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Continuous Bijection Supervised Pyramid Diffeomorphic Deformation for Learning Tooth Meshes From CBCT Images

IEEE Transactions on Multimedia 2025年

作者： Zhang, Zechu Peng, Weilong Wen, Jinyu Tang, Keke Fang, Meie Feng, David Dagan Li, Ping Guangzhou University Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou511400 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China The University of Sydney Biomedical and Multimedia Information Technology Research Group School of Computer Science SydneyNSW2006 Australia The Hong Kong Polytechnic University Department of Computing School of Design and Research Institute for Sports Science and Technology Hong Kong Hong Kong

Accurate and high-quality tooth mesh generation from cone-beam computerized tomography (CBCT) is an essential computer-aided technology for digital dentistry. However, existing segmentation-based methods require complicated post-processing and significant manual correction to generate regular tooth meshes. In this paper, we propose a method of continuous bijection supervised pyramid diffeomorphic deformation (PDD) for learning tooth meshes, which could be used to directly generate high-quality tooth meshes from CBCT Images. Overall, we adopt a classic two-stage framework. In the first stage, we devise an enhanced detector to accurately locate and crop every tooth. In the second stage, a PDD network is designed to deform a sphere mesh from low resolution to high one according to pyramid flows based on diffeomorphic mesh deformations, so that the generated mesh approximates the ground truth infinitely and efficiently. To achieve that, a novel continuous bijection distance loss on the diffeomorphic sphere is also designed to supervise the deformation learning, which overcomes the shortcoming of loss based on nearest-neighbour mapping and improves the fitting precision. Experiments show that our method outperforms the state-of-the-art methods in terms of both different evaluation metrics and the geometry quality of reconstructed tooth surfaces. © 1999-2012 IEEE.

关键词： Mapping

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Optimizing Vehicle Positioning via Multi-Model Image and Radio Frequency Fusion

Optimizing Vehicle Positioning via Multi-Model Image and Rad...

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IEEE International Conference on Communications (ICC)

作者： Ouwen Huan Mingzhe Chen Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

关键词： Training Deep learning Base stations Simulation Fingerprint recognition Cameras Data models

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Optimal Logistic Map with DNA computing Model for Image Encryption

Optimal Logistic Map with DNA Computing Model for Image Encr...

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computing and Data Analytics (ICCDA), International Conference on

作者： Shiny K.V Wilfred Blessing N. R Jino R Suresh Palarimath Mathumitha V Sutherlin Subitha G Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Department of Civil Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Tamil nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times; the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes.

关键词： Resistance Ciphers PSNR Noise Reliability theory Encoding Matrices Encryption Optimization Logistics

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SigDLA: A Deep Learning Accelerator Extension for Signal Processing

arXiv

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arXiv 2024年

作者： Fu, Fangfa Zhang, Wenyu Jiang, Zesong Zhu, Zhiyu Li, Guoyu Yang, Bing Liu, Cheng Xiao, Liyi Wang, Jinxiang Li, Huawei Li, Xiaowei Department of Microelectronics Science and Technology Harbin Institute of Technology Harbin China SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China Dept. of Computer Science University of Chinese Academy of Sciences Beijing China Institute of Advanced Technology University of Science and Technology of China Hefei China Dept. of Computer Science and Technology Harbin University of Science of Technology Harbin China

