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Optimizing deep learning architectures for novel view synthesis: Investigating the impact of NeRF MLP parameters on complex scenes

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International Journal of Information technology (Singapore) 2024年第4期16卷 2295-2305页

作者： Singla, Kanika Nand, Parma Department of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida India

Machine learning combined with geometric reasoning is a promising approach for generating new perspectives of a scene using limited image captures, known as neural rendering techniques. Neural radiance fields (NeRF) represent complex 3D scenes by learning the radiance implicit function and have shown impressive performance in various applications. However, the influence of NeRF's parameters on its overall performance remains largely unexplored. In this paper, we conduct an extensive grid search on NeRF's multi-layer perceptron (MLP) parameters to examine their impact on image quality. We analyze the results by varying parameters related to the MLP architecture, such as hidden layer count and neurons per layer. Our findings reveal that the choice of MLP architecture significantly affects image quality. Our proposed approach outperforms NeRF's default settings on challenging scenes, including synthetic and real-world datasets. These results shed light on the impact of different parameters on NeRF's performance, assisting practitioners in selecting suitable settings for their specific applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Neural radiance fields (NeRF) Neural rendering Novel view synthesis Peak Signal to Noise Ratio (PSNR)

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supported by the National Natural Science Foundation of China(Nos.62172051,61772085,and 61877005)and Jiangsu Agriculture Science and technology Innovation Fund(***(18)3054).

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Tsinghua Science and technology 2023年第1期28卷 82-92页

作者： Jinghui Zhang Yuchen Wang Tianyu Huang Fang Dong Wei Zhao Dian Shen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Hefei Comprehensive National Science Center Hefei 231299China School of Computer Science and Technology Anhui University of TechnologyHefei 230026China Department of Computer Science&Engineering The Chinese University of Hong KongHong Kong 999077China

Numerous neural network(NN)applications are now being deployed to mobile *** applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile ***,devices’life and performance depend on ***,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady *** this paper,we propose a thermal-aware channel-wise heterogeneous NN inference *** contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)*** on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous *** experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.

关键词： neural network inference mobile device temperature adjustment channel-wise parallelization

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Fast and flexible stack‐based inverse tone mapping

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1444-1454页

作者： Ning Zhang Yuyao Ye Yang Zhao Xufeng Li Ronggang Wang School of Electronic and Computer Engineering Peking University Shenzhen Graduate SchoolShenzhenChina School of Computer and Information Hefei University of TechnologyHefeiChina Department of Computer Science(CS) College of Engineering(EG)City University of Hong KongHong KongChina

Inverse tone mapping technique is widely used to restore the lost textures from a single low dynamic range ***,many stack‐based deep inverse tone mapping networks have achieved impressive results by estimating a set of multi‐exposure images from a single low dynamic range ***,there are still some *** the one hand,these methods usually set a fixed length for the estimated multi‐exposure stack,which may introduce computational redundancy or cause inaccurate *** the other hand,they neglect that the difficulties of estimating each exposure value are different and use the identical model to increase or decrease exposure *** solve these problems,the authors design an exposure decision network to adaptively determine the number of times the exposure of low dynamic range input should be increased or ***,the authors decouple the increasing/decreasing process into two sub‐modules,exposure adjustment and optional detail recovery,based on the characteristics of different variations of exposure *** these improvements,this method can fast and flexibly estimate the multi‐exposure stack from a single low dynamic range *** on several datasets demonstrate the advantages of the proposed method compared to state‐of‐the‐art inverse tone mapping methods.

关键词： 2‐D image enhancement image processing

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Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

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computers, Materials & Continua 2023年第10期77卷 1209-1226页

作者： P.Kalaiselvi S.Anusuya Department of Computer Science and Engineering Saveetha School of EngineeringChennaiTamil Nadu602117India Information Technology Saveetha School of EngineeringChennaiTamil Nadu602117India

In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so ***,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific *** tumor prediction is a vital chore in analyzing and treating liver *** paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver *** anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver *** proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art ***,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver *** results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor *** can assist radiologists in their diagnosis and treatment *** proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.

