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Reduced-Rank Estimation for Ill-Conditioned Stochastic Linear Model with High Signal-to-Noise Ratio

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arXiv 2024年

作者： Piotrowski, Tomasz Yamada, Isao Department of Informatics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Torun87-100 Poland Interdisciplinary Center of Modern Technologies Nicolaus Copernicus University Wilenska 4 Torun87-100 Poland Department of Communications and Computer Engineering Tokyo Institute of Technology Tokyo152-8550 Japan

Reduced-rank approach has been used for decades in robust linear estimation of both deterministic and random vector of parameters in linear model y = Hx+√ϵn. In practical settings, estimation is frequently performed under incomplete or inexact model knowledge, which in the stochastic case significantly increases mean-square-error (MSE) of an estimate obtained by the linear minimum mean-square-error (MMSE) estimator, which is MSE-optimal among linear estimators in the theoretical case of perfect model knowledge. However, the improved performance of reduced-rank estimators over MMSE estimator in estimation under incomplete or inexact model knowledge has been established to date only by means of numerical simulations and arguments indicating that the reduced-rank approach may provide improved performance over MMSE estimator in certain settings. In this paper we focus on the high signal-to-noise ratio (SNR) case, which has not been previously considered as a natural area of application of reduced-rank estimators. We first show explicit sufficient conditions under which familiar reduced-rank MMSE and truncated SVD estimators achieve lower MSE than MMSE estimator if singular values of array response matrix H are perturbed. We then extend these results to the case of a generic perturbation of array response matrix H, and demonstrate why MMSE estimator frequently attains higher MSE than reduced-rank MMSE and truncated SVD estimators if H is ill-conditioned. The main results of this paper are verified in numerical simulations. © 2024, CC BY-NC-ND.

关键词： Signal to noise ratio

BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision

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arXiv 2024年

作者： Zhao, Xin Hu, Shiyu Wang, Yipei Zhang, Jing Hu, Yimin Liu, Rongshuai Ling, Haibin Li, Yin Li, Renshu Liu, Kun Li, Jiadong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Instrument Science and Engineering Southeast University Nanjing China Suzhou Institute of Nano-tech and Nano-bionics Chinese Academy of Sciences Suzhou China Department of Computer Science Stony Brook University Stony Brook United States Biostatistics & Medical Informatics Computer Sciences University of Wisconsin-Madison Madison United States

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone – the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack [1]) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry

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Image Acquisition and Deep Convolutional Neural Network Inco...

Moratuwa engineering Research Conference (MERCon)

作者： Dinithi Bhagya Weerasekara Nipun Shantha Kahatapitiya Nuwan Sanjeewa Vimukthi Madushan Wathudura Nuwan Prasanna Deshan Kalupahana Udaya Wijenayake Bhagya Nathali Silva Akila Wijethunge Ruchire Eranga Wijesinghe Department of Materials and Mechanical Technology Faculty of Technology University of Sri Jayewardenepura Pitipana Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact.

关键词： Industries YOLO Costs Accuracy Plantations Crops Real-time systems Convolutional neural networks Monitoring Diseases

Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis

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arXiv 2023年

作者： Ahmed, Aram M. Rashid, Tarik A. Hassan, Bryar A. Majidpour, Jaffer Noori, Kaniaw A. Rahman, Chnoor Maheadeen Abdalla, Mohmad Hussein Qader, Shko M. Tayfor, Noor Mohammed, Naufel B. Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Computer Science University of Raparin Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity. © 2023, CC BY.

关键词： Optimization

Investigating Efficacy of Perplexity in Detecting LLM-Generated Code

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arXiv 2024年

作者： Xu, Jinwei Zhang, He Yang, Yanjin Cheng, Zeru Lyu, Jun Liu, Bohan Zhou, Xin Yang, Lanxin Bacchelli, Alberto Chiam, Yin Kia Chiew, Thiam Kian State Key Laboratory of Novel Software Technology Software Institute Nanjing University Nanjing210008 China Department of Informatics University of Zürich Zürich8006 Switzerland Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Large language model-generated code (LLMgCode) has become increasingly prevalent in software development. Many studies report that LLMgCode has more quality and security issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully checked. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method (PERPLEXITY for short) is the state-of-the-art method. However, the efficacy evaluation of PERPLEXITY has focused on the detection accuracy. In this article, we are interested in whether PERPLEXITY is good enough in a wider range of realistic evaluation settings. To this end, we devise a large-scale dataset that includes 11,664 HaCode snippets and 13,164 LLMgCode snippets, and based on that, we carry out a family of experiments to compare PERPLEXITY against feature-based and pre-training-based methods from three perspectives: (1) detection accuracy in terms of programming language, degree of difficulty, and scale of solution, (2) generalization capability, and (3) inference efficiency. The experimental results show that PERPLEXITY has the best generalization capability while it has low accuracy and efficiency in most cases. Based on the experimental results and detection mechanism of PERPLEXITY, we discuss implications into both the strengths and limitations of PERPLEXITY, e.g., PERPLEXITY is unsuitable for high-level programming languages while it has good interpretability. As the first large-scale investigation on detecting LLMgCode from HaCode, this article provides a wide range of evidence for future improvement. Copyright © 2024, The Authors. All rights reserved.

