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arXiv 2019年

作者： Cheikhrouhou, Omar Koubâa, Anis College of CIT Taif University Taif Saudi Arabia Computer and Embedded Systems Laboratory University of Sfax Sfax Tunisia Prince Sultan University Saudi Arabia CISTER/INESC-TEC ISEP Portugal Gaitech Robotics China

—Several IoT applications are tightly dependent on the locations of the devices. However, localization algorithms can be easily compromised by injecting false locations. In this paper, we propose a Blockchain-based secure localization algorithm for the Internet of Things (IoT). The algorithm uses a public ledger (Blockchain) that contains nodes position and the list of their neighbor nodes. This ledger is shared among the IoT devices. Once an IoT device is localized its new position and the list of neighbor nodes are added to the Blockchain. This shared localization data will be used later by other IoT devices for their localization process. To avoid the attack where a malicious node sends a fake position, the correctness of the claimed position are verified before adding it to the Blockchain. Moreover, data exchanged between nodes (IoT devices) are signed to guarantee their authenticity and integrity. The integration of these security mechanisms into the localization process permits to exclude false data and therefore reduces the localization error. The simulation results show that adding the proposed security mechanism improves the localization accuracy of the algorithm when running in the presence of malicious nodes. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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Suitable Clustering for Multi-shot Person Re-identification

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Research Journal of Applied Sciences, Engineering and Technology 2018年第1期15卷 7-15页

作者： Yousra Hadj Hassen Kais Loukil Tarek Ouni Mohamed Jallouli Computer and Embedded Systems Laboratory National School of Engineers of Sfax Street of Soukra km 4.5 Sfax 3038 Tunisia

This study address the problem of selecting the most informative images for multi-shot person re-identification approaches. Actually, clustering algorithms have been one of the most proposed solutions. The objective of this study is to propose a suitable clustering method for most discriminative images selection in person re-identification. Clustering methods aim to divide huge data amount into groups depending on their characteristics, so that further data processing can be easier and more manageable. Actually, clustering is concerned in multiple fields such as document treatments, video summery and image processing. Person re-identification, a new area of research deeply investigated by the vision community, would be an interest application of the clustering algorithms. Person re-identification can be defined as the process of finding the identity of an unknown person who has already been observed in a camera view. Recently, re-identifying people over large public cameras networks has become a crucial task of great importance to ensure public security. Person Re-identification approaches can be either single shot, using one image to model a person's appearance, or multiple shot, using many images to identify a person. Actually, the real person re-identification framework is a multi-shot scenario. However, redundant images remain a challenging problem because execution time and memory consumption are significantly affected. In this study, an extensive comparison of clustering algorithms of state of art associated to a person re-identification framework is studied. Specifically, we evaluate the impact of clustering algorithms in re-identification rates. A standard re-identification framework is detailed and a synthesis of state of art methods comparison is conducted, using personal images and two standard datasets PRID_2011 and iLIDS-VID. Performing results are achieved in both person re-id rates (67.1% and 59.2%) and memory gain (92.7% and 97.8%) for Prid_2011 and

关键词： Camera network clustering identity selection multi-shot person re-identification redundancy

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Are state-of-the-art visual place recognition techniques any good for aerial robotics?

arXiv

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arXiv 2019年

作者： Zaffar, Mubariz Khaliq, Ahmad Ehsan, Shoaib Milford, Michael Alexis, Kostas McDonald-Maier, Klaus Embedded and Intelligent Systems Laboratory in Computer Science Electronic Engineering Department University of Essex Colchester United Kingdom Australian Centre for Robotic Vision and School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD Australia Autonomous Robots Lab University of Nevada United States

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect 8 state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on 2 recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR. Copyright © 2019, The Authors. All rights reserved.

关键词： Antennas

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Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

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Information Fusion 2025年 124卷

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

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Transfer learning-based online multiperson tracking with Gaussian process regression

Transfer learning-based online multiperson tracking with Gau...

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作者： Zhang, Baobing Li, Siguang Huang, Zhengwen Rahi, Babak H. Wang, Qicong Li, Maozhen Department of Electronic and Computer Engineering Brunel University London London United Kingdom The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University Binzhou China Department of Computer Science Xiamen University Xiamen China

Most existing tracking-by-detection approaches are affected by abrupt pedestrian pose changes, lighting conditions, scale changes, and real-time processing, which leads to issues such as detection errors and drifts. To deal with these issues, we present a novel multi-person tracking framework by introducing a new Gaussian Process Regression based observation model, which learns in a semi-supervised manner. The background information is taken into consideration to build the discriminative tracker, training samples are re-weighted appropriately to ease the impact of the potential sample misalignment and noisy during model updating. Unlabeled samples from the current frame provide rich information, which is used for enhancing the tracking inference. Experimental results show that the proposed approach outperforms a number of state-of-the-art methods on some benchmark datasets. © 2018 John Wiley & Sons, Ltd.