Deep learning and signal processing are closely correlated in many IoT scenarios such as anomaly detection to empower intelligence of things. Many IoT processors utilize digital signal processors (DSPs) for signal processing and build deep learning frameworks on this basis. While deep learning is usually much more computing-intensive than signal processing, the computing efficiency of deep learning on DSPs is limited due to the lack of native hardware support. In this case, we present a contrary strategy and propose to enable signal processing on top of a classical deep learning accelerator (DLA). With the observation that irregular data patterns such as butterfly operations in FFT are the major barrier that hinders the deployment of signal processing on DLAs, we propose a programmable data shuffling fabric and have it inserted between the input buffer and computing array of DLAs such that the irregular data is reorganized and the processing is converted to be regular. With the online data shuffling, the proposed architecture, SigDLA, can adapt to various signal processing tasks without affecting the deep learning processing. Moreover, we build a reconfigurable computing array to suit the various data width requirements of both signal processing and deep learning. According to our experiments, SigDLA achieves an average performance speedup of 4.4×, 1.4×, and 1.52×, and average energy reduction of 4.82×, 3.27×, and 2.15× compared to an embedded ARM processor with customized DSP instructions, a DSP processor, and an independent DSP-DLA architecture respectively with 17% more chip area over the original DLAs. © 2024, CC BY-NC-SA.

关键词： Digital signal processing

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Comparative Study of Noises Over Quantum Key Distribution Protocol 7th

Comparative Study of Noises Over Quantum Key Distribution ...

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7th International Conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Bhattacharyya, Sawan Das, Ajanta Banerjee, Anindita Chakrabarti, Amlan Department of Computer Science Ramakrishna Mission Vivekananda Centenary College West Bengal Kolkata India Amity Institute of Information Technology Amity University Kolkata Kolkata India Corporate Research and Development Centre for Development of Advanced Computing Pune India A. K. Choudhury School of Information Technology University of Calcutta West Bengal Kolkata India

ISBN: (纸本)9789819914135

The security of modern cryptographic systems mostly depends on mathematical hardness to solve a particular problem manually. The most popular and widely used classical cryptosystem, RSA (Rivest, Shamir, Alderman) is based on the mathematical hardness of prime factorization of the product of two large prime by any of the present classical processors. But the advancement in technology particularly in quantum information provides a threat to the present communication protocols. It motivates the researchers to move to new technologies that are fundamentally more secure through the principle of quantum mechanics. Such protocols rely upon quantum bits or qubits for data transmissions securely. The application of qubits instead of bits in cryptography using quantum key distribution protocols makes encryption robust. But the quantum communication protocols are prone to noise due to environmental factors, fragile and traverse’ a long fiber optical path. Hence, the states of qubits are easily tampered with by external factors. Noise alters the information;eventually, the message may not be meaningful to receivers. Except for bit flip and phase flip noises, depolarization, amplitude damping, decoherence, thermal relaxation, and phase damping are obstacles to achieving long-distance communication. The objective of this paper is to present a comparative study of quantum communication protocols in a noisy environment with different classes of noise. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quantum entanglement

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Quantum-Informed Recursive Optimization Algorithms

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PRX Quantum 2024年第2期5卷 020327-020327页

作者： Jernej Rudi Finžgar Aron Kerschbaumer Martin J.A. Schuetz Christian B. Mendl Helmut G. Katzgraber BMW AG Munich Germany Department of Computer Science Technical University Munich School of CIT Garching Germany Amazon Quantum Solutions Lab Seattle Washington USA AWS Center for Quantum Computing Pasadena California USA Technical University of Munich Institute for Advanced Study Garching Germany

We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps that recursively simplify the problem. These reduction steps address the limitations of the quantum component (e.g., locality) and ensure solution feasibility in constrained optimization problems. Additionally, we use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware. We showcase the capabilities of our approach by informing QIRO with correlations from classical simulations of shallow circuits of the quantum approximate optimization algorithm, solving instances of maximum independent set and maximum satisfiability problems with hundreds of variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum processor to find large independent sets of graphs. In summary, our scheme achieves results comparable to classical heuristics even with relatively weak quantum resources. Furthermore, enhancing the quality of these quantum resources improves the performance of the algorithms. Notably, the modular nature of QIRO offers various avenues for modifications, positioning our work as a template for a broader class of hybrid quantum-classical algorithms for combinatorial optimization.