关键词： Deep learning convolution neural networks liver tumors CT scans attention mechanism classifier

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Rotation,Translation and Scale Invariant Sign Word Recognition Using Deep Learning

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computer Systems Science & engineering 2023年第3期44卷 2521-2536页

作者： Abu Saleh Musa Miah Jungpil Shin Md.Al Mehedi Hasan Md Abdur Rahim Yuichi Okuyama School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science and Engineering Pabna University of Science and TechnologyPabnaBangladesh

Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious *** of the main functions of sign language is to communicate with each other through hand *** of hand gestures has become an important challenge for the recognition of sign *** are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance *** resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word ***,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version *** we have applied the gesture segmentation *** segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed ***,our designed CNN model has been trained to classify the hand gesture as well as the sign *** model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these *** achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset ***,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.

关键词： Sign word recognition convolution neural network(cnn) rotation translation and scaling(rts) otsu segmentation

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Energy Harvesting Jammer-Aided Covert Communications in Wireless Multi-Relay IoT Systems

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IEEE Internet of Things Journal 2025年第11期12卷 17443-17455页

作者： Lv, Hao Yang, Bin Sun, Xiuwen Gao, Chan Gui, Bao Taleb, Tarik School of Computer Science and Technology of Anhui University Hefei230000 China Chuzhou University School of Computer and Information Engineering Chuzhou239000 China Xi'an University of Posts and Telecommunications National Engineering Research Center for Secured Wireless School of Cybersecurity Xi'an710121 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum44801 Germany

This paper investigates covert communications in a multi-relay Internet of Things (IoT) system with multiple energy harvesting jammers, where a transmitter (Alice) attempts to covertly transmit confidential messages to its destination (Bob) through relay forwarding, while a warden (Willie) detects the existence of Alice's transmission. Specifically, we employ a harvesting-then-jamming protocol with which the jammers first harvest energy from Alice and then send jamming signals to interfere with Willie's detection. We propose a relay and jammer selection strategy, namely quality of service (QoS)-aware selection, and use the random selection strategy as a comparison strategy. Under these two selection strategies, we derive the optimal detection threshold and minimum detection error probability at Willie, respectively. We then model the covert throughput performance and obtain the maximum covert throughput by jointly optimizing covert transmit power and jamming transmit power. Extensive numerical results are provided to illustrate the impacts of system parameters on covert throughput performance. © 2014 IEEE.

关键词： Jamming

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(Information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Efficient Docker-Based Resource Management for CPU-Intensive Tasks in Edge-Cloud Computing

Efficient Docker-Based Resource Management for CPU-Intensive...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Wang, Wei Zhang, Yongmin She, Liang Liu, Jiagang Jing, Yao Woldeyes, Surafel Kifetew School of Computer Science and Engineering Central South University ChangSha China School of Computer Science and Engineering Hunan University of Technology and Business ChangSha China School of Computer Science and Engineering Hunan Institute of Technology HengYang China

ISBN: (纸本)9798331507886

Edge-cloud computing integrating the advantages of edge and cloud to provide high-quality services for mobile users is one of the hotspots recently. However, the increasing number of applications brings various tasks with different resource requirements, potentially leading to poor computing performance in Edge-cloud systems with fixed resource configurations, especially for tasks that are heavily reliant on CPU resources. To enhance computing performance, we first characterize the correlation between computing delay and CPU resources for CPU-intensive tasks based on Dockers technology. Then, we investigate a Docker-based resource management framework to handle task allocation, resource collaboration, and configuration for the edge-cloud computing system, where edge servers can allocate tasks to the cloud and purchase collaborative resources from the cloud while adjusting the configured number of Dockers. In such a way, edge servers can maximize their utility while ensuring quality of service. Based on the intersections between edge servers and the cloud, we formulate a utility maximization problem for edge servers that involves the expected allocated tasks to the cloud, collaborative CPU resources, and the configured number of Dockers, which is NP-hard. Next, based on the correlation analysis between the utility of edge servers and the optimized variables, we propose an effective task allocation and resource management scheme by alternating iterations to obtain the optimal solutions. Finally, we conduct extensive simulation results to show the performance of the proposed scheme in maximizing the utility of edge servers compared to other benchmarks. © 2024 IEEE.

关键词： Mobile edge computing

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