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A Novel Design Methodology for Constructing Accurate Models of Microwave Channel Utilizing EDA Softwares

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A Novel Design Methodology for Constructing Accurate Models ...

IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Tiecheng Wu Wei Zhang Jian Li Shuangfei Yin Nanjing Institute of Electronic Equipment Nanjing China Beijing Institute of Control and Electronic Technology Beijing China Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665418294

A novel design methodology for constructing accurate models of microwave channel utilizing Electronic Design Automation (EDA) softwares is introduced. The two cases of filter channel are firstly presented, then, the electromagnetic field simulation is detailedly demonstrated. Simultaneously, field-circuit co-simulation utilizing EDA softwares is gived. The EDA softwares include Advanced Design System (ADS), computer Simulation technology (CST), Altium Designer (AD) and so on. The out-band rejections of the second case are 20.16dBc@0.9GHz and 10.82dBc@10GHz better than the first one. Finally, the experimental curves are depicted, which are in excellent agreement with the simulation. With the practical engineering value, a novel design methodology applied in the microwave channel is given, which can be also used in millimeter-wave circuit, making the precise analysis of the filter channel come true.

关键词： Microwave measurement Technological innovation Millimeter wave circuits Computational modeling Prototypes Microwave circuits Software

Contract Composition for Dynamical control Systems: Definition and Verification using Linear Programming

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arXiv 2022年

作者： Sharf, Miel Besselink, Bart Johansson, Karl Henrik Division of Decision and Control Systems Kth Royal Institute of Technology Digital Futures Stockholm10044 Sweden Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9700 AK Netherlands

Designing large-scale control systems to satisfy complex specifications is hard in practice, as most formal methods are limited to systems of modest size. Contract theory has been proposed as a modular alternative to formal methods in control, in which specifications are defined by assumptions on the input to a component and guarantees on its output. However, current contract-based methods for control systems either prescribe guarantees on the state of the system, going against the spirit of contract theory, or can only support rudimentary compositions. In this paper, we present a contract-based modular framework for discrete-time dynamical control systems. We extend the definition of contracts by allowing the assumption on the input at a time k to depend on outputs up to time k -1, which is essential when considering the feedback connection of an unregulated dynamical system and a controller. We also define contract composition for arbitrary interconnection topologies, under the pretence of well-posedness, and prove that this notion supports modular design, analysis and verification. This is done using graph theory methods, and specifically using the notions of topological ordering and backward-reachable nodes. Lastly, we use k-induction to present an algorithm for verifying vertical contracts, which are claims of the form "the conjugation of given component-level contracts is a stronger specification than a given contract on the integrated system". These algorithms are based on linear programming, and scale linearly with the number of components in the interconnected network. A numerical example is provided to demonstrate the scalability of the presented approach, as well as the modularity achieved by using it. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear programming

PoRF: Proof-of-Reputation-based Consensus Scheme for Fair Transaction Ordering 13

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PoRF: Proof-of-Reputation-based Consensus Scheme for Fair Tr...

13th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2021

作者： Shyamsukha, Sidharth Bhattacharya, Pronaya Patel, Farnazbanu Tanwar, Sudeep Gupta, Rajesh Pricop, Emil Nirma University Dept. of Computer Science and Engg. AhmedabadGujarat India Institute of Technology Nirma University Dept. of Computer Science and Engg. AhmedabadGujarat India Petroleum-Gas University of Ploiesti Automatic Control Comp. and Elect. Dept Romania

ISBN: (纸本)9781665425346

Blockchain (BC)-driven applications ensure trust and transparency among multiple stakeholders through different consensus mechanisms. In consensus mechanisms, a dishonest node (or application), might collude with peer nodes, or miners, to prioritize its transactions over other node transactions, thereby reducing its latency and improving its quality-of-service (QoS). To address the above limitation, this paper proposes a consensus scheme, PoRF, based on a reputation-based consensus scheme that allows fair and random transaction selection. The scheme proposes that a sharded BC is considered, managed through shard managers (SM), to address latency and bandwidth issues. A reputation coefficient score is generated, and a reward-penalty setup is considered for nodes based on honest, or dishonest transaction proposals. Based on the reward-penalty outcome, the reputation score for nodes is modified. The scheme is compared with fixed transaction ordering scheme, Helix scheme, and practical byzantine fault tolerance (PBFT) consensus for transaction fairness, transaction time, and epochs required for consensus formations. The scheme outperforms the considered schemes for the considered parameters. For example, for 50% dishonest nodes, an improvement of 70 % is recorded against a fixed transaction scheme, and at 4000 nodes, the proposed scheme takes 130 milliseconds (ms) less for the execution of transactions. For consensus formation, at 250 nodes, PBFT takes 167 epochs for consensus, compared to 24 epochs in the proposed scheme, which indicates the scheme's viability in real-world setups. © 2021 IEEE.

关键词： Blockchain

Real-time Seamless Image Stitching in Autonomous Driving

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Real-time Seamless Image Stitching in Autonomous Driving

International Conference on Information Fusion

作者： Christian Kinzig Irene Corté s Carlos Ferná ndez Martin Lauer Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department of Computer Science and Engineering Universidad Carlos III de Madrid (UC3M) Madrid Spain

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Autonomous vehicles depend on an accurate perception of their surroundings. For this purpose, different approaches are used to detect traffic participants such as cars, cyclists, and pedestrians, as well as static objects. A commonly used method is object detection and classification in camera images. However, due to the limited field of view of camera images, detecting in the entire environment of the ego-vehicle is an additional challenge. Some solutions include the use of catadioptric cameras or clustered surround view camera systems that require a large installation height. In multi-camera setups, an additional step is required to merge objects from overlapping areas between cameras. As an alternative to these systems, we present a real-time capable image stitching method to improve the horizontal field of view for object detection in autonomous driving. To do this, we use a spherical camera model and determine the overlapping area of the neighboring images based on the calibration. Furthermore, lidar measurements are used to improve image alignment. Finally, seam carving is applied to optimize the transition between the images. We tested our approach on a modular redundant sensor platform and on the publicly available nuScenes dataset. In addition to qualitative results, we evaluated the stitched images using an object detection network. Moreover, the real-time capability of our image stitching method is shown in a runtime analysis.

关键词： Runtime Laser radar Pipelines Merging Object detection Cameras Real-time systems

Suppressing the endemic equilibrium in SIS epidemics: A state dependent approach

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arXiv 2021年

作者： Wang, Yuan Gracy, Sebin Ishii, Hideaki Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology

This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure and focuses on the effect of social distancing to regulate the epidemic level. We demonstrate that if each subpopulation is informed of its infection rate and reduces interactions accordingly, the fraction of the subpopulation infected stays below half for all time instants. To this end, we first modify the basic SIS model by introducing a state dependent parameter representing the frequency of interactions between subpopulations. Thereafter, we show that for this modified SIS model, the spectral radius of a suitably-defined matrix being not greater than one causes all the agents, regardless of their initial sickness levels, to converge to the healthy state;assuming non-trivial disease spread, the spectral radius being greater than one leads to the existence of a unique endemic equilibrium, which is also asymptotically stable. Finally, by leveraging the aforementioned results, we show that the fraction of (sub)populations infected never exceeds half. Copyright © 2021, The Authors. All rights reserved.

关键词： Epidemiology

Distributed Optimal Data Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks

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Distributed Optimal Data Allocation in Finite Time with Effi...

IEEE Conference on Decision and control

作者： Apostolos I. Rikos Christoforos N. Hadjicostis Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Digital Futures Stockholm Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm, which operates over a dynamically changing network and allows each node to calculate the optimal allocation of data in a finite number of time steps. We show that our proposed algorithm (i) converges to the optimal solution in finite time with very high probability, and (ii) once the optimal solution is reached, each node is able to cease transmissions without needing knowledge of a global parameter such as the network diameter. Furthermore, our algorithm (i) operates exclusively with quantized values (i.e., each node processes and transmits quantized information), (ii) relies on event-driven updates, and (iii) calculates the optimal solution in the form of a quantized fraction which avoids errors due to quantization. Finally, we demonstrate the operation, performance, and potential advantages of our algorithm over random dynamic networks.

关键词： Wireless communication Knowledge engineering Quantization (signal) Heuristic algorithms Distributed databases Dynamic scheduling computer networks

Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication

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arXiv 2022年

作者： Rikos, Apostolos I. Charalambous, Themistoklis Hadjicostis, Christoforos N. Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorithms which rely on quantized operation (i.e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps. Furthermore, during the operation of our algorithms, each node is able to determine in a distributed manner whether convergence has been achieved and thus terminate its operation. To the best of the authors’ knowledge these algorithms are the first to find the exact solutions under quantized communication (i.e., there is no error in the final calculation). Additionally, note that our network size calculation algorithm is the first in the literature which calculates the exact size of a network in a finite number of steps without introducing a final error. This error in other algorithms can be either due to quantization or asymptotic convergence. In our case, no error is introduced since the desired result is calculated in the form of a fraction involving a quantized numerator and a quantized denominator. Finally, we demonstrate the operation of our algorithms and their potential advantages. © 2022, CC BY.

关键词： Errors

Lazy Probabilistic Roadmaps Revisited

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arXiv 2022年

作者： Ramirez, Miquel Selvaratnam, Daniel Manzie, Chris School of Electric and Electronic Engineering University of Melbourne Australia Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

This paper describes a revision of the classic Lazy Probabilistic Roadmaps algorithm (Lazy PRM), that results from pairing PRM and a novel Branch-and-Cut (BC) algorithm. Cuts are dynamically generated constraints that are imposed on minimum cost paths over the geometric graphs selected by PRM. Cuts eliminate paths that cannot be mapped into smooth plans that satisfy suitably defined kinematic constraints. We generate candidate smooth plans by fitting splines to vertices in minimum-cost path. Plans are validated with a recently proposed algorithm that maps them into finite traces, without need to choose a fixed discretization step. Trace elements exactly describe when plans cross constraint boundaries modulo arithmetic precision. We evaluate several planners using our methods over the recently proposed BARN benchmark, and we report evidence of the scalability of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Combinatorial optimization

Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

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arXiv 2024年

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence Anhui University Hefei230031 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Computer Engineering Indiana University Purdue University IndianapolisIN46202 United States

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. Copyright © 2024, The Authors.

关键词： Forecasting