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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhengguang Xu Xiaoxu Liu School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Conventional imaging algorithms, such as the Back Projection Algorithm (BPA) and the Range Migration Algorithm (RMA), have their own respective limitations in terms of computational complexity and efficiency. While BPA delivers high-resolution imaging, it demands significant computational resources. In contrast, RMA is more computationally efficient but sacrifices some precision. To address these challenges, a new near-field imaging algorithm called FastBPA is introduced. It builds on BPA and leverages the Fast Fourier Transform (FFT) for faster imaging, similar to RMA. A comparison of the computational complexities of RMA, BPA, and FastBPA shows that FastBPA reduces the time complexity of BPA imaging from Ο (N 2 L 2 ) to Ο (NLlog(NL)), making it equivalent to that of RMA. Experimental results from the 3DRIED dataset demonstrate that FastBPA matches BPA in focusing accuracy.

关键词： Image quality Fast Fourier transforms Signal processing algorithms Imaging Radar imaging approximation algorithms Radar signal processing Radar polarimetry Computational efficiency Time complexity

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Valued Neighborhood Search for Determination of Pareto-Optimal Solutions

Valued Neighborhood Search for Determination of Pareto-Optim...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Marek Kvet Jaroslav Janácek Michal Kvet Faculty of Management Science and Informatics University of Zilina Zilina Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Classical version of the neighborhood search based on exchange operation tests objective function of solution obtained by the exchange operation and depending on the objective function value decides on further progress. When a series of Pareto-optimal solutions of the p-location problem is determined, huge number of such operations must be tested and evaluated. As many of exchanges are repeated with the same consequence for the further progress, it is questionable whether the previously obtained information about their success can be taken into account in the decision process. In this contribution, we focus on answering this question. We perform the associated research of influence of information about previous success or failure on effectiveness of the neighborhood search application to determination of a set of Pareto-optimal solutions.

关键词： Costs Decision making Transportation Search problems Linear programming approximation algorithms Informatics System analysis and design Machine intelligence

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Flood Risk Assessment of Bihar State of India using Sentinel-1 on Google Earth Engine

Flood Risk Assessment of Bihar State of India using Sentinel...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Rajeev Ranjan Pandey Dhirendra Pratap Singh Jaytrilok Choudhary Nikhil Nigam Department of CSE Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The devastating impact of floods could result in long-term economic consequences, property damage, mortality, and disruptions to essential services. Flood risk assessment is crucial to the solution of these problems because it provides valuable information about the potential frequency and magnitude of flooding episodes. Through a rigorous examination of variables such as geography, hydrology, land use, and climatic changes, flood risk assessment determines how susceptible populations and assets are to flooding. This method assists in locating areas that are vulnerable to flooding. With this information at hand, policymakers may improve catastrophe planning and response procedures, create informed plans for reducing the effects of floods, and allocate resources more effectively to reduce their effects. In the proposed method, the otsu and difference thresholding methods are being used to calculate the population affected by the flood. The model is 92.3% accurate in predicting the locations of the 12 affected districts. The resulting flood map may then be coupled with demographic statistics.

关键词： Earth Accuracy Sentinel-1 approximation algorithms Internet Sensors Satellite images Floods Risk management Engines

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Fairness and Efficiency in Two-Sided Matching Markets

arXiv

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arXiv 2025年

作者： Jain, Pallavi Jha, Palash Solanki, Shubham Indian Institute of Technology Jodhpur India

We propose a new fairness notion, motivated by the practical challenge of allocating teaching assistants (TAs) to courses in a department. Each course requires a certain number of TAs and each TA has preferences over the courses they want to assist. Similarly, each course instructor has preferences over the TAs who applied for their course. We demand fairness and efficiency for both sides separately, giving rise to the following criteria: (i) every course gets the required number of TAs and the average utility of the assigned TAs meets a threshold;(ii) the allocation of courses to TAs is envy-free, where a TA envies another TA if the former prefers the latter’s course and has a higher or equal grade in that course. Note that the definition of envy-freeness here differs from the one in the literature, and we call it merit-based envy-freeness. We show that the problem of finding a merit-based envy-free and efficient matching is NP-hard even for very restricted settings, such as two courses and uniform valuations;constant degree, constant capacity of TAs for every course, valuations in the range {0, 1, 2, 3}, identical valuations from TAs, and even more. To find tractable results, we consider some restricted instances, such as, strict valuation of TAs for courses, the difference between the number of positively valued TAs for a course and the capacity, the number of positively valued TAs/courses, types of valuation functions, and obtained some polynomial-time solvable cases, showing the contrast with intractable results. We further studied the problem in the paradigm of parameterized algorithms and designed some exact and approximation algorithms. © 2025, CC BY.