关键词： Adiabatic quantum optimization Optimization problems Quantum algorithms Quantum information with atoms & light

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Conformal Graph-level Out-of-distribution Detection with Adaptive Data Augmentation 25

Conformal Graph-level Out-of-distribution Detection with Ada...

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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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Expression of Concern for: Stock Market Prediction using Machine Learning Models

Expression of Concern for: Stock Market Prediction using Mac...

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International Conference on Edge computing and Applications (ICECAA)

作者： A. Yasmin S. Kamalakkannan P. Kavitha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482332

Stock market prediction is the needed emerging economic statistics from business to normal middle-class peoples, to make their investment as a profitable one. This article has utilized the dynamic dataset of the company. The dataset includes the closing price of the stock of the last 290 working days. The dataset is downloaded using the yahoo finance (https://***), so the data is pretty accurate. Further, some technical analysis and machine learning techniques are used to predict the future prices and exchange of company’s stock. The machine learning models includes Linear Regression, Decision Tree, Random Forest, SVR, LSTM, Lasso Regression, KNN, Bayesian Ridge, Gradient Boosting, and Ada Boost are used in this article and suitable technique for the dataset is chosen for performing effective prediction of stock market.

关键词：

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Evaluating Conditional Handover for 5g Networks with Dynamic Obstacles

SSRN

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SSRN 2024年

作者： Deb, Souvik Rathod, Megh Balamurugan, Rishi Singh, Rajeev Kumar Sanyal, Samriddha Ghosh, Shankar K. Advanced Computing & Microelectronics Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Engineering Shiv Nadar Institution of Eminence Delhi201314 India School of Computing and Data Science FLAME University Maharashtra Pune412115 India Shiv Nadar University India

To enhance seamless connectivity in millimetre wave New radio networks, Conditional handover has evolved as a promising solution. Unlike A3 handover where handover execution is certain after receiving handover command from the serving access network, in conditional handover, handover execution is conditional on the Reference signal received power measurements from both current and target access networks, as well as on mobility parameters such as preparation and execution offsets. Presence of dynamic obstacles may block the signal from serving and (or) target access networks, which results in violation of the conditions for the handover preparation/execution. Moreover, signal blockage by dynamic obstacles may cause radio link failure, which may cause handover failure as well. Analytic evaluations of conditional handover performance in the presence of dynamic obstacles are quite limited in the preceding literature. In this work, handover performance of conditional handover has been carried out in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of dynamic obstacles, different mobility parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), user velocity and channel fading characteristics has been proposed to characterize handover failure. Results obtained from the analytic model has been validated against extensive simulation results. Our study reveal that optimal configuration of the mobility parameters is actually conditional on the presence of dynamic obstacles, user velocity and fading characteristics. This study will be helpful for the mobile operators to configure the mobility parameters for New radio systems where dynamic obstacles are present. © 2024, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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The Role of Convolutional Neural Network in Vehicle Detection on Spatial - Temporal Road Traffic Data

The Role of Convolutional Neural Network in Vehicle Detectio...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： S. Senthilarasi S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (纸本)9781665489638

Due to urbanization, people who reside in cities utilize different types of transportation based on their requirement. On regular working days, on-time travelling from one place to another is a difficult task. This results in on-road traffic. Traffic congestion is still a challenging task to be solved by using different methodologies. This research study discusses about different ways to measure traffic congestion in the Intelligent Transportation System (ITS) in Chennai. Since the proposed research study is focused on road traffic congestion, a convolutional neural network is used to detect the vehicles in road traffic by means of a trained neural network to recognize vehicles such as cars, two-wheelers, buses, trucks, vans, auto, etc. To display the road traffic images in different forms, computer vision technology is used. Additionally, the Chennai road traffic dataset is applied in a grouped bar graph to display the number of persons killed and injured due to traffic congestion from 2014 to 2021.

关键词： Recurrent neural networks Roads Urban areas Sociology Transportation Convolutional neural networks Task analysis

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