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Automatic Regression Framework for MRAM Compact Models Calibration including stochasticity

Automatic Regression Framework for MRAM Compact Models Calib...

International conference on Synthesis, Modeling, Analysis and Simulation methods and Applications to Circuit Design (SMACD)

作者： Pierre Graindorge Bowen Wang Marie Garcia Bardon Fernando Garcia Redondo IMEC Leuven Belgium University of Liege Liege Belgium

ISBN: (数字)9798350351927

ISBN: (纸本)9798350351934

This paper introduces a comprehensive optimization framework designed to enhance the regression of physical parameters for MRAM technologies, including STT-MRAM, VCMA, and SOT devices. As MRAM emerges as a promising candidate for next-generation memory technologies, the accuracy of physical models becomes crucial for the development and optimization of these devices. However, regressing precise physical parameters from device measurements poses significant challenges due to the complex nature of the phenomena involved and the noise inherent in experimental data. Our framework addresses these challenges by implementing advanced data processing techniques to clean and preprocess measurement data, facilitating the accurate calibration of both electrical and magnetic switching physical models. Furthermore, it incorporates statistical models to account for device variations and intrinsic stochastic behavior, offering a robust solution for the optimization of MRAM technologies. The efficacy of our framework is demonstrated through comprehensive simulations and experimental validations against in-house STT, SOT, and VCMA-MRAM devices. The framework fills the gap between test structures and circuit compact models, required for the development of future MRAM-based applications.

关键词： Analytical models Accuracy Current measurement Noise stochastic processes Calibration Noise measurement Integrated circuit modeling Optimization Next generation networking

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A Lightweight SAR Ship Detection Network Based on Superpixel statistical Modeling 5th

A Lightweight SAR Ship Detection Network Based on Superpixe...

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5th IFIP TC 12 International conference on Intelligence Science, ICIS 2022

作者： Guo, Zhengxi Hou, Biao Ren, Bo Xidian University Xi’an710071 China

ISBN: (纸本)9783031149023

Deep learning technology has been widely used in SAR ship detection tasks. However, complex sea level backgrounds, such as sea clutter and shorelines, greatly interfere with the accuracy of the detection of ship targets. In addition, embedded devices need to deploy multiple detection models, such as FPGA, so model size and detection speed are also important indicators in practical applications. In order to solve these problems, we developed a method combining traditional detection methods with deep learning. In this paper, SAR image clutter distribution model is used to suppress SAR image clutter, and then the processed image is sent to the network for learning. Based on this idea, we have established a superpixel composite Gamma distribution model, which can obtain more accurate fitting results than pixel scale Gamma distribution and suppress background clutter more effectively. We also propose the YOLO-SX lightweight detection network, which significantly reduces model size, detection time, calculation parameters, and memory consumption. Its overall performance is superior to other detection methods. © 2022, IFIP International Federation for Information processing.

关键词： Synthetic aperture radar

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image Encryption Using A Novel Orbital-Extraction Permutation Technique and Chaotic Key Generation

Image Encryption Using A Novel Orbital-Extraction Permutatio...

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International conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Sikandar Khan Safee Ullah Jawad Ahmad Amjad Ullah Arshad Arshad Muhammad Shahbaz Khan Department of Electrican Engineering HITEC University Taxila Pakistan School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK School of Computing Engineering and the Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

This paper presents an image encryption scheme that introduces a novel permutation technique named as orbital-extraction permutation. The proposed encryption scheme contains three important modules, i.e., the key generation module, the orbital-extraction permutation module, and dynamic chaotic substitution module. The key generation module utilizes the 2D Hénon map, a highly non-linear and unpredictable chaotic map, to generate cryptographic keys. The orbital-extraction permutation module reshuffles the pixels of the plain text image in a complex manner disrupting the inherent correlation within the neighboring pixels of the input image. The proposed permutation technique serves as a strong diffusion stage. Furthermore, for the confusion part of the encryption scheme, bit-XOR operations and chaotic substitution methods have been employed. The proposed scheme has been evaluated for key statistical security parameters. Results indicate the enhanced security and robustness of the proposed scheme with an information entropy of 7.9974 and a correlation coefficient of 0.007.

关键词： Correlation coefficient Correlation image processing Digital images Orbits Robustness Entropy

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Analysis of Thresholding methods for the Segmentation of Brain Vessels 10th

Analysis of Thresholding Methods for the Segmentation of...

