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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Saranya, L. Arvind, C. Zhu Tang, Seng Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India University of Huaguoshan Department of Intelligent Robotics Huaguoshan Jileshijie Province China

ISBN: (纸本)9798350397376

WSNs are made up of a network of sensor nodes that are used to track environmental characteristics like pressure, temperature, etc. and communicate the data to the intended location. Networks must overcome obstacles including security, routing, problems with energy conservation, scalability, dependability, secrecy, integrity, authentication, and others when transmitting information. Different routing systems, including data-centric, location- and mobility-based, Quality of Service (QoS)-based, and multipath routing protocols, have been employed to address these issues. Even while these routing methods guarantee a successful transaction, the network still has to manage intermediary assaults that can completely alter the content of messages being carried. Therefore, a number of cryptographic approaches have been used to minimise the network. The newly developed symmetric and asymmetric algorithms are susceptible to problems with route latency, area overhead, and on-chip power consumption. Thus, to reduce these harmful actions in WSNs, an efficient reduced key size and high security based cryptographic method must be used. Utilizing the limited network resources, the main goal of the study is to increase network security while reducing network overhead and path delay-related problems. © 2023 IEEE.

关键词： Network security

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Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

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intelligent Automation & Soft Computing 2023年第2期35卷 1843-1856页

作者： Walid El-Shafai Randa Ali Ahmed Sedik Taha El-Sayed Taha Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of ROBOTICS and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The use of intelligent machines to work and react like humans is vital in emerging smart ***-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare *** AI(Artiﬁcial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchﬁelds that have become attractive to *** are several modalities of imaging performed on the brain for the pur-pose of tumor *** paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)*** proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal *** dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:*** main contribution of this paper is introdu-cing an optimum deep learning structure of CNN *** simulation results are obtained for 50 epochs in the training *** simulation results reveal that the optimum CNN architecture consists of four layers.

关键词： Healthcare smart cities clinical automation CNN machine learning brain tumor medical diagnosis

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Disturbance Observer-Based Data Driven Model Predictive Tracking Control of Linear Systems

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IEEE Access 2023年 11卷 88597-88608页

作者： Farbood, Mohsen Echreshavi, Zeinab Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel Shiraz University of Technology Department of Electrical and Electronics Engineering Shiraz71557-13876 Iran University of Zanjan Department of Electrical Engineering Zanjan45371-38791 Iran National Yunlin University of Science and Technology Douliou Graduate School of Intelligent Data Science Yunlin640301 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces a new data-driven MPC structure based on two offline and online parts to achieve the robust and constrained performance in an optimal scheme. In the first step, according to the model matching condition, an offline data-driven controller is designed to reach the tracking performance. In addition, to reduce the effects of the external disturbance, a data-driven-based disturbance observer is presented to estimate the external disturbance. Therefore, the robustness against the external disturbances is achieved in an offline procedure. Then, a data-driven model predictive control (MPC) is structured based on a data-driven-based model of a stabilized system. In other words, the overall controller is configured such that the limitations of the system states and control input are considered in the control design process. Moreover, by employing the move blocking strategy, the online computational burden of the suggested controller is greatly reduced. To further improve of the feasibility problem, an ellipsoidal terminal (ET) constraint is considered. The rows number of the blocking matrix influences on the ET set which leads to feasibility enhancement. So, the main contributions of the presented data-driven controller are feasibility improvement and reducing online computational burden in an optimal and constrained scheme which are illustrated in the simulation section. © 2013 IEEE.

关键词： Linear systems

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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User Perception and Evaluation of a Portrait Drawn by KUKA KR3 AGILUS Manipulator 9th

User Perception and Evaluation of a Portrait Drawn by KUKA K...

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9th International Conference on Interactive Collaborative robotics, ICR 2024

作者： Zagirov, Aidar Chebotareva, Elvira Osokin, Sergey Li, Hongbing Magid, Evgeni Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlevskaya St. Kazan420008 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China School of Electronic Engineering Tikhonov Moscow Institute of Electronics and Mathematics HSE University 34 Tallinskaya St. Moscow Russia

ISBN: (纸本)9783031713590

Creating an artistic portrait is considered one of the most complex areas of a generative art. Currently, generative artificial intelligence (AI) has made significant advancements in generating portraits of people based on photographic images. This has opened up new opportunities for utilizing AI achievements in robotic artistry. However, questions related to evaluation and human perception of such works, including their artistic value, remain open. In our work, we present experimental results of integrating the processing of human photographs using the generative model InstantID with a drawing robot and studying users’ reactions to the obtained images. In our experiments, we generated graphic portraits of people and constructed their images using KUKA KR3 AGILUS robot. We then asked participants to rate the resulting portraits based on three parameters: similarity/recognition, beauty/aesthetics, and evoked emotions. The results of the experiments can be used in social robotics, including art therapy that involves social robