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International Symposium on Antennas and Propagation (ISAP)

作者： Kosaku Shigeta Hiroyoshi Yamada Rhoichi Sato Hiroyuki Kamo Graduate School of Science & Technology Niigata University Niigata Japan TAIYO YUDEN Co. Ltd Japan

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

The main objective of our research is to construct a security system using millimeter-wave 2D-MIMO radar to check the possession of dangerous objects such as knives and guns. In our previous study, to realize 3D imaging by the radar, we used the Beamformer method. The method is based on the three-dimensional Fourier transform (3D-DFT), hence it is time-consuming for analyzing large areas and/or detailed imaging. Another factor that increases the computational time is that the larger the number of virtual elements in the MIMO radar. In security applications of the MIMO radar, targets locate in the near-field region, hence far-field approximation which can speed up calculations is not applicable. Therefore, in this paper we apply the range migration algorithm to the imaging processing by using the T-shaped MIMO radar, and evaluate computational efficiency by the algorithm.

关键词： Three-dimensional displays Fourier transforms MIMO radar Imaging Radar imaging Millimeter wave radar Parallel processing approximation algorithms Radar antennas Security

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Ship Detection and Classification in Adverse Weather Conditions

Ship Detection and Classification in Adverse Weather Conditi...

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International Conference on Computer Science and Engineering (UBMK)

作者： Yahya İzala Yaşar Becerikli Kocaeli Üniversitesi Bilgisayar Mühendisliği Kocaeli Türkiye Adli Bilişim İhtisas Dairesi Adli Tıp Kurumu İstanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The use of Unmanned Aerial Vehicles (UAVs) is quite common today. Equipped with various tools, these vehicles are actively utilized in both civilian and military fields. Due to their high-altitude photography, images captured from UAVs often have a wide field of view. However, adverse weather conditions can reduce visibility, making it impossible for the operator controlling the UAV to see all the details. To analyze the images obtained from the UAV and improve environmental conditions, an automated system is needed. A different approach is required to mitigate the negative effects of weather and to detect small objects, which may appear smaller due to high-altitude captures. In this study, the adverse weather conditions faced by UAVs were identified, and subsequently, the Pix2Pix model was used to improve the results caused by these conditions. Enhancements were made in algorithms that were insufficient for detecting small objects. After correcting for adverse weather conditions, object recognition algorithms such as YOLOv8 and Faster R-CNN were used separately for ship detection and classification. It was found that after correcting for adverse weather conditions. there was an increase in mAP values of approximately 2% with YOLOv8 and 1% with Faster R-CNN.

关键词： Photography Computer science Autonomous aerial vehicles approximation algorithms Classification algorithms Object recognition Marine vehicles Meteorology

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Approximating the Geometric Knapsack Problem in Near-Linear Time and Dynamically

arXiv

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arXiv 2024年

作者： Buchem, Moritz Deuker, Paul Wiese, Andreas Technische Universität München Munich Germany

One important goal in algorithm design is determining the best running time for solving a problem (approximately). For some problems, we know the optimal running time, assuming certain conditional lower bounds. In this paper, we study the d-dimensional geometric knapsack problem in which we are far from this level of understanding. We are given a set of weighted d-dimensional geometric items like squares, rectangles, or hypercubes and a knapsack which is a square or a (hyper-)cube. Our goal is to select a subset of the given items that fit non-overlappingly inside the knapsack, maximizing the total profit of the packed items. We make a significant step towards determining the best running time for solving these problems approximately by presenting approximation algorithms whose running times are near-linear, i.e., O(n · poly(log n)), for any constant d and any parameter ϵ > 0 (the exponent of log n depends on d and 1/ϵ) In the case of (hyper)-cubes, we present a (1 + ϵ)-approximation algorithm. This improves drastically upon the currently best known algorithm which is a (1 + ϵ)-approximation algorithm with a running time of nOϵ,d(1) where the exponent of n depends exponentially on 1/ϵ and d. In particular, our algorithm is an efficient polynomial time approximation scheme (EPTAS). Moreover, we present a (2 + ϵ)-approximation algorithm for rectangles in the setting without rotations and (Equation presented) approximation algorithm if we allow rotations by 90 degrees. The best known polynomial time algorithms for this setting have approximation ratios of (Equation presented) and 1.5+ϵ, respectively, and running times in which the exponent of n depends exponentially on 1/ϵ. In addition, we give dynamic algorithms with polylogarithmic query and update times, having the same approximation guarantees as our other algorithms above. Key to our results is a new family of structured packings which we call easily guessable packings. They are flexible enough to guarantee the ex