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10th International conference on Analysis of images, Social Networks, and Texts, AIST 2021

作者： Kruzhalov, Alexey Philippovich, Andrey Moscow Polytechnic University Moscow107023 Russia

ISBN: (纸本)9783031151675

In this paper, the problem of segmentation of the vascular network is solved based on the results of rotational angiography. This task is considered as one of the stages of data pre-processing in the construction of a computer-aided diagnostic system for the recognition of cerebral aneurysms. A statistical analysis of the effectiveness of using threshold segmentation methods to solve the problem under consideration was carried out and the choice of one of them was justified. The following methods are considered in this study: Otsu’s method, Yen’s thresholding method, triangle method, and Sauvola method. As a result of the experiments, it was determined that, among the considered methods, the triangle method, followed by the selection of the largest connected area, is the most suitable for solving the problem of vascular segmentation because this method proved to be the most sensitive to the isolation of low-contrast vessels of small size, allowing to get a complete picture of the vascular network structure. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image segmentation

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High-Speed Compilation of Large-Scale stochastic Circuits

High-Speed Compilation of Large-Scale Stochastic Circuits

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IEEE International conference on Rebooting Computing (ICRC)

作者： Alex Ilov Alexandru Paler Aalto University Espoo Finland

stochastic computing (SC) is executed on bitstreams which encode probabilities into the ratio between the number of one bits and the length of the stream. SC is successfully applied, for example, to image processing and beliefpropagation (BP) of low-density parity-check (LDPC) codes. With increased interest into SC, there is a gap with respect to the available scalable design automation tools needed for the fast prototyping of SC circuits. We bridge this gap by starting from the *** library, and implement data structures and methods to achieve orders of magnitude speed-ups for the compilation of SC circuits. We demonstrate the speedups by compiling BP decoders for LDPC codes. Our improvements enable the compilation of circuits which were previously out of reach for the tools. Our methods pave the way towards scalable design automation of SC.

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Robustly overfitting latents for flexible neural image compression 24

Robustly overfitting latents for flexible neural image compr...

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Proceedings of the 38th International conference on Neural Information processing Systems

作者： Yura Perugachi-Diaz Arwin Gansekoele Sandjai Bhulai Vrije Universiteit Amsterdam Centrum Wiskunde & Informatica

ISBN: (纸本)9798331314385

Neural image compression has made a great deal of progress. State-of-the-art models are based on variational autoencoders and are outperforming classical models. Neural compression models learn to encode an image into a quantized latent representation that can be efficiently sent to the decoder, which decodes the quantized latent into a reconstructed image. While these models have proven successful in practice, they lead to sub-optimal results due to imperfect optimization and limitations in the encoder and decoder capacity. Recent work shows how to use stochastic Gumbel annealing (SGA) to refine the latents of pre-trained neural image compression models. We extend this idea by introducing SGA+, which contains three different methods that build upon SGA. We show how our method improves the overall compression performance in terms of the R-D trade-off, compared to its predecessors. Additionally, we show how refinement of the latents with our best-performing method improves the compression performance on both the Tecnick and CLIC dataset. Our method is deployed for a pre-trained hyperprior and for a more flexible model. Further, we give a detailed analysis of our proposed methods and show that they are less sensitive to hyperparameter choices. Finally, we show how each method can be extended to three-instead of two-class rounding.

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Automating Exam Assessment Through Digital Transformation and image processing 3rd

Automating Exam Assessment Through Digital Transformation an...

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3rd International conference on Electronic Governance with Emerging Technologies, EGETC 2024

作者： Dogan, Onur Department of Management Information Systems Izmir Bakircay University Izmir35665 Turkey Smart University and Digital Transformation Coordinatorship Izmir Bakircay University Izmir35665 Turkey

ISBN: (纸本)9783031770289

The paper presents a study aimed at optimizing the reading and evaluating process of multiple-choice exam sheets (optical forms) as a smart campus application at Izmir Bakircay University, significantly reducing the time and cost involved. Traditional manual evaluation methods are time-consuming and expensive. The proposed solution leverages digital transformation and image processing technologies. The algorithm developed processes scanned images of optical forms, generates scores, and provides statistical reports without requiring specialized knowledge. The methodology is divided into three main steps: detecting sections of the optical form, cropping sections, reading marked circles based on coordinates, reporting exam results, and statistical analysis. Testing with previously used optical forms showed 100% accuracy at a resolution of 600×600 dpi. Errors caused by user handling were anticipated and the system was designed to be robust against such errors. The results of implementing this automated system include a 98.15% reduction in processing time and a 99.95% reduction in cost. The new evaluation time is around 5 min per exam, costing 347 TL. The process now requires only one person instead of four. This study exemplifies the potential of digital transformation and image processing to enhance operational efficiency and cost-effectiveness in academic settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical data processing

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Decoding the Mystery of statistical Sequences and Speculative Data Spaces

Decoding the Mystery of Statistical Sequences and Speculativ...

