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arXiv 2023年

作者： Neuhaus, Leonhard Croquette, Michaël Metzdorff, Rémi Chua, Sheon Jacquet, Pierre-Edouard Journeaux, Alexandre Heidmann, Antoine Briant, Tristan Jacqmin, Thibaut Cohadon, Pierre-François Deléglise, Samuel Laboratoire Kastler Brossel Sorbonne Université ENS - Université PSL Collège de France CNRS 4 place Jussieu ParisF-75005 France

We present PyRPL, an open source software package that allows the implementation of automatic digital feedback controllers for quantum optics experiments on commercially available, affordable FPGA boards. Our software implements the digital generation of various types of error signals, from an analog input through the application of loop filters of high complexity and real-time gain adjustment for multiple analog output signals, including different algorithms for resonance search, lock acquisition sequences and in-loop gain optimization. Furthermore, all necessary diagnostic instruments such as an oscilloscope, a network analyzer and a spectrum analyzer are integrated into our software. Apart from providing a quickly scalable, automatic feedback controller, the lock performance that can be achieved by using PyRPL with imperfect equipment such as piezoelectric transducers and noisy amplifiers is better than the one achievable with standard analog controllers due to the higher complexity of implementable filters and possibilities of nonlinear operations in the FPGA. This drastically reduces the cost of added complexity when introducing additional feedback loops to an experiment. The open-source character also distinguishes PyRPL from commercial solutions, as it allows users to customize functionalities at various levels, ranging from the easy integration of PyRPL-based feedback controllers into existing setups to the modification of the FPGA functionality. A community of developers provides fast and efficient implementation and testing of software modifications. © 2023, CC BY.

关键词： Open source software

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Double Deep RL-Based Strategy for UAV-Assisted Energy Harvesting optimization in Disaster-Resilient IoT Networks

Double Deep RL-Based Strategy for UAV-Assisted Energy Harves...

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International conference on Mechatronics (ICOM)

作者： Nahla Nur Elmadina Rashid A. Saeed Elsadig Saeid Elmustafa Sayed Ali Ibtehal Nafea Mayada A. Ahmed Rania A. Mokhtar Othman O. Khalifa Department of Electrical Engineering Alzaiem Alazhari University Khartoum Sudan Electronics Department Sudan University of Science and Technology Khartoum Sudan College of Computer Science and Engineering Taibah University Medina Saudi Arabia Faculty of Eng. School of ElecsEng. Sudan Uni. of Sci. and Tech. Khartoum Sudan Dept of Elec and Comp Eng Inter. Islamic Uni. Malaysia KL Malaysia Libyan Center for Engineering Research and Information Technology Bani Walid Libya

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Unmanned Aerial Vehicles (UAVs) are increasingly crucial for emergency-response scenarios, including tasks like wireless power transfer (WPT) and data collection in disaster zones. This paper proposes a Double Deep Reinforcement Learning (DDRL) framework for energy harvesting (EH) in such scenarios. Our framework involves a UAV swarm navigating an area to provide WPT. The primary goal is to enhance service quality in critical areas while enabling dynamic swarm management for tasks like recharging. We formulate this as a nonlinear programming (NLP) optimization problem, maximizing EH from IoT devices and optimizing UAV trajectories under constraints like mission duration and energy limits. Due to the problem's complexity, we propose a lightweight DDRL solution capable of efficiently learning system dynamics. Extensive simulations and comparisons with Deep RL and DDPG algorithms demonstrate the superior performance of DDRL in enhancing EH, covering strategic locations effectively, and achieving high satisfaction and accuracy rates.

关键词： Wireless power transfer Programming Autonomous aerial vehicles nonlinear dynamical systems Trajectory Internet of Things Energy harvesting

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An optimal software-pipelining method for instruction-level parallel processors based on scaled retiming

An optimal software-pipelining method for instruction-level ...

