of engine performance data recorded during flight, establish methods to deduce useful information regarding the engine “health” status and, as a result, take appropriate actions to maintain a good engine operating condition. The current work presents such a method tailored on the “F100-PW-

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Cross, Stephanie Behm, Alyssa Hadley Dunn, and Erica K. Dotson. "The intersections of selves and policies: A poetic inquiry into the hydra of teacher education." education policy analysis archives 26 (March 5, 2018): 29. http://dx.doi.org/10.14507/epaa.26.2813.

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This article explores the intersection of selves and policies for teacher educators in an era of teacher education reform. Borne out of a promise to one another to write about our experiences navigating increasingly complex market-driven, neoliberal attacks on our work and world, we collected data across several years that documented our attempt to break our silence (Lorde, 1977) and explore how we, as teacher educators, make sense of neoliberal reforms and policies in teacher preparation. We draw specifically on Dunn’s theory of the Hydra of Teacher Education (2016), alongside literature on r

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Wang, Jihong, Hao Ni, Yiyang Ge, and Shuo Li. "Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks." PLOS ONE 17, no. 9 (2022): e0272505. http://dx.doi.org/10.1371/journal.pone.0272505.

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In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first ti

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Kumar, K. Tulasi Krishna, B. Keerthi, C. SP Vishwak Sen, and T. Karthikeya. "Driver Drowsiness Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1–6. http://dx.doi.org/10.22214/ijraset.2023.50031.

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Abstract: Drowsy driving is one of the major causes of road accidents and death. Hence, detection of driver's fatigue and its indication is an active research area. Most of the conventional methods are either vehicle based, or behavioral based or physiological based. Few methods are intrusive and distract the driver, some require expensive sensors and data handling. Therefore, in my literature survey, a low cost, real time driver's drowsiness detection system is developed with acceptable accuracy. The proposed work mainly focus on a webcam records the video and driver's face is detected in eac

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Dai, Yanyan, and Suk-Gyu Lee. "Perception, Planning and Control for Self-Driving System Based on On-board Sensors." Advances in Mechanical Engineering 12, no. 9 (2020): 168781402095649. http://dx.doi.org/10.1177/1687814020956494.

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The autonomous vehicle can recognize and understand environment, self-control, and achieve the driving level of the human driver. To create this kind of system, the following work was carried out: (a) a real time lane detection system is proposed, based on vision system functions, using webcam camera; (b) In order to detect curve lanes, deep learning is applied for lane detection, based on fully Convolutional Neural Network (CNN); (c) The cubic spline interpolation method is used for path generation, based on Global Positioning System (GPS) data, where distance between two adjacent path genera

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Zhang, Renshan, Su Cao, Kuang Zhao, Huangchao Yu, and Yongyang Hu. "A Hybrid-Driven Optimization Framework for Fixed-Wing UAV Maneuvering Flight Planning." Electronics 10, no. 19 (2021): 2330. http://dx.doi.org/10.3390/electronics10192330.

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Performing autonomous maneuvering flight planning and optimization remains a challenge for unmanned aerial vehicles (UAVs), especially for fixed-wing UAVs due to its high maneuverability and model complexity. A novel hybrid-driven fixed-wing UAV maneuver optimization framework, inspired by apprenticeship learning and nonlinear programing approaches, is proposed in this paper. The work consists of two main aspects: (1) Identifying the model parameters for a certain fixed-wing UAV based on the demonstrated flight data performed by human pilot. Then, the features of the maneuvers can be described

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