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Innovations in High Speed Communication and Signal processing (IHCSP), International conference on

作者： Soumya V Menon Shilpa M. Katikar D. Anandhasilambarasan Amanveer Singh S. Devi Mithhil Arora School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A special method called "statistical Sequence Decomposition and Speculative Space Analysis" (SSDSSA) is used to look into both real and made-up data spaces in this study. Compared to other ways of looking at large amounts of data, SSDSSA works better because it is faster, easier to understand, and covers more ground. SSDSSA can break sequences by using t-Distributed stochastic Neighbor Embedding (t-SNE) to reduce the number of dimensions and Singular Spectrum Analysis (SSA) to connect them to made-up data. This reveals data patterns, trends, and linkages that would otherwise be difficult to interpret. SSDSSA out-performs other methods on ranks one to five. SSDSSA outperforms most approaches in complexity, flexibility, speed, readability, and versatility. This paper suggests techniques to improve data analysis in biology, economics, and other domains.

关键词： Dimensionality reduction Couplings Economics Technological innovation Signal processing Data science Market research Decoding Complexity theory Spectral analysis

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Reliability Assessment of Ventilation Networks Based on stochastic Simulation methods

Reliability Assessment of Ventilation Networks Based on Stoc...

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Computer Science and Blockchain (CCSB), International conference on

作者： Junlong Nie Yongkui Shi Xinxin Zhang Shandong University of Science and Technology College of Energy and Mining Engineering Qingdao China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

The degree of safety and reliability of the mine ventilation system directly affects the safety of the mine and workers, in order to scientifically and rationally assess the reliability of the mine ventilation network system, taking the ventilation system of coal mine B as the research object, fully considering the multiple factors affecting the reliability of the ventilation network, establishing the reliability model of the ventilation network considering the airflow, gas concentration and other multi-factors, adopting the method of mathematical statistical analysis to derive the probability distribution of the airflow and gas content of the roadway, and using the Monte Carlo simulation method to obtain the reliability of the ventilation network of coal mine B, it provides a new research idea for the study of reliability of mine ventilation network, which is useful for the study of improving the reliability of mine ventilation system.

关键词： Temperature distribution Monte Carlo methods Computer network reliability Ventilation Probability distribution Mathematical models Fuel processing industries Coal mining Safety Reliability

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A stochastic Proximal Alternating Minimization for Nonsmooth and Nonconvex

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SIAM JOURNAL ON IMAGING SCIENCES 2021年第4期14卷 1932-1970页

作者： Driggs, Derek Tang, Junqi Liang, Jingwei Davies, Mike Schonlieb, Carola-Bibiane Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB3 0WA England Univ Edinburgh Sch Engn Edinburgh EH9 3JL Midlothian Scotland Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China

In this work, we introduce a novel stochastic proximal alternating linearized minimization algorithm [J. Bolte, S. Sabach, and M. Teboulle, Math. Program., 146 (2014), pp. 459--494] for solving a class of nonsmooth and nonconvex optimization problems. Large-scale imaging problems are becoming increasingly prevalent due to the advances in data acquisition and computational capabilities. Motivated by the success of stochastic optimization methods, we propose a stochastic variant of proximal alternating linearized minimization. We provide global convergence guarantees, demonstrating that our proposed method with variance-reduced stochastic gradient estimators, such as SAGA [A. Defazio, F. Bach, and S. Lacoste-Julien, Advances in Neural Information processing Systems, 2014, pp. 1646--1654] and SARAH [L. M. Nguyen, J. Liu, K. Scheinberg, and M. Taka'\c<^>\, Proceedings of the 34th International conference on Machine Learning, PMLR 70, 2017, pp. 2613-2621], achieves stateof-the-art oracle complexities. We also demonstrate the efficacy of our algorithm via several numerical examples including sparse nonnegative matrix factorization, sparse principal component analysis, and blind image-deconvolution.

关键词： nonconvex and nonsmooth optimization stochastic optimization variance reduction Kurdyka--Lojasiewicz inequality stochastic PALM

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