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International Symposium on Image and Signal Processing and Analysis

作者： F. Fernandez A. Sanchez A. Duarte Dept. Fotónica FI-UPM Campus Montegancedo Madrid Spain ESCET-URJC Campus de Móstoles Madrid Spain

ISBN: (纸本)9539676940

software pipelining is an instruction-level loop scheduling method for achieving high performance fine-grain parallelism on VLIW (very long instruction word) processors. This paper presents a novel software pipelining method for non-pipelining parallel processors based on integer scaling and retiming transformations. This approach generalises and simplifies the analogous extended retiming model of T.W. O'Neil et al. (see Proc. ISCA 12th Int. Conf. Parallel & Distributed Computing Syst., p.292-7, 1999; Proc. of ICASSP'99 Conf., vol.4 p.2001-4, 1999). Matrix techniques are used in order to simplify the corresponding graph transformations. Some general properties taken from algebraic graph theory are applied in order to obtain general scheduling techniques: node and cycle methods. The two-phase scheduling method considered is first defined by means of two standard linear programming problems. We transform the corresponding problems into some variants of the maximum cost-to-time ratio problem and shortest path problem, in order to obtain efficient polynomial time algorithms. An example of software pipelining optimization of a digital correlator is also given.

关键词： Pipeline processing Processor scheduling VLIW software performance Parallel processing Distributed computing Graph theory Linear programming Shortest path problem Polynomials

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Control Architecture for Autonomous RF Cavity Filter and Multiplexer Tuning

Control Architecture for Autonomous RF Cavity Filter and Mul...

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International Automatic Testing conference, AUTOTESTCON

作者： Yarkin Yigit Onder Suvak Department of Electronics Engineering Gebze Technical University ASELSAN INC. ANKARA Turkey Department of Electronics Engineering Gebze Technical University Gebze Turkey

ISBN: (数字)9781728154008

ISBN: (纸本)9781728154015

The method of tuning a cavity filter and multiplexer is a stringent process since material and manufacturing tolerances. The post-production process is not only time consuming but also expensive, especially for high-order narrowband complex filters which include coupling and cross-coupling parts. In addition to manual tuning of these filters limits precise tuning and production volumes and it increases manufacturing costs. In this scope, it is inevitable to replace this traditional manual tuning task with some more advanced and automatic methods. In order to overcome these problems, software controlled robotic tuning system is implemented. This paper introduces robotic control architecture for cavity filter and multiplexer tuning based on intelligence computer-aided tuning. The system is specially designed for miniaturized tuning screw filters. It works fully autonomously and its members are COBOT, single and multi-axis robotic arms, and a cartesian platform. Also, it includes a pattern recognition system and force-torque sensors to sense and measure all relevant data during the operation process. Rf tuning and control algorithms architectures are built on data driven model which fed and learned from data derived during the test with optimization approaches. The system works with soft locking principle to prevent damage to tuning screws. In the end, experimental automated tuned filter performance and improvement of tuning iteration times are given.

关键词： Radio frequency Multiplexing Computer architecture Filtering algorithms Fasteners software Sensor systems

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SBGen: A high-performance library for rapid generation of cryptographic S-boxes

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softwareX 2024年 27卷

作者： Kuznetsov, Alexandr Kandii, Serhii Frontoni, Emanuele Poluyanenko, Nikolay Univ Macerata Dept Polit Sci Commun & Int Relat Via Crescimbeni 30-32 I-62100 Macerata Italy eCampus Univ Fac Engn Via Isimbardi 10 I-22060 Novedrate CO Italy V N Karazin Kharkiv Natl Univ Fac Comupter Sci Dept Informat & Commun Syst Secur 4 Svobody Sq UA-61022 Kharkiv Ukraine JSC Inst Informat Technol Bakulin St 12 UA-61166 Kharkiv Ukraine Marche Polytech Univ Dept Informat Engn Via Brecce Bianche 12 I-60131 Ancona Italy