关键词： approximation algorithms

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Enhanced Sand Cat Swarm Optimization to Achieving Maximum Coverage for Wireless Sensor Networks

Enhanced Sand Cat Swarm Optimization to Achieving Maximum Co...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： J Elumalai K. Kumuthapriya R. Hema P. Sakthi Shunmuga Sundaram Department of Electronics and Communication Engineering S.K.R Engineering College Ponamallee Chennai Department of Electronics and Communication Engineering Tagore Engineering College Rathinamangalam Chennai Tamil Nadu

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

When it comes to ensuring the effective operation of Wireless Sensor Networks (WSNs), it is essential to achieve total coverage of the region that is being targeted. However, the random initial deployment of sensor nodes often leads to inefficient positioning, resulting in coverage gaps. To mitigate this issue, an improved algorithm based on the Optimization of the Virtual Force-Directed Enhanced Sand Cat Swarm (SCSO) is introduced to optimize sensor node placement, thereby reducing coverage voids and enhancing overall network coverage. This approach enhances the natural hunting behavior of sand cats and integrates a nonlinear convergence mechanism to improve their sensitivity. Moreover, the algorithm incorporates a virtual resultant force as a perturbation factor during node position updates, accelerating convergence and enhancing optimization efficiency. Simulation results, based on the deployment of 30 sensor nodes within a 60 m × 80 m area, demonstrate that the SCSO algorithm that was proposed is capable of achieving a coverage rate (CR) of approximately 99.61%, reflecting a 29.3% increase over the baseline. Additionally, the algorithm maintains superior coverage performance across different sensor node densities and monitoring region sizes.

关键词： Wireless sensor networks Sensitivity Perturbation methods Simulation Force Clustering algorithms Quality of service approximation algorithms Optimization Convergence

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Stabilizer Ranks, Barnes Wall Lattices and Magic Monotones

arXiv

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arXiv 2025年

作者： Kalra, Amolak Ratan Sinha, Pulkit Institute for Quantum Computing David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada Perimeter Institute for Theoretical Physics WaterlooON Canada

In 2024, Kliuchnikov and Schönnenbeck showed a connection between the Barnes Wall lattices, stabilizer states and Clifford operations. In this work, we study their results and relate them to the problem of lower bounding stabilizer ranks. We show the first quantitative lower bound on stabilizer fidelity as a function of stabilizer ranks, which reproduces the linear-by-log lower bound for χδ(|H>☉n), i.e., on the approximate stabilizer rank of |H>☉n. In fact, we show that the lower bound holds even when the fidelity between the approximation and |H>☉n is exponentially small, which is currently the best lower bound in this regime. Next, we define a new magic monotone for pure states, the Barnes Wall norm, and its corresponding approximate variant. We upper bound these monotones by the CScount of state preparation, and also by the stabilizer ranks. In particular, the upper bound given by the CS-count is tight, in the sense that we exhibit states that achieve the bound. Apart from these results, we give a Fidelity Amplification algorithm, which provides a trade-off between approximation error and the stabilizer rank. As a corollary, it gives us a way to compose approximate stabilizer decompositions into approximate decompositions of their tensor products. Finally, we provide an alternate, elementary proof of the existence and density of product states with maximal stabilizer ranks, which was first proven by Lovitz and Steffan (2022), where they used results from algebraic geometry. © 2025, CC BY.

关键词： approximation algorithms

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Ground-Density Clustering for Approximate Agricultural Field Segmentation

Ground-Density Clustering for Approximate Agricultural Field...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Henry J. Nelson Nikolaos Papanikolopoulos Department of Computer Science and Engineering University of Minnesota

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Instance and semantic segmentation form the backbone of robotic perception and are crucial to many tasks. While most research in the area focuses on improving segmentation quality metrics, there are plenty of applications where approximate methods are adequate as long as they are fast, especially in applications with large amounts of data like precision agriculture. In order to apply the recent successes of machine learning and computer vision on a large scale using robotics, efficient and general algorithms must be designed to intelligently split point clouds into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper, we capitalize on a similarity between the current state-of-the-art for roughly segmenting corn plants and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a novel algorithm, Ground-Density Quickshift++, with the goal of producing a general and scalable field segmentation algorithm that segments individual plants and their stems. This algorithm produces quantitatively better results than the current state-of-the-art on both plant separation and stem segmentation while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithm should work as a drop-in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.

