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arXiv 2023年

作者： Chen, Cheng Miao, Juzheng Wu, Dufan Yan, Zhiling Kim, Sekeun Hu, Jiang Zhong, Aoxiao Liu, Zhengliang Sun, Lichao Li, Xiang Liu, Tianming Heng, Pheng-Ann Li, Quanzheng Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States School of Computing The University of Georgia AthensGA30602 United States

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM’s performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM’s pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM’s pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://***/cchen-cc/MA-SAM. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Automatic Modulation Classification of Frequency-Hopping Signals Using High-Dimensional Phase Diagrams

Automatic Modulation Classification of Frequency-Hopping Sig...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chen, Qibo Yan, Kun Wu, Hsiao-Chun Chang, Shih Yu Yan, Xiao Wu, Yiyan Chang, Haonan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing GuilinGuangxi China Guilin University of Electronic Technology Department of Information and Telecommunication Guangxi Guilin541004 China Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States San Jose State University Department of Applied Data Science San JoseCA95192 United States University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu China Communications Research Centre Canada OttawaK2H 8S2 Canada Haonan Chang Is with Xi'an University Xi'an China

ISBN: (纸本)9798350321524

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM);the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise. © 2023 IEEE.

关键词： Support vector machines

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Support Vector Machine reconfigurable hardware implementation on FPGA

Franklin Open

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Franklin Open 2024年 7卷

作者： Yacoub, Mohammed H. Ismail, Samar M. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Nanoelectronics Integrated Systems Center (NISC) Nile University Giza 12588 Egypt School of Engineering and Computer Science University of Hertfordshire Egypt Engineering Mathematics and Physics Department Faculty of Engineering Cairo University Giza 12613 Egypt Radiation Engineering Department National Centre for Radiation Research and Technology Egyptian Atomic Energy Authority Cairo 11787 Egypt School of Engineering and Applied Sciences Nile University Giza 12588 Egypt

Support Vector Machine (SVM) is a robust Machine Learning (ML) algorithm used extensively in classification tasks. This work proposes a reconfigurable hardware implementation of the SVM classification algorithm for the linear and three kernel cases on FPGA. Efficient implementations of two generalization techniques, One-versus-All (OvA) and One-versus-One (OvO), to deal with multi-class problems are also realized on FPGA to overcome the binary nature of the SVM algorithm. The presented model is fully reconfigurable and can easily be adapted to any dataset with any number of classes or features. The results show that the proposed model excels in power efficiency, requires low area utilization, and reaches high performance up to 250.7 MHz. The two realized generalization methods, OvO and OvA, offer a trade-off between accuracy and hardware cost. OvA provides lower accuracy than OvO and is more affected by the data imbalance problem, which becomes more dominant as the number of classes increases;however, it is more resource-efficient than OvO. The proposed hardware design is realized and experimentally tested on FPGA for three different datasets, employing different linear and kernel SVM cases. The classification accuracy achieved by the reconfigurable hardware design matches the software simulation results. © 2024 The Authors

关键词： Artificial intelligence Classification Floating point FPGA Support Vector Machine

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Optimised hybrid image encryption scheme using arnold transform, chaotic map and genetic algorithm for improved security and robustness

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AIP Conference Proceedings 2024年第1期3081卷

作者： Bhavana Sharma Mehak Khurana Hukum Singh Department of Computer Science and Engineering The Northcap University Gurgaon India Department of Applied Sciences The Northcap University Gurgaon India

Inadequate safety, image degradation, low latency, low-key sensitivity, diffusion and strong correlation are various major challenges and issues with the traditional image encryption approach. Consequently, a better picture encryption algorithm based on scrambling and genetic algorithm is proposed in this work. In the beginning, image scrambling performs transformations in the position of every pixel without altering values of the pixels. Its statistical features do not change. Therefore, the proposed algorithm of two-level image encryption makes extensive use of a mix of Chaotic Scrambling and Arnold transform along with genetic encryption to increase image encryption more secure. Conclusively, to evaluate the durability of the established two level techniques, the decrypted image is exposed to brute force attack along with comparable key attack. The method offers certain benefits in robust performance, statistical efficacy in addition to this, it also gives high performance in key sensitivity test, picture security and real-time performance, according to experimental results.