关键词： approximation algorithms

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The Indistinguishability Query

The Indistinguishability Query

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International Conference on Data Engineering

作者： Ashwin Lall Department of Computer Science Denison University Granville OH US

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

We propose the indistinguishability query for iden-tifying all of a user's near-optimal tuples. This query returns all the tuples that are at most a small fraction away from the optimal of the user's unknown utility function. This is motivated by the idea that users can have a hard time distinguishing very similar tuples and in fact even tuples that are slightly inferior in the identified criteria may have additional characteristics that make them more attractive to the user. In order to perform this query without knowledge of the user's utility function, we use a simple interactive framework that asks the user to perform a modest number of comparisons to narrow down their utility function. We show that the indistinguishability query cannot be approximated solely with real tuples in the database and thus our algorithms with provable bounds must present the user with artificial tuples. We also give heuristic algorithms that show the user only real tuples from the database. Since the user may make errors while performing comparisons, we generalize our algorithms to account for user error as well. Experiments on synthetic and real data sets show that the indistinguishability query can be performed accurately while asking the user to compare a small number of tuples.

关键词： Databases Heuristic algorithms approximation algorithms Data engineering

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A Simple LP-Based approximation Algorithm for the Matching Augmentation Problem

arXiv

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arXiv 2022年

作者： Bamas, Étienne Drygala, Marina Svensson, Ola EPFL Switzerland

The Matching Augmentation Problem (MAP) has recently received significant attention as an important step towards better approximation algorithms for finding cheap 2-edge connected subgraphs. This has culminated in a 5/3-approximation algorithm. However, the algorithm and its analysis are fairly involved and do not compare against the problem's well-known LP relaxation called the cut LP. In this paper, we propose a simple algorithm that, guided by an optimal solution to the cut LP, first selects a DFS tree and then finds a solution to MAP by computing an optimum augmentation of this tree. Using properties of extreme point solutions, we show that our algorithm always returns (in polynomial time) a better than 2-approximation when compared to the cut LP. We thereby also obtain an improved upper bound on the integrality gap of this natural relaxation. Copyright © 2022, The Authors. All rights reserved.

关键词： approximation algorithms

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Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator approximation

Iterative Learning and Model Predictive Control for Repetiti...

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IEEE Conference on Decision and Control

作者： M. Bahadır Saltık Bayu Jayawardhana Ashish Cherukuri Faculty of Science and Engineering University of Groningen Groningen The Netherlands

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper presents an iterative way of computing a control algorithm with the aim of enabling reference tracking for an unknown nonlinear system. The method consists of three blocks: iterative learning control (ILC), robust model predictive control (MPC), and a linear approximation of the Koopman operator. The method proceeds in iterations, where at the end of an iteration, two steps are performed. First, the trajectories of the system obtained from previous iterations are used to build the linear approximation of the Koopman operator. Second, the linear model is used to compute the ILC signal. While these steps are executed in an offline manner, during an iteration, the control actions are computed online using the robust tubebased MPC. The tubes are defined by constraint tightening sets that compensate for the discrepancy between the true dynamics and its linear approximation. We demonstrate our method on the reference tracking for a 4 tank system.

关键词： Computational modeling Linear approximation Prediction algorithms approximation algorithms Iterative algorithms Trajectory Nonlinear dynamical systems

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Non-stationary Bandits with Habituation and Recovery Dynamics and Knapsack Constraints

Non-stationary Bandits with Habituation and Recovery Dynamic...

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IEEE Conference on Decision and Control

作者： Qinyang He Yonatan Mintz Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Multi-armed bandit models have proven to be useful in modeling many real world problems in the areas of control and sequential decision making with partial information. However, in many scenarios, such as those prevalent in healthcare and operations management, the decision maker’s expected reward will decrease if an action is selected too frequently while it may recover if they abstain from selecting this action. This scenario is further complicated when choosing a particular action also expends a random amount of a limited resource where the distribution is also initially unknown to the decision maker. In this paper we study a class of models that address this setting that we call reducing or gaining unknown efficacy bandits with stochastic knapsack constraints (ROGUEwK). We propose a combination upper confidence bound (UCB) and lower confidence bound (LCB) approximation algorithm for optimizing this model. Our algorithm chooses which action to play at each time point by solving a linear program (LP) with the UCB for the average rewards and LCB for the average costs as inputs. We show that the regret of our algorithm is sub-linear as a function of time and total constraint budget when compared to a dynamic oracle. We validate the performance of our algorithm against existing state of the art non-stationary and knapsack bandit approaches in a simulation study and show that our methods are able to on average achieve a $\mathbf{1 3 \%}$ improvement in terms of total reward.