In the realm of cryptographic research, the generation of S-boxes with high nonlinearity and optimal cryptographic properties remains a critical challenge. This paper presents a novel approach to S-box generation, leveraging the strengths of heuristic optimization methods. Through a meticulous integration of Simulated Annealing (SA) and Hill Climbing (HC) algorithms with sophisticated cost functions, we introduce an innovative software tool that significantly advances the efficiency of generating highly nonlinear S-boxes. Our methodology is distinguished by its ability to consistently produce S-boxes that meet stringent security criteria, with a remarkable 100 % success rate and minimized computational overhead. A comparative analysis reveals that our approach outperforms existing methods in terms of the probability of generating target S-boxes and the average number of iterations required. The software's practical implications extend beyond theoretical advancements, offering a valuable resource for cryptographic system designers in their quest to fortify cipher systems against linear and differential cryptanalysis. By setting new benchmarks for nonlinearity and search efficiency, our work paves the way for future research in cryptographic S-box generation, highlighting the potential of combining heuristic techniques with domain-specific cost functions to achieve superior security outcomes.

关键词： S-box generation Cryptographic security Heuristic optimization Simulated annealing

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Partial Multi-label Learning using Label Compression

Partial Multi-label Learning using Label Compression

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IEEE International conference on Data Mining (ICDM)

作者： Tingting Yu Guoxian Yu Jun Wang Carlotta Domeniconi Xiangliang Zhang School of Software Shandong University Jinan China College of Computer and Information Sciences Southwest University Chongqing China Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University Jinan China CEMSE King Abdullah University of Science and Technology SA Department of Computer Science George Mason University VA USA

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Partial multi-label learning (PML) aims at learning a robust multi-label classifier from partial multi-label data, where a sample is annotated with a set of candidate labels, while only a subset of those labels is valid. The existing PML algorithms generally suffer from the high computational cost when learning with large label spaces. In this paper, we introduce a PML approach (PML-LCom) that uses Label Compression to efficiently learn from partial multi-label data. PML-LCom firstly splits the observed label data matrix into a latent relevant label matrix and an irrelevant one, and then factorizes the relevant label matrix into two low-rank matrices, one encodes the compressed labels of samples, and the other explores the underlying label correlations. Next, it optimizes the coefficient matrix of the multi-label predictor with respect to the compressed label matrix. In addition, it regularizes the compressed label matrix with respect to the feature similarity of samples, and optimizes the label matrix and predictor in a coherent manner. Experimental results on both semi-synthetic and real-world PML datasets show that PML-LCom achieves a performance superior to the state-of-the-art solutions on predicting the labels of unlabeled samples with a large label space. The label compression improves both the effectiveness and efficiency, and the coherent optimization mutually benefits the label matrix and predictor.

关键词： Correlation conferences Computational efficiency Classification algorithms Data mining optimization

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Paralleled Genetic Algorithm for Solving the Knapsack Problem in the Cloud

Paralleled Genetic Algorithm for Solving the Knapsack Proble...

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International conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Javid Taheri Shaghayegh Sharif Xing Penju Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Information Technologies University of Sydney Sydney NSW Australia

This paper proposes a Parallel Genetic Algorithm (PGA) framework to solve the Knapsack problem in the Cloud. Our PGA consists of several independent workers that cooperatively run in parallel to find optimal solutions for a Knapsack problem, we chose the Knapsack problem because it is known to be NP-Complete and can be used to motivate other cloud-based solutions for other combinatorial problems too. Although this problem has already been extensively studied in the literature, no cloud-based solution is ever presented for that -- to the best of our knowledge. We used several benchmarks to validate our solutions against the optimal solution computed by dynamic programming. Results are very promising and illustrated reasonable scalability as well as speed up factor for our implementation using Microsoft Azure.

关键词： Biological cells Genetic algorithms Sociology Statistics Electronics packaging software optimization

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Controlling precision stepper motors in flight using (almost) no parts

Controlling precision stepper motors in flight using (almost...