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A new class of efficient high order semi-Lagrangian IMEX discontinuous Galerkin methods on staggered unstructured meshes

arXiv

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arXiv 2024年

作者： Tavelli, M. Boscheri, W. Faculty of Engineering Free University of Bolzano Piazzetta Università 1 Bolzano39100 Italy Laboratoire de Mathématiques UMR-5127 CNRS Université Savoie Mont Blanc Le Bourget du Lac73376 France Department of Mathematics and Computer Science University of Ferrara Italy

In this paper we present a new high order semi-implicit discontinuous-Galerkin (DG) scheme on two-dimensional staggered triangular meshes applied to different nonlinear systems of hyperbolic conservation laws such as advection-diffusion models, incompressible Navier-Stokes equations and natural convection problems. While the temperature and pressure field are defined on a triangular main grid, the velocity field is defined on a quadrilateral edge-based staggered mesh. A semi-implicit time discretization is proposed, which separates slow and fast time scales by treating them explicitly and implicitly, respectively. The nonlinear convection terms are evolved explicitly using a semi-Lagrangian approach, whereas we consider an implicit discretization for the diffusion terms and the pressure contribution. High-order of accuracy in time is achieved using a new flexible and general framework of IMplicit-EXplicit (IMEX) Runge-Kutta schemes specifically designed to operate with semi-Lagrangian methods. To improve the efficiency in the computation of the DG divergence operator and the mass matrix, we propose to approximate the numerical solution with a less regular polynomial space on the edge-based mesh, which is defined on two sub-triangles that split the staggered quadrilateral elements. This allows for a fast computation of the DG operators and matrices without any need to store them for each mesh element. Due to the implicit treatment of the fast scale terms, the resulting numerical scheme is unconditionally stable for the considered governing equations because the semi-Lagrangian approach is the only explicit discretization for convection terms that does not need any stability restriction on the maximum admissible time step. Contrarily to a genuinely space-time discontinuous-Galerkin scheme, the IMEX discretization permits to preserve the symmetry and the positive semi-definiteness of the arising linear system for the pressure that can be solved at the aid of an efficie

关键词： Navier Stokes equations

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An Example of a Fractional Model of Enzymatic Reactions with Bi-Bi Random Mechanism

SSRN

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SSRN 2024年

作者： Nhan, Thái Anh Mai, Vinh Quang Department of Mathematics Thu Dau Mot University 06 Tran Van On Binh Duong Thu Dau Mot820000 Viet Nam Department of Mathematics and Computer Science Santa Clara University 500 El Camino Real Santa ClaraCA95053 United States Applied Math and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Enzymes are proteins capable of accelerating biochemical reactions in the metabolism process. A wide range of applications of enzymes has been developed in biotechnology, industry, pharmaceuticals, biofuels. In this paper, we give a main example which shows how to develop a fractional mathematical model that describes the enzymatic reactions with Bi-Bi random mechanism. © 2024, The Authors. All rights reserved.

关键词： Enzymes

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Model-based control algorithms for the quadruple tank system: An experimental comparison

arXiv

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arXiv 2022年

作者： Andersen, Anders H.D. Ritschel, Tobias K.S. Hørsholt, Steen Huusom, Jakob Kjøbsted Jørgensen, John Bagterp Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Department of Chemical and Biochemical Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS). We estimate the parameters in a continuous-discrete time stochastic nonlinear model for the QTS using a prediction-error-method based on the measured process data and a maximum likelihood (ML) criterion. In the NMPC algorithm, we use this identified continuous-discrete time stochastic nonlinear model. The LMPC algorithm is based on a linearization of this nonlinear model. We tune the PID controller using Skogestad’s IMC tuning rules using a transfer function representation of the linearized model. Norms of the the observed tracking errors and the rate of change of the manipulated variables are used to compare the performance of the control algorithms. The LMPC and NMPC perform better than the PID controller for a predefined time-varying setpoint trajectory. The LMPC and NMPC algorithms have similar performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Markovian Degradation Modeling on Rails