关键词： Costs Heuristic algorithms Decision making Stochastic processes Medical services approximation algorithms Time factors

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AGV Path Planning Algorithm Based on Improved DQN

AGV Path Planning Algorithm Based on Improved DQN

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ge Sun Rui Zhang Peng Xia Jingwen Xu School of Automobile and Transportation Tianjin University of Technology and Education Tianjin China Tianjin Dolphin Intelligent Driving Technology Co. Ltd

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In the context of rapid growth in the logistics and e-commerce industries, improving the efficiency of automated warehousing systems, particularly in sorting and throughput, has become crucial. Automated guided vehicles (AGVs) have increasingly replaced human workers in handling and sorting tasks, leading to significant improvements in efficiency and accuracy. Path planning, a core AGV technology, plays a vital role in optimizing logistics operations and reducing transportation costs. Common path planning algorithms, such as Dijkstra and A*, each have strengths and limitations. While Dijkstra’s algorithm guarantees finding the shortest path by exploring all possible nodes, it suffers from high time and space complexity. A*, though more efficient, sometimes compromises optimality by neglecting certain nodes. Current algorithms overlook the impact of excessive turns, which increases travel time. Deep reinforcement learning (DRL), by combining deep learning and reinforcement learning, offers a promising solution to path planning in dynamic, complex environments. DRL can make intelligent decisions without pre-constructed maps, adapting to the environment through training. In this paper, an improved DQN-based algorithm for AGV path planning is proposed, introducing the concepts of a turning factor and a smoothness factor to address the time cost of turns. By enhancing DQN’s state and reward design, the algorithm minimizes the number of turns while maintaining the shortest possible path. Experimental results demonstrate that this approach outperforms traditional A*, modified A*, and standard DQN algorithms, reducing transport time by approximately 21.3% compared to the best-performing baseline, the improved A* algorithm.

关键词： Costs Heuristic algorithms Warehousing Transportation approximation algorithms Turning Path planning Vehicle dynamics Sorting Logistics

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NLOS Satellites Detection Method Applying Relative Positioning

NLOS Satellites Detection Method Applying Relative Positioni...

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IEEE Region 10 International Conference TENCON

作者： Ryuga Hayashi Yukihiro Kubo Department of Electronical and Electronic Engineering Ritsumeikan University Shiga Japan

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

In this paper, we present a detection method for non-line-of-sight (NLOS) satellites applying relative positioning. NLOS satellites affect the accuracy of positioning, and conventional approach to reduce the effect of multipath cannot improve the low accuracy. So, we propose the method to apply relative positioning. This method does not need some special devices or complex computations. In this paper, the observe at the fixed point and open sky condition is carried out to simplify the verification. The NLOS satellite was simulated by modifying the information obtained from the line-of-sight (LOS) satellite, and this method was applied. As a result, the NLOS satellite was successfully detected in approximately 98% of the simulation time.

关键词： Satellites Accuracy Line-of-sight propagation approximation algorithms IEEE Regions

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Secrecy Capacity Analyses and Trajectory Optimization for IRS-UAV Networks

Secrecy Capacity Analyses and Trajectory Optimization for IR...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Mustafa Namdar Arif Basgumus Nurullah Calik Hakan Alakoca Lutfiye Durak-Ata Electrical and Electronics Eng. Dept. Kutahya Dumlupinar University Kutahya Turkey Electrical and Electronics Eng. Dept. Bursa Uludag University Bursa Turkey Biomedical Eng. Dept. Istanbul Medeniyet University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

In this study, an intelligent reflective surface (IRS)-assisted communication system has been investigated from the perspective of secrecy capacity analysis. Analyses have been conducted in the presence of a group of unmanned aerial vehicles (UAVs) acting as covert listeners, aiming to eavesdrop on legitimate user information in the transmission environment. Additionally, trajectory optimization has been explored in a scenario integrated with UAVs and IRS, evaluating the impact of the determined optimum trajectory on the physical layer security performance. With the Multiple Penalized Principal Curves (MPPC) algorithm, it is concluded that secrecy capacity is increased by approximately 4 dB for the scenario involving IRS-UAV networks.

关键词： Privacy Transmitting antennas Signal processing algorithms Physical layer security Signal processing Autonomous aerial vehicles approximation algorithms

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