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How Does a Swarm Exhibit Emergent Behavior Through Synchronization?

Journal of Institute of Control, Robotics and Systems 2024年第4期30卷 352-362页

作者： Shim, Hyungbo ASRI Electrical and Computer Engineering Department Seoul National University Korea Republic of

A swarm of individuals often exhibits behaviors that are not possible for each individual. This phenomenon is called emergence, and this paper mathematically demonstrates that new dynamics can arise in swarm behavior that cannot be explained by the dynamics of individuals. In particular, we argue that emergence occurs when heterogeneity is coupled with synchronization. These two concepts may seem conflicting, as heterogeneity is the tendency to differ whereas synchronization is the tendency toward sameness. However, we show that emergent behavior arises from the interplay between the two. © ICROS 2024.

关键词： Multi agent systems

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft Computing 2025年第7期29卷 3241-3251页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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SAFED: secure and adaptive framework for edge-based data aggregation in IoT applications

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Discover Internet of Things 2025年第1期5卷 1-28页

作者： Naaz, Zaineb Joshi, Gamini Sharma, Vidushi Department of Computer Science and Engineering School of ICT Gautam Buddha University Greater Noida India Department of Computer Science and Engineering PES University Electronic City Campus Bangalore India

The paper proposed a secured and efficient data aggregation mechanism leveraging the edge computing paradigm and homomorphic data encryption technique. The paper used a unique combination of Paillier cryptosystem and RSA digital signature mechanism to preserve data privacy, confidentiality, and integrity while maintaining the efficiency of the IoT network. The efficiency and implementation of the proposed scheme have been demonstrated through a case study, showcasing how the scheme can be effectively applied in real-world scenarios, such as in a smart grid network. Additionally, the future scope has been outlined, with potential techniques to address challenges that the scheme may face. © The Author(s) 2025.

关键词： Data aggregation Edge computing Internet of things Paillier cryptosystem Privacy-preserving Security

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Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

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Multimedia Tools and Applications 2024年 1-29页

作者： Das, Bappaditya Raghuvanshi, C.S. Research Scholar Department of Computer Science and Engineering FET Rama University Kanpur India Professor Department of Computer Science and Engineering FET Rama University Kanpur India

Plant disease causes a highly significant impact on production due to its destructive characteristics. A variety of chemical techniques should be applied to agriculture at different stages of disease infestation to enable precise plant protection. The integration of unmanned aerial vehicles (UAVs) for obtaining high-resolution images has become necessary due to the growing demand for precision agriculture, making crop health monitoring more efficient. In this study, we developed an innovative approach that combines the Deep Radial Basis Function Network (DRBFN) with the Multidimensional Mixed Attention (MMA) method. Since DRBFNs use radial basis functions as effective feature extractors, the model can detect even the smallest changes in the composition, color, and texture of leaves. This capability is particularly helpful when dealing with a diversity of plant species and pathogens exhibiting different symptoms. By deliberately considering pertinent spatial and spectral attributes, MMA is applied to enhance the discriminatory abilities of the network. By integrating spatial and spectral attention, the network can efficiently acquire information from both local and global sources, thereby enhancing the overall resilience and precision of the detection system. Our proposed model leverages UAV-captured images to achieve a classification accuracy of 96.95% in identifying leaf diseases across two different plant species. The integration of mixed attention mechanisms improved feature representation, leading to a 47.63% reduction in Root Mean Square Error (RMSE) compared to traditional methods. Furthermore, our model demonstrates significant efficiency by processing 2000 images, making it suitable for real-time applications. These results underscore the potential of UAV-based systems combined with advanced deep learning techniques for precision agriculture, offering scalable solutions for early disease detection and management. © The Author(s), under exclusive licence to S

关键词： Deep learning

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SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

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IEEE Journal on Selected Areas in Communications 2023年第4期41卷 977-989页

作者： Jahani-Nezhad, Tayyebeh Maddah-Ali, Mohammad Ali Li, Songze Caire, Giuseppe Technische Universität Berlin Department of Electrical Engineering and Computer Science Berlin10587 Germany University of Minnesota Twin Cities Department of Electrical and Computer Engineering MinneapolisMN55455 United States The Hong Kong University of Science and Technology IoT Thrust Department of Computer Science and Engineering Sai Kung Hong Kong

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb {N}$ distributed users, each of size $L \in \mathbb {N}$ , trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most $D=o(N)$ dropout users, SwiftAgg+ achieves a per-user communication load of $\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$ symbols and a server communication load of $\left({1+\mathcal {O}\left({\frac {1}{N}}\right)}\right)L$ symbols, with a worst-case information-theoretic security guarantee, against any subset of up to $T=o(N)$ semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for $T © 1983-2012 IEEE.

关键词： Information theory

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Scene text visual question answering by using YOLO and STN

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International Journal of Speech Technology 2024年第1期27卷 69-76页

作者： Nourali, Kimiya Dolkhani, Elham Computer Science Department Semnan University Semnan Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran

Extracting text from an image using a Visual Question Answering (VQA) system is an application at the intersection of computer vision and Natural Language Processing (NLP) to help blind people better view and comprehend textual information within the image. A VQA system takes a natural language question and an image as its input and then targets different areas of the image to extract an answer from the text within the image. To detect and recognize text in the image, the Look-Read-Reason-Answer (LoRRA), DCD_ZJU and Multimodal Multi-Copy Mesh methods, classify image features by using an Optical-Character-Recognition (OCR) module. Due to the curvature, distortion, background noise, and different fonts, understanding the text in natural scenes is a challenge as OCR techniques are mainly designed to detect textual features from within clean image files. In this paper, we introduce a novel architecture that rectifies perspective and curved text in an image using Thin-Plate-Spline (TPS) transformations and detects objects through the utilization of You-Only-Look-Once networks to extract answers. The results of our experiments show that our proposed VQA method reaches higher accuracy levels on the OpenImage dataset as compared with the existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Feature extraction