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IEEE conference on Aerospace

作者： David Randall Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

This concept allows control of high-performance stepper motors with minimal parts count and minimal flight software complexity. Although it uses a small number of common flight-qualified parts and simple control algorithms, it is capable enough to meet demanding system requirements. Its programmable nature makes it trivial to implement changes to control algorithms both during integration & test and in flight. Enhancements such as microstepping, half stepping, back-emf compensation, and jitter reduction can be tailored to the requirements of a large variety of stepper motor based applications including filter wheels, focus mechanisms, antenna tracking subsystems, pointing and mobility. The hardware design (using an H-bridge motor controller IC) was adapted from JPL's MER mission, still operating on Mars. This concept has been fully developed and incorporated into the MCS instrument on MRO, currently operating in Mars orbit. It has been incorporated into the filter wheel mechanism and linear stage (focus) mechanism for the AMT instrument. On MCS/MRO, two of these circuits control the elevation and azimuth of the MCS telescope/radiometer assembly, allowing the instrument to continuously monitor the limb of the Martian atmosphere. Implementation on MCS/MRO resulted in a 4:1 reduction in the volume and mass required for the motor driver electronics (100:25 square inches of PCB space), producing a very compact instrument. In fact, all of the electronics for the MCS instrument are packaged within the movable instrument structure. It also saved approximately 3 Watts of power. Most importantly, the design enabled MCS to meet very its stringent maximum allowable torque disturbance requirements.

关键词： Instruments Wheels Mars Control systems Testing Jitter Micromotors Hardware nonlinear filters Circuits

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Latchup-aware placement and parasitic-bounded routing of custom analog cells 93

Latchup-aware placement and parasitic-bounded routing of cus...

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IEEE International conference on Computer-Aided Design

作者： B. Basaran R.A. Rutenbar L.R. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9780818644900

This paper presents new results in constraint-directed placement and routing of device-level analog cells. We describe the first algorithm for latchup-aware device placement that guarantees sufficient placement of well/substrate contacts by simultaneous placement of latchup protection geometry and devices. A novel cost-to-target predictor for cost-based A/sup */ routing, and a new scheme for pruning evolving paths that violate user-supplied constrained routing of analog signals in dense placements are described. Experimental results suggest the strategy avoids the artifacts of length-for-crosstalk trade-offs seen in earlier algorithms, and allows users more fine-grain control of the routing of defense, high-performance cells. An implementation of these ideas in the tool set KA III is used to derive improved layouts from several analog cells.

关键词： Routing Protection Geometry Crosstalk Circuits Design optimization Merging Contacts Silicon software libraries

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Privacy-Preserving Federated Learning for Science: Challenges and Research Directions

Privacy-Preserving Federated Learning for Science: Challenge...

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IEEE International conference on Big Data

作者： Kibaek Kim Krishnan Raghavan Olivera Kotevska Matthieu Dorier Ravi Madduri Minseok Ryu Todd Munson Rob Ross Thomas Flynn Ai Kagawa Byung-Jun Yoon Christian Engelmann Farzad Yousefian Argonne National Laboratory Lemont IL Oak Ridge National Laboratory Oak Ridge TN Arizona State University Tempe AZ Brookhaven National Laboratory Upton NY Rutgers University Piscataway NJ

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper discusses the key challenges and future research directions for privacy-preserving federated learning (PPFL), with a focus on its application to large-scale scientific artificial intelligence models, in particular, foundation models (FMs). PPFL enables collaborative model training across distributed datasets while preserving privacy—an important collaborative approach for science. We discuss the need for efficient and scalable algorithms to address the increasing complexity of FMs, particularly when dealing with heterogeneous clients. In addition, we underscore the need for developing advance privacy-preserving techniques, such as differential privacy, to balance privacy and utility in large FMs emphasizing fairness and incentive mechanisms to ensure equitable participation among heterogeneous clients. Finally, we emphasize the need for a robust software stack supporting scalable and secure PPFL deployments across multiple high-performance computing facilities. We envision that PPFL would play a crucial role to advance scientific discovery and enable large-scale, privacy-aware collaborations across science domains.

关键词： Training Adaptation models Frequency modulation Federated learning Computational modeling Soft sensors software algorithms Collaboration Data models optimization

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