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A survey for light field super-resolution

High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Data Trading and Monetization: Challenges and Open Research Directions 7

Data Trading and Monetization: Challenges and Open Research ...

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7th International Conference on Future Networks and Distributed systems, ICFNDS 2023

作者： Ramadan, Qusai Boukhers, Zeyd Alshaikh, Muath Lange, Christoph Jurjens, Jan Institute Of Software Engineering University Of Koblenz Germany Fraunhofer Institute For Applied Information Technology Fit Germany Uniklinik Köln Germany College Of Computing And Informatics Saudi Electronic University Saudi Arabia Rwth Aachen University Germany University Of Koblenz Germany Fraunhofer-Institute For Software And Systems Engineering Isst Germany

ISBN: (纸本)9798400709036

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking. © 2023 Owner/Author.

关键词： Electronic data interchange

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Fuzzy Logic Expert System for Analyzing Student Performance

Fuzzy Logic Expert System for Analyzing Student Performance

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P Aruna Lokhesh R J Shajith Rooban G G Shakthivel K Tarun S R Department of Computing Coimbatore Institute of Technology Coimbatore India MSc Software Systems Coimbatore Institute of Technology Coimbatore India

Assessing the effectiveness of academics at various tiers has grown progressively difficult when employing traditional approaches that predominantly depend on numerical ratings to gauge their teaching and research accomplishments. With the indexing of academic performance in international databases using impact indices at various scales, the demand for advanced computer models has arisen. In this study, we suggest employing expert systems that utilize fuzzy logic to tackle the evaluation of educators, even when confronted with imprecise data and uncertainties. As a novel contribution, this research develops a fuzzy logic model and implements an algorithm using Python and Anaconda, which allows for fuzzy logic rule inference and comprehensive analysis. The implementation of the system is done using Python and leveraging Anaconda's data science environment, providing a seamless and efficient development process. The results of this pilot study involve testing and validating the proposed model through a user-friendly graphical interface, providing comprehensive insights based on minimum criteria along with additional explanations. This approach opens up new possibilities for more accurate assessment of academic performance, particularly when dealing with ambiguous data and uncertain conditions.

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Smart Advertising Based on Customer Preferences and Manage the Supermarket 4

Smart Advertising Based on Customer Preferences and Manage t...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Eishan Dinuka, W.H.A. Wickramasinghe, A.Y.S.W. Weerasinghe, W.S.H. Karunaratne, K.P.G. Liyanapathirana, Chethana Rupasinghe, Lakmal Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users' preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly. © 2022 IEEE.

关键词： Sales

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Emergency Vehicle Navigation in Connected Autonomous systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Subramanian Girivel, Niveditha Mani Shanmuga Sundaram, Varsha Ali Khowaja, Sunder Dev, Kapal Anna University NGNLab Department of Computer Technology Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Software Development Citicorp Services Private Ltd. Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Dublin 9 D09 V209 Ireland Munster Technological University Department of Computer Science Cork 021 T12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Auckland Park2006 South Africa Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India

Autonomous vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite emergency vehicle (EV) travel in the context of connected AVs (CAVs) and ensured secured communication employing public key infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV’s signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and road side units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV’s lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an adaptive smart leader selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

Rare Collision Risk Estimation of Autonomous Vehicles with M...

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International Conference on Intelligent Transportation

作者： Mahdieh Zaker Henk A.P. Blom Sadegh Soudjani Abolfazl Lavaei School of Computing Newcastle University United Kingdom Delft University of Technology The Netherlands Max Planck Institute for Software Systems Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-to-collision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest.

关键词： Monte Carlo methods Noise Decision making Estimation Stochastic processes Switches IP networks Vehicle dynamics Autonomous vehicles Intelligent transportation systems

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

arXiv

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arXiv 2024年

作者： Zaker, Mahdieh Blom, Henk A.P. Soudjani, Sadegh Lavaei, Abolfazl School of Computing Newcastle University United Kingdom Delft University of Technology Netherlands Max Planck Institute For Software Systems Germany

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. In our proposed setting, the situation awareness is considered for one of the ego vehicles by aggregating a range of diverse information gathered from other vehicles into a vector. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS) and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one, while utilizing the time-to-collision measure for decision-making as required. Due to the substantial data requirements of simulation-based methods for the rare collision risk estimation, we leverage a multi-level importance splitting technique, known as interacting particle system-based estimation with fixed assignment splitting (IPS-FAS). This approach allows us to estimate the probability of a rare event by employing a group of interacting particles. Specifically, each particle embodies a system trajectory and engages with others through resampling and branching, focusing computational resources on trajectories with the highest probability of encountering the rare event. The effectiveness of our proposed approach is demonstrated through an extensive simulation of a lane-change scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Split Learning of Multi-Modal Medical Image Classification

Split Learning of Multi-Modal Medical Image Classification

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Artificial Intelligence (CAI), IEEE Conference on

作者： Bishwamittra Ghosh Yuan Wang Huazhu Fu Qingsong Wei Yong Liu Rick Siow Mong Goh Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Max Planck Institute for Software Systems Saarbrücken Germany

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

In the past decades, machine learning (ML) has made significant progress in medical image classification. The success can be attributed to two factors: (i) unique patient data collected and processed by clinics/hospitals and (ii) corresponding ML models solving the underlying classification task. In practice, patient data may contain sensitive information unique to patients’ demography; and ML models often require higher computational resources beyond the affordability of an individual *** practical concerns, we explore a collaborative ML approach in which the data provider, referred to as the client, aims to leverage the computational resources of a server in jointly training a unified ML model without the need to share any raw data. Specifically, we focus on the skin lesion classification problem using a real-world dataset containing multi-modal image inputs and multi-label ground *** enable collaborative yet privacy-preserving skin lesion classification, we develop a learning framework called SplitFusionNet based on u-shape split learning. The key idea of SplitFusionNet is to split the ML model into a (client, server) partition of deep neural network layers: the client layers process multimodal input data and multi-labels, while server layers perform computationally extensive mid-layer computations. Additionally, we apply lossless compression and decompression to improve the communication cost between the client and the server. Experimentally, SplitFusionNet requires less training pipeline time than non-split centralized training while achieving equal predictive performance.

关键词： Training Image coding Computational modeling Pipelines Collaboration Machine learning Data models

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Student Placement Prediction Using Machine Learning Algorithms

Student Placement Prediction Using Machine Learning Algorith...

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Innovations in Power and Advanced computing Technologies (i-PACT)

作者： P Aruna Abinayashri V S Abinayaa Sree K S Meghashri A Department of Computing Coimbatore Institute of Technology Coimbatore India M.Sc. Software Systems Coimbatore Institute of Technology Coimbatore India

The student placement prediction plays a crucial role in modern educational institutions and the job market. To make informed decisions with machine learning algorithms, accurate placement prediction has become achievable. This model focuses on the application of machine learning algorithms for placement prediction, analyzing their effectiveness, and comparing their performance. Educational institutions that facilitate this process can identify areas of improvement in their curriculum and career counseling services, leading to better student outcomes and satisfaction. To save time and resources for companies, it helps optimize their hiring process. This model also focuses on the implementation of machine learning algorithms that can accurately predict whether a student will be placed or not, based on a set of input features such as academic scores, internship experience, activities, and more. This model supports other machine learning algorithms. It provides valuable insights into student employability factors and contributes to fostering a better future for both students and the job market. However, updating the model with fresh data and refinement ensures its accuracy and relevance in an ever-changing job landscape.

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