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arXiv 2025年

作者： Kim, Youngseok Hwang, Sunwook Kim, Hyung-Sin Bahk, Saewoong Department of Electrical and Computer Engineering Seoul National University Korea Republic of System LSI Samsung Electronics Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of

The growing use of 3D point cloud data in autonomous vehicles (AVs) has raised serious privacy concerns, particularly due to the sensitive information that can be extracted from 3D data. While model inversion attacks have been widely studied in the context of 2D data, their application to 3D point clouds remains largely unexplored. To fill this gap, we present the first in-depth study of model inversion attacks aimed at restoring 3D point cloud scenes. Our analysis reveals the unique challenges, the inherent sparsity of 3D point clouds and the ambiguity between empty and non-empty voxels after voxelization, which are further exacerbated by the dispersion of non-empty voxels across feature extractor layers. To address these challenges, we introduce ConcreTizer, a simple yet effective model inversion attack designed specifically for voxel-based 3D point cloud data. ConcreTizer incorporates Voxel Occupancy Classification to distinguish between empty and non-empty voxels and Dispersion-Controlled Supervision to mitigate non-empty voxel dispersion. Extensive experiments on widely used 3D feature extractors and benchmark datasets, such as KITTI and Waymo, demonstrate that ConcreTizer concretely restores the original 3D point cloud scene from disrupted 3D feature data. Our findings highlight both the vulnerability of 3D data to inversion attacks and the urgent need for robust defense strategies. Copyright © 2025, The Authors. All rights reserved.

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FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning

arXiv

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arXiv 2023年

作者： Lee, Gihun Jeong, Minchan Kim, Sangmook Oh, Jaehoon Yun, Se-Young Graduate School of AI KAIST Korea Republic of Samsung Advanced Institute of Technology Korea Republic of Department of Electrical and Computer Engineering UBC Canada

Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge, it can also hinder local learning by interfering with local objectives. To address this problem, we propose a novel method, Federated Stabilized Orthogonal Learning (FedSOL), which adopts an orthogonal learning strategy to balance the two conflicting objectives. FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically, FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios. © 2023, CC BY.

关键词： Data privacy

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups 22

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Harding, J. Mason Pourmotabbed, Haatef Li, Yamin Rogge-Obando, Kimberly Wang, Kate Goodale, Sarah E. Wang, Shiyu Bibro, Camden Allee, Bergen Martin, Caroline Morgan, Victoria L. Englot, Dario J. Chang, Catie Departments of Electrical and Computer Engineering Vanderbilt University United States Departments of Biomedical Engineering Vanderbilt University United States Departments of Computer Science Vanderbilt University United States Neuroscience Graduate Program Vanderbilt University United States Departments of Radiology Vanderbilt University Medical Center United States Departments of Neurological Surgery Vanderbilt University Medical Center United States Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center United States

ISBN: (纸本)9798331520526

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups. © 2025 IEEE.

关键词： Brain mapping

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Tunable Source of Quantum-Correlated Photons with Integrated Pump Rejection in a Silicon CMOS Platform

Tunable Source of Quantum-Correlated Photons with Integrated...

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Frontiers in Optics + Laser Science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Fargas Cabanillas, Josep M. Kramnik, Danielius Ramesh, Anirudh Gentry, Cale M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

ISBN: (纸本)9781557528209

A wavelength-tunable, silicon photon-pair source based on spontaneous four-wave mixing, integrated with a pump rejection filter in a single, flip-chip packaged CMOS chip, is demonstrated with a coincidence-to-accidentals ratio of 9.1 with no off-chip pump filtering. Frontiers in Optics / Laser Science © Optica Publishing Group 2021.

关键词： CMOS integrated circuits

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Deep Q-Network Based Decision Making for Autonomous Driving

arXiv

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arXiv 2023年

作者： Ronecker, Max Peter Zhu, Yuan Technical University of Munich Department of Electrical and Computer Engineering Munich Germany Tongji University Sino-German College for Graduate Studies Shanghai China

Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers. In order to prove the functionality of this approach it is evaluated on two different highway traffic scenarios. Furthermore, the impact of different state representations on the performance and training process is analyzed. The results show that the proposed system can produce efficient and safe driving behavior. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Development of a digital twin environment for smart collision avoidance algorithms for mobile robots using reinforcement learning

Development of a digital twin environment for smart collisio...