SSRN

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SSRN 2024年

作者： Bisgaard, Albert Skovgaard Harrod, Steven Møller, Jan Kloppenborg Nielsen, Bo Friis Rasmussen, Carsten Jørn Bjørge, Thomas Vatn Vatn, Jørn Technical University of Denmark Department of Engineering Technology and Didactics Production Transportation and Planning Lautrupvang 15 Ballerup2750 Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Richard Petersens Plads Building 324 Kongens Lyngby2800 Denmark Norwegian University of Science and Technology Department of Mechanical and Industrial Engineering Høgskoleringen 1 Trondheim7034 Norway COWI A/S Railways & Metro Parallelvej 2 Kongens Lyngby2800 Denmark Bane NOR Oslo gate 3 Oslo0192 Norway

Rail defects pose a significant threat to railway safety and efficiency. Refined modeling of the propagation of rail defects has the potential of informing maintenance activities for circumvention of dangerous rail degradation. In this study, we estimate defect transition rates based on discretely observed degradation trajectories for defects discovered at the Norwegian rail network considering the impact from tonnage and line speed, as well as rail curvature, profile and *** propagation of rail defects is modeled by a continuous-time Markov chain (subject to a generalized Erlang distribution) with transition rates regressed on location-dependent covariates. We propose two estimation approaches. The first approach obtains the full data log-likelihood by Monte Carlo simulation of full data defect trajectories, which informs the Expectation-Maximization algorithm. The second approach maximizes the discrete data log-likelihood informed by analytical gradient *** methodologies give rise to fast convergence of model estimates with similar numerical values, indicating a favorable local optimum. The estimated coefficients for the covariates are found to be statistically significant and in directional accordance with their physical interpretation. Model checking is performed by k-fold cross-validation. Experiments with indicator variables show that the covariate information describes enough variance to satisfactorily account for the structural variability between the different rail *** estimated models are used to compute expected transition times for defects in all the considered rail lines, and demonstrate how the spatially varying track conditions affect the defect propagation. © 2024, The Authors. All rights reserved.

关键词： Defects

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 38

Binding in hippocampal-entorhinal circuits enables compositi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kymn, Christopher J. Mazelet, Sonia Thomas, Anthony Kleyko, Denis Frady, E. Paxon Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Department of Electrical and Computer Engineering UC Davis Davis United States Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara United States Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley United States

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. © 2024 Neural information processing systems foundation. All rights reserved.

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Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 476-487页

作者： Kenis, Michiel Dvorkin, Vladimir Schittekatte, Tim Bruninx, Kenneth Delarue, Erik Botterud, Audun Celestijnenlaan 300 KU Leuven Divison of Applied Mechanics and Energy Conversion LeuvenB-3001 Belgium MolB-2400 Belgium EnergyVille GenkB-3600 Belgium Massachusetts Institute of Technology Energy Initiative and Laboratory for Information and Decision Systems CambridgeMA02139 United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Energy Initiative Massachusetts Institute of Technology CambridgeMA02142 United States Faculty of Technology Policy and Management Delft University of Technology GA Delft2600 Netherlands Divison of Applied Mechanics and Energy Conversion Celestijnenlaan 300 KU Leuven LeuvenB-3001 Belgium KU Leuven Divison of Applied Mechanics and Energy Conversion Celestijnenlaan 300 LeuvenB-3001 Belgium Massachusetts Institute of Technology CambridgeMA02139 United States Argonne National Laboratory LemontIL60439 United States

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms. © 2024 IEEE.

关键词： Investments

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