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

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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Approach for enhancing sensitivity of tin-oxo cluster resist for high NA extreme UV lithography 41

Approach for enhancing sensitivity of tin-oxo cluster resist...

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Advances in Patterning Materials and Processes XLI 2024

作者： Ku, Yejin Kim, Gayoung Kim, Min Seung Lee, Jin-Kyun Kim, Jiho Park, Byeong-Gyu Lee, Sangsul Lee, Seohyeon Jung, Byung Jun Lee, Changhyeon Kim, Hyunseok Hur, Su-Mi Koh, Chawon Nishi, Tsunehiro Kim, Hyun-Woo Program in Environment and Polymer Engineering Inha University Incheon22212 Korea Republic of Department of Polymer Science and Engineering Inha University Incheon22212 Korea Republic of Pohang Accelerator Laboratory Postech Pohang37673 Korea Republic of Department of Materials Science and Engineering University of Seoul Seoul02504 Korea Republic of Department of Polymer Engineering Graduate School Chonnam National University Gwangju61186 Korea Republic of School of Polymer Science and Engineering Chonnam National University Gwangju61186 Korea Republic of Department of Materials Science and Engineering Yonsei University Seoul03722 Korea Republic of Samsung Electronics Co. Ltd. Semiconductor R&d Center Gyeonggi-do 18448 Korea Republic of

ISBN: (纸本)9781510672208

High numerical aperture (NA) EUV lithography is considered as the most-promising candidate for next generation lithography protocol that will play a crucial role in meeting the demand on the enhanced semiconductor performance and productivity. While this technology enables the fabrication of sub-10 nm patterns, the increased NA has posed challenges, such as reduced depth of focus and narrower process margins. To overcome these hurdles, it is essential to apply thinner resist films while ensuring that the resulting small patterns maintain sufficient physical and chemical durability. Moreover, considering that thinner films absorb fewer photons, the resist molecular structure should be designed to compensate sensitivity burdens. Here, an approach has been proposed to enhance sensitivity by accelerating the solubility change of a well-known tinbased nanocluster resist. To accomplish this goal, it is important to increase not only the number of secondary electrons but also reaction sites and promote radical-based chemical reactions. We aimed at experimentally validating this concept by utilizing elements with high EUV absorbance and highly reactive functional groups with tin radicals. As a model resist, we chose a tin-oxo cage material consisting of a divalent cation containing 12 tin atoms and two counter anions. Our findings indicate that the introduction of unsaturated groups capable of building bridging bonds with radicals leads to fast solubility change at a lower exposure dose, thus enhancing sensitivity. This research offers a promising direction for the development of resists tailored for High NA EUV lithography. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tin

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Real-Time Validation of Intelligent Super-Twisting Sliding Mode Control for Variable-Speed DFIG Using dSPACE 1104 Board

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IEEE Access 2024年 12卷 31892-31915页

作者： Yessef, Mourad Benbouhenni, Habib Taoussi, Mohammed Lagrioui, Ahmed Colak, Ilhami Mobayen, Saleh Zhilenkov, Anton Bossoufi, Badre University LIMAS Laboratory Faculty of Sciences Dhar El Mahraz Fes30000 Morocco Nişantaşi University Faculty of Engineering and Architecture Department of Electrical and Electronics Engineering Istanbul34481742 Turkey SMBA University Higher School of Technology Fes30000 Morocco Moulay Ismail University ENSAM Meknes50000 Morocco National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliou Yunlin640301 Taiwan Saint Petersburg State Marine Technical University Department of Cyber-Physical Systems Saint-Petersburg190121 Russia

The super-twisting sliding mode controller (STSMC) is considered one of the simplest and easiest to implement nonlinear controls, as it can be easily applied in industrial systems. Using this controller leads to reducing the phenomenon of chatter and significantly increasing the durability of the systems. However, this controller has several drawbacks and problems, as its use does not lead to improving the systems as required. In this study, an idea has been proposed based on the use of neural networks in order to overcome the defects and problems of the STSMC strategy. This proposed strategy is used to increase the durability and improve the characteristics of an energy system based on wind turbines using a dual induction generator. The latter is controlled by direct power control (DPC). The proposed controller was used to control energies, where the pulse width modulation strategy was used to convert voltage reference values into pulses to operate the rotor side converter. Therefore, simplicity, durability, outstanding performance, ease of implementation, few gains, and low cost are among the most prominent features of the proposed intelligent STSMC technique and control. The MATLAB environment was used first to implement the proposed energy system under different working conditions using variable wind speed, where the proposed controller was compared with the performance of the STSMC technique. The proposed intelligent STSMC technique was implemented experimentally using the dSPACE 1104 Card to verify the performance and the validity of the simulated results. The experimental results show the effectiveness and ability of the proposed controller to improve the characteristics of the DPC strategy and the energy system as a whole. © 2013 IEEE.