关键词： Gaussian noise (electronic)

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Semantic enhanced deep learning for image classification

Semantic enhanced deep learning for image classification

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作者： Li, Siguang Li, Maozhen Jiang, Changjun The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University BinzhouShandong China Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom

Extracting training data semantics mainly depends on manual annotation samples prepared in advance, and semantic mapping largely relies on the prior conditions in image classification. A majority of the data samples in reality are unlabeled data, which necessitates a more efficient way to extract image semantics in classification. Deep learning, as an advanced machine learning technique, has recently received significant attention from both the academia and industry. Deep learning has the potential to extract semantic features from images in an automated way to fill up the semantic gap. This paper presents 2 novel deep learning models (ie, stacked denoising auto-encoder and convolution deep Boltzmann machine). A stacked denoising auto-encoder builds on a stacked auto-encoder but uses a denoising auto-encoder to improve the learning quality. Convolution deep Boltzmann machine combines the convolutional neural network model with the deep Boltzmann machine. These 2 models are evaluated on CIFAR-10 and STL-10 datasets, respectively. Experimental results show that the proposed 2 models outperform the original models in terms of both precision and recall. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Convolution

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PID2018 Benchmark Challenge: Model-based Feedforward Compensator with A Conditional Integrator

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IFAC-PapersOnLine 2018年第4期51卷 888-893页

作者： Yuan, Jie Ates, Abdullah Dehghan, Sina Zhao, Yang Fei, Shumin Chen, YangQuan School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey Mechatronics Embedded Systems and Automation Lab University of California MercedCA95340 United States School of Science and Engineering Shandong University Jinan250061 China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China

Since proportional-integral-derivative (PID) controllers absolutely dominate the control engineering, numbers of different control structures and theories have been developed to enhance the efficiency of PID controllers. Thus, it is essential and inspiring to operate different PID control strategies to the PID2018 Benchmark Challenge. In this paper, a novel control strategy is designed for this refrigeration system, where a feedforward compensator and a conditional integrator are utilized to compensate the disturbances and remove the steady-state error in the benchmark problem, respectively. The simulation results given in the benchmark problem show the straightforward effectiveness of the proposed control structure compared with the existing control methods. © 2018

关键词： Controllers Benchmarking Disturbance rejection Electric control equipment Proportional control systems Refrigeration Three term control systems Two term control systems Conditional integrators Feed forward compensation Model based method PID controllers Refrigeration system

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DEFINITION OF A TRANSPARENT CONSTRAINT-BASED MODELING AND SIMULATION LAYER FOR THE MANAGEMENT OF COMPLEX systems

DEFINITION OF A TRANSPARENT CONSTRAINT-BASED MODELING AND SI...

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Simulation Multi-Conference

作者： Kevin Henares Jose L. Risco-Martin Marina Zapater Dept. of Computer Architecture and Automation Complutense University of Madrid Madrid Spain Embedded Systems Laboratory (ESL) Swiss Federal Institute of Technology Lausanne (EPFL) Lausanne Switzerland

Modeling and Simulation (M&S) is one of the most multifaceted topics present today in both industry and academia. However, we are involved in a new M&S paradigm. systems are becoming more complex and new simulation needs arise and have to be studied. As a consequence, the way in which we perform M&S must be adapted, providing new ideas and tools. In this paper, we propose a rule-based constraints evaluator, which facilitate the validation and verification of complex models in a transparent manner. For this, constraints are defined. The constraints definition process is completely independent of the model development process because (a) the set of constraints is defined once the model has been developed, and (b) constraints are validated at simulation time. The proposed Constraint M&S architecture has been built using the Discrete Event System Specification (DEVS) formalism and has been tested on a validated data center simulation model.

关键词： Model checking Constraint modeling and simulation Discrete events Verification Data centers

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A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud

A genetic algorithm enhanced automatic data flow management ...

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作者： Li, Siguang Huang, Zhengwen Han, Liangxiu Jiang, Changjun The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China College of Electrical Engineering Binzhou University Binzhou China Department of Electronic and Computer Engineering Brunel University London London United Kingdom School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester United Kingdom

The past few years have witnessed a rapid deployment of computing infrastructures in the cloud in support of data intensive applications. The effort of the existing works is mainly focused on data reusing mechanisms without considering data processing routes, which can significantly affect the computation costs when exchanging data among the computing node in the cloud. This paper presents a genetic algorithm enhanced Automatic Data Flow Management Solution (ADFMS) that facilitates automatic routing function and a self-adjustable intermediate data management mechanism to achieve an efficient data processing structure of cloud computing. Experimental results show that ADFMS optimizes costs in managing intermediate data in the cloud. © 2018 John Wiley & Sons, Ltd.

关键词： Petri nets

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Achieving efficient realization of kalman filter on CGRA through algorithm-architecture co-design

arXiv

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arXiv 2018年

作者： Merchant, Farhad Vatwani, Tarun Chattopadhyay, Anupam Raha, Soumyendu Nandy, S.K. Narayan, Ranjani Institute for Communication Technologies and Embedded Systems RWTH Aachen University Germany Hardware and Embedded Systems Lab Nanyang Technological University Singapore Scientific Computation Laboratory Indian Institute of Science Bangalore India Computer Aided Design Laboratory Indian Institute of Science Bangalore India Morphing Machines Pvt. Ltd

In this paper, we present efficient realization of Kalman Filter (KF) that can achieve up to 65% of the theoretical peak performance of underlying architecture platform. KF is realized using Modified Faddeeva Algorithm (MFA) as a basic building block due to its versatility and REDEFINE Coarse Grained Reconfigurable Architecture (CGRA) is used as a platform for experiments since REDEFINE is capable of supporting realization of a set algorithmic compute structures at run-time on a Reconfigurable Data-path (RDP). We perform several hardware and software based optimizations in the realization of KF to achieve 116% improvement in terms of Gflops over the first realization of KF. Overall, with the presented approach for KF, 4-105x performance improvement in terms of Gflops/watt over several academically and commercially available realizations of KF is attained. In REDEFINE, we show that our implementation is scalable and the performance attained is commensurate with the underlying hardware resources. Copyright © 2018, The Authors. All rights reserved.

关键词： Kalman filters

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