关键词： High level languages

Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making

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Improving Communication Systems Through Federated Learning: ...

IEEE World Symposium on Communication engineering (WSCE)

作者： Konstantinos D. Stergiou Yutaka Ishibashi Sotirios K. Goudos Thomas Lagkas Panagiotis Sarigiannidis Konstantinos E. Psannis Department of Applied Informatics University of Macedonia Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Computer Science International Hellenic University Kavala Greece Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems.

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UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Communications Based on DRL

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UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Commu...

IEEE Conference on Vehicular technology (VTC)

作者： Jiawei Hu Ying Ju Haoyu Wang Lei Liu Qingqi Pei Y. G. Shee Xiaojie Zhu Celimuge Wu Guangzhou Institute of Technology Xidian University Guangzhou China Department of Electrical Engineering and Computer Science University of California Irvine CA USA Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts Telecommunications Xi’an China Department of Electrical and Electronic Engineering Universiti Tunku Abdul Rahman Kajang Malaysia Department of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks.

关键词： Vehicular and wireless technologies Simulation Quality of service Phase control Reconfigurable intelligent surfaces Autonomous aerial vehicles Energy efficiency Trajectory Uplink Vehicle dynamics

Systematic Literature Review Langchain Proposed

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Systematic Literature Review Langchain Proposed

International Electronics Symposium (IES)

作者： Rakha Asyrofi Mutia Rahmi Dewi Muhammad Irfan Lutfhi Prasetyo Wibowo Faculty of Marine Science and Engineering Universitas Hangtuah Surabaya Indonesia Department of Computer Science and Information Engineering National Central University Taiwan Faculty of Engineering Universitas Negeri Yogyakarta Yogyakarta Indonesia Graduate Institute of Network Learning Technology National Central University Taiwan Department of Information Technology Politeknik Negeri Padang Padang Indonesia Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review.

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Exploiting Circular Shifts for Efficient Chaotic Image Encryption

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IEEE Access 2025年 13卷 92997-93016页

作者： Lazaros Moysis Marcin Lawnik Wassim Alexan Sotirios K. Goudos Murilo S. Baptista George F. Fragulis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Mechanical Engineering University of Western Macedonia Kozani Greece Department of Mathematical Methods in Technics and Informatics Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland Communications Department Faculty of Information Engineering and Technology German University in Cairo Cairo Egypt ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Physics Institute for Complex Systems and Mathematical Biology SUPA The University of Aberdeen Aberdeen U.K.

This work presents a chaotic image encryption algorithm that acts on the binary level of the image for effective permutation, and on the byte level for substitution. The permutation step combines actions of bit level rearranging, and bit shuffling. Circular shift operations are used for bit shuffling, to reduce the execution time. Circular shifts are also applied to shuffle the permutation and substitution rules, effectively increasing security at a very low execution cost. The encryption keys are plaintext dependent, and a key mixing step in included, so that each part of the key affects all the subsequent encryption operations. For the encryption, a new Soboleva hyperbolic tangent based map is first designed. The dynamical behavior of the map is studied and shows robust chaos, and an absence of equilibria, meaning that it can generate hidden attractors. This map is used as an entropy source, along with a chaotic pseudo random number generator (PRNG). The encryption process shows a good performance under a collection of tests, and has also a low execution time, 0.0134 sec for a $256\times 256$ image, 0.0530 sec for a $512\times 512$ image, and 0.2050 sec for a $1024\times 1024$ image. So the proposed algorithm is a viable option for fast, efficient, and secure data encryption.

关键词： Encryption Security Chaotic communication Generators Robustness Matrices Costs Standards Real-time systems Computational efficiency