关键词： Asynchronous generators

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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A High-Time-Resolution Time-to-Digital Converter Using Coupled Ring Oscillator with Phase Averaging

A High-Time-Resolution Time-to-Digital Converter Using Coupl...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Daiki Ogata Ryosuke Kamiya Yusuke Toyoshima Kenichi Ohhata Department of Engineering Electrical and Electronics Engineering Program Kagoshima University Kagoshima Japan

This paper proposes a high-time-resolution time to digital converter (TDC) using a coupled ring oscillator (CRO) with phase averaging. The CRO can generate short time interval multiphase clocks, and combining it with a counter enables a high-time-resolution TDC with a wide input range and relatively small circuitry. In addition, the application of phase averaging to the CRO reduces the timing error due to mismatch. A novel sampling technique is also proposed to eliminate the sampling error due to counter delay and glitches. Post-layout simulations were performed on a 10-bit TDC in 28-nm CMOS technology to evaluate the performance of the proposed TDC. The results showed that the high time resolution of 2.5 ps and low power dissipation of 15.6 m W at the sampling frequency of 350 MHz could be obtained, along with the FOM of 0.083 pJ/conv.-step.

关键词： Ring oscillators Semiconductor device modeling Time-frequency analysis Circuits and systems Delay lines CMOS technology Power dissipation

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Exploring the Science and Art of UAV Light Painting: From Equations and Pixels to Long-Exposure Photography

Exploring the Science and Art of UAV Light Painting: From Eq...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes-Junior Celso O. Barcelos Raquel F. Vassallo Alexandre S. Brandão Department of Electrical Engineering Robotics Specialization Center Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil Department of Electrical Engineering Federal University of Espirito Santo Vitória ES Brazil

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The use of Unmanned Aerial Vehicles (in short, UAVs, aka drones) for cultural and entertainment purposes, such as drone light shows, has grown exponentially. One such innovative and creative application is the visual arts using drones to explore long-exposure photography. Light painting are generally performed indoors and outdoors in a dedicated space with a human using a moving light source to create spectacular images and save the movement perception in a picture. In this work, we propose a robotic perception system designed to choreograph UAV movements based on time-parametric curves or image edges, serving as reference motions. Our framework begins by processing a digital image, extracting its contours through boundary tracing, and subsequently generating a safe, navigable, and precise path for UAV motion planning. This process involves optimizing waypoints within the UAV workspace to determine a feasible trajectory that encompasses all designated points or computes safe trajectories utilizing established mathematical equations. The validation of the motion planning is performed through light painting, where the UAV can either fly through the motion reference to mimic the original image. The generated trajectories on light painting mode by physical robots are compared against the ground-truth demonstrating the accuracy of the applied control scheme.

关键词： Photography Visualization Art Autonomous aerial vehicles Trajectory Planning Robots

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Multi-Objective Grey Wolf Optimizer for the Identification Process of a real MIMO System

Multi-Objective Grey Wolf Optimizer for the Identification P...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Allan Christian Krainski Ferrari Gideon Villar Leandro Leandro dos Santos Coelho Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) Federal University of Paraná (UFPR) and Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR)

This article implements the multi-objective grey wolf optimizer (MOGWO) in the identification process of a multivariable system. For a sequence of 100 runs, MOGWO approach had compared and analyzed with the grey wolf optimizer (GWO), the single-objective version. In the computational simulation, the determination coefficient values and mean square error had used to analyze the quality of the models obtained. In the results, MOGWO showed superior to GWO to the differences between the order of magnitude of the system outputs. The implementation shows that MOGWO and other multi-objective metaheuristics can be an alternative method to solve problems of identification processes of multivariable systems.

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A Modified Radiopropagation Multipath Model for Constant Refractivity Gradient Profiles

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2023年第2期22卷 298-312页

作者： Parada, D. Rego, C.G. Guevara, D. Navarro, A. Ramos, G.L. Oliveira, R. Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627 MG Belo Horizonte Brazil Departament of Electronics Engineering Universidade Federal de Minas Gerais MG Belo HorizonteCEP 31270-901 Brazil Departament of Electricity and Electronics Universidad Francisco de Paula Santander Norte de Santander CúcutaCEP 540003 Colombia Departament of Information and Communication Technologies Universidad ICESI Cali Valle del CaucaCEP 760031 Colombia Departament of Telecommunications and Mechatronics Engineering Federal University of São João del-Rei MG Ouro BrancoCEP 36415-000 Brazil

This paper presents a radiopropagation algorithm based on a Ray Tracing (RT) technique that combines a modified multipath model for constant refractivity gradient profiles and the Uniform Theory of Diffraction (UTD). A novel formulation is proposed by the authors for calculation and ground-reflection analysis of ray paths depending on atmospheric refractivity. The algorithm introduced herein was evaluated in a mixed scenario and in two more realistic case studies, under conditions of constant refractivity gradient and lossy terrain profiles. Pathloss results are obtained and compared with Parabolic Equation (PE) numerical solution results at 2.0 GHz, 3.5 GHz and 5.4 GHz. In such conditions, the modified radiopropagation multipath algorithm with atmospheric refractivity introduced herein showed satisfactory results. © 2023 SBMO/SBMag.

关键词： Refraction

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