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Super-resolution reconstruction of off-axis digital holography based on Real-ESRGAN

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Applied Optics 2025年第9期64卷 2245-2254页

作者： Chen, Geng Chen, Jie Liao, Houzhang Liu, Keke Tang, Xin Kong, Yong School of Electric and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Off-axis digital holography plays a crucial role in high-precision three-dimensional imaging. However, high-resolution phase images are often affected by the limited pixel size of the sensor. To address this issue, this study proposes, for the first time, to the best of our knowledge, the application of Real-ESRGAN for the super-resolution reconstruction of off-axis digital holography images. By directly applying super-resolution processing to the hologram, the limitations of the sensor's pixel size are overcome, followed by phase reconstruction to obtain high-resolution phase images. The Real-ESRGAN model enhances the texture information of the hologram through a deep network with multiple residual-in-residual dense blocks (RRDBs), restoring a clearer hologram. A U-Net architecture with spectral normalization, offering stronger discriminative capability, is used to provide detailed per-pixel feedback for the generator. The experimental results demonstrate that this method can be applied to multi-scale holograms, outperforming comparison networks in both visual quality and quantitative metrics, thus providing an innovative solution for the super-resolution reconstruction of off-axis digital holography. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Holograms

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A novel data-driven algorithm for object detection, tracking, distance estimation, and size measurement in stereo vision systems

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Multimedia Tools and Applications 2025年第12期84卷 11041-11061页

作者： Taromi, Amirhossein Dadashzadeh Klidbary, Sajad Haghzad Department of Engineering University of Zanjan Zanjan Iran Faculty of Engineering Department of Electrical and Computer Engineering University of Zanjan Zanjan Iran

Distance and size estimation of objects of interests is an inevitable task for many navigation and obstacle avoidance algorithms mainly used in autonomus and robotic systems. Stereo vision systems, inspired by human visual perception, can infer depth from images as a cheap and accessible solution. On one hand, accurately calibrating cameras is a challenging task and the main source of error in current stereo vision based distance and size estimation algorithms. On the other hand, considering the recent advancements in Deep Learning, alongside the fact that human eyes do not need calibration but human brain can estimate the distance and size of objects fairly accurate was the main motivation behind this study. The proposed algorithm uses YOLOv8 as the object detector, and an MLP to learn the relation between distance, size, and disparity from collected data in a stereo vision system. In our experiments, conducted at distances ranging from 50 to 200 centimeters with calibrated and uncalibrated cameras, our proposed algorithm showcased accurate performance in both scenarios. It achieved distance measurements with an accuracy of up to 99.99% in select cases and maintained the mean accuracy of 98.15% for distance, 92.87% for width, and 93.92% for height estimations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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Application and comparison of K-means and PCA based segmentation models for Alzheimer disease detection using MRI

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Discover Artificial Intelligence 2024年第1期4卷 11页

作者： Olle Olle, Daniel Georges Zoobo Bisse, Julien Abessolo Alo’o, Ghislain Department of Computer Science University of Yaoundé 1 Yaoundé Cameroon Department of Computer Engineering University of Ebolowa Ebolowa Cameroon

Machine learning algorithms can be used to detect Alzheimer disease with RMI-images. One of the challenges of these algorithms is to clearly extract image features that show small variants of brain cells changes, which reveal the condition of dementia at the intermediate stages of mild cognitive impairment. In this article, we explore the abilities of two approaches to diagnose Alzheimer’s disease with MRI. In the first approach, after noise reduction and correction of alterations was conducted by a non-linear filter size 3*3, a kmeans algorithm is used for segmentation of cells showing white and grey matters of brain images. A Convolutional neural network (CNN) model is trained to indicate variations on these cells and the presence of Alzheimer Disease. The second approach performs image feature reduction using principal component analysis (PCA) to extract white and grey matters, and the cerebrospinal fluid as the three relevant features for Alzheimer diagnosis. A multilayer perceptron algorithm is trained to classify Alzheimer cases. Experiments are conducted on both approaches to compare accuracy and processing time using a real dataset of 602 images from the Alzheimer’s disease Neuroimaging Initiative (ADNI) of cognitively normal and Alzheimer’s disease patients. Results show that the accuracy can be enhanced when PCA is used to extract relevant features on RMI images;and with relatively low processing time. © The Author(s) 2024.

关键词： Image segmentation

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Distributed Estimation and Control for Jamming an Aerial Target With Multiple Agents

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IEEE Transactions on Mobile Computing 2023年第12期22卷 7203-7217页

作者： Papaioannou, Savvas Kolios, Panayiotis Ellinas, Georgios Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target maneuvers in 3D space, avoiding collisions with obstacles and other 3D structures in its way, according to a stochastic dynamical model. Based on this, a track-and-jam control approach is proposed which allows a team of distributed aerial agents to decide their control actions online, over a finite planning horizon, to achieve uninterrupted radio-jamming and tracking of the malicious target, in the presence of jamming interference constraints. The proposed approach is formulated as a distributed model predictive control (MPC) problem and is solved using mixed integer quadratic programming (MIQP). Extensive evaluation of the system's performance validates the applicability of the proposed approach in challenging scenarios with uncertain target dynamics, noisy measurements, and in the presence of obstacles. © 2002-2012 IEEE.

关键词： Drones

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