关键词： Precision agriculture Machine learning algorithms Semantic segmentation Plants (biology) Scalability Clustering algorithms approximation algorithms Throughput Time complexity Synthetic data

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Sparsity via Sparse Group k-max Regularization

Sparsity via Sparse Group k-max Regularization

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American Control Conference (ACC)

作者： Qinghua Tao Xiangming Xi Jun Xu Johan A.K. Suykens STADIUS ESAT Leuven KU Belgium Zhejiang Lab Hangzhou Zhejiang China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

For the linear inverse problem with sparsity constraints, the $l_{0}$ regularized problem is NP-hard, and existing approaches either utilize greedy algorithms to find almost optimal solutions or approximate the $l_{0}$ regularization with its convex counterparts. In this paper, we propose a novel regularization technique, namely the sparse group $k$ -max regularization. This approach has the advantages of enhancing both the group-wise and in-group sparsity without imposing any additional constraints on the magnitude of variables in each group. Our approach approximates the $l_{0}$ norm more closely than other relaxation methods, making it especially important for problems with variables at varying scales. We have also established an iterative soft thresholding algorithm and have provided local optimality conditions and complexity analysis. We have tested our method with numerical experiments on both synthetic and real-world datasets, and our results show that our technique is effective and flexible.

关键词： Greedy algorithms Inverse problems approximation algorithms Relaxation methods Iterative algorithms Complexity theory

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Experimental Validation of User Experience-Focused Dynamic Onboard Service Orchestration for Software Defined Vehicles

Experimental Validation of User Experience-Focused Dynamic O...

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International Conference on Intelligent Transportation

作者： Pierre Laclau Stéphane Bonnet Bertrrand Ducourthial Trista Lin Xiaoting Li Heudiasyc CNRS Université de Technologie de Compiègne France Stellantis Poissy France

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In response to the growing need for dynamic software features in automobiles, Software Defined Vehicles (SDVs) have emerged as a promising solution. They integrate dynamic onboard service management to handle the large variety of user-requested services during vehicle operation. Allocating onboard resources efficiently in this setting is a challenging task, as it requires a balance between maximizing user experience and guaranteeing mixed-criticality Quality-of-Service (QoS) network requirements. Our previous research introduced a dynamic resource-based onboard service or-chestration algorithm. This algorithm considers real-time in-vehicle and V2X network health, along with onboard resource constraints, to globally select degraded modes for onboard applications. It maximizes the overall user experience at all times while being embeddable onboard for on-the-fly decision-making. A key enabler of this approach is the introduction of the Automotive eXperience Integrity Level (AXIL), a metric expressing runtime priority for non-safety-critical applications. While initial simulation results demonstrated the algorithm's effectiveness, a comprehensive performance assessment would greatly contribute in validating its industrial feasibility. In this current work, we present experimental results obtained from a dedicated test bench. These results illustrate, validate, and assess the practicality of our proposed solution, providing a solid foundation for the continued advancement of dynamic onboard service orchestration in SDVs.

关键词： Runtime Heuristic algorithms Software algorithms Quality of service Dynamic scheduling approximation algorithms User experience Software Vehicle dynamics Vehicle-to-everything

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Benchmarking Quantum Image Representations algorithms for Hybrid-Quantum applications

Benchmarking Quantum Image Representations Algorithms for Hy...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Abhishek Tiwari Saiyam Sakhuja Britant CDAC Noida Uttar Pradesh India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

This paper examines Quantum Image Representation algorithms (QIRA), which have significantly advanced the encoding and manipulation of visual data using quantum computing. It explores key developments in image representation techniques, focusing on Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation (NEQR), Generalized Quantum Image Representation (GQIR), Bit Plane Representation of Quantum Images (BRQI), and Order Encoding Quantum Image Model (OQIM). Using the Qiskit framework, these methods are implemented to encode classical images into quantum states and perform various quantum operations. The paper provides a comprehensive understanding of QIRA by outlining the background of these representation techniques and rigorously evaluating their performance in image reconstruction. The evaluation is based on metrics such as Mean Squared Error (MSE), Cosine Similarity, Normalized Cross-Correlation (NCC), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR).

关键词： Measurement Visualization Quantum computing PSNR Image coding Image representation Quantum state approximation algorithms Indexes Image reconstruction

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