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SICE Annual Conference

作者： Natsumi Matsumoto Kazuyuki Kobayashi Tomoyuki Ohkubo Kaiqiao Tian Nashwan J Sebi Ka C Cheok Cai Changqing Graduate School of Science and Engineering Hosei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Electrical & Computer Engineering Department School of Engineering & Computer Science Oakland University Rochester USA School of Electrical Engineering and Information Technology Changchun Institute of Technology China

This paper describes a study on building a digital twin environment using point cloud data for mobile robots. A digital twin environment for mobile robots is a method to reproduce almost the same environment in a simulation space as if it were a twin, using data from an actual natural environment. In fact, in the development of mobile robots, experiments on public urban area are conducted, however, they are incredibly time-consuming and labor-intensive, so it would be helpful if a realistic simulation environment could be created. This study focuses on building a digital twin environment using point-cloud-data for mobile robots. In order to verify the effectiveness of this approach, we constructed the environment using data from a real mobile robot that has been driven. The navigation of the mobile robot was performed in the constructed simulation environment, and it was possible to realize a navigation run in the driving environment that was almost the same as the run of the real mobile robot.

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Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

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computers, Materials & Continua 2021年第8期68卷 1447-1465页

作者： T.Kowsalya R.Ganesh Babu B.D.Parameshachari Anand Nayyar Raja Majid Mehmood Department of Electronic and Communication Engineering Muthayammal Engineering CollegeRasipuramNamakkalTamilnaduIndia Department of Electronics and Communication Engineering SRM TRP Engineering CollegeTiruchirappalliTamilnaduIndia Department of Telecommunication Engineering GSSS Institute of Engineering and Technology for WomenMysuruIndia Faculty of Information Technology Graduate SchoolDuy Tan UniversityDa Nang550000Viet Nam Department of Information and Communication Technology School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive ***,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious *** order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious *** True Random Number Generator-Pseudo Random Number Generator(TRNG-PRNG)based key generation is proposed to generate the unpredictable keys,being highly difficult to predict by the ***,the hardware utilization of PRESENT architecture is optimized using the Dual port Read Only Memory(DROM).The proposed PRESENT-TRNGPRNG architecture supports the 64-bit input with 80-bit of key *** performance of the PRESENT-TRNG-PRNG architecture is evaluated by means of number of slice registers,flip flops,number of slices Look Up Table(LUT),number of logical elements,slices,bonded input/output block(IOB),frequency,power and *** input retrieval performances analyzed in this PRESENT-TRNG-PRNG architecture are Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Mean-Square Error(MSE).The PRESENT-TRNG-PRNG architecture is compared with three different existing PRESENT architectures such as PRESENT On-TheFly(PERSENT-OTF),PRESENT Self-Test Structure(PRESENT-STS)and PRESENT-Round Keys(PRESENT-RK).The operating frequency of the PRESENT-TRNG-PRNG is 612.208 MHz for Virtex 5,which is high as compared to the PRESENT-RK.

关键词： Dual port read only memory hardware utilization lightweight cryptography malicious attackers present block cipher pseudo random number generator true random number generator

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Digital Phenotype Representation by Statistical, Information Theory, Data-Driven Approach with Digital Health Data 48

Digital Phenotype Representation by Statistical, Information...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Nguyen, Binh Nigro, Michael Rueda, Alice Bhat, Venkat Krishnan, Sri Toronto Metropolitan Univerity Department of Electrical Computer and Biomedical Engineering TorontoONM5B 2K3 Canada St. Michael's Hospital Interventional Psychiatry Program TorontoM5B 1W8 Canada

ISBN: (纸本)9781728163277

Digital phenotyping (DP) is a multidisciplinary field of science that quantifies the individual level phenotype through active and passive data. Although DP is a multidisciplinary field, there lacks a technical and a systematic approach to representing DP. This work proposes the development of digital phenotype profile (DPP) to represent a user's physical and behavioural health baseline through systematic investigations with an emphasis on robustness and explainability. To achieve this, a Statistical, Information Theory, and Data-driven (SID) pipeline will develop the foundation of the DPP. SID evaluates the non-linearity of the signal to offer inference for domain-specific feature extraction, evaluates the information theory to rank the DPP parameters, and imputes missing data for robust analysis, respectively. SID was applied to a 24-hr Multi-Level dataset and was able to represent individual DPPs. The respective DPPs were visualized and clusters of awake and asleep were used for individual specific modelling. © 2023 IEEE.

关键词： Data-Driven Digital Phenotyping Robustness Signal Analysis Wearables

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Ivan Alexander Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Departement Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers.

关键词： Training Sentiment analysis Accuracy Social networking (online) Text categorization Nearest neighbor methods Naive Bayes methods Random forests Robots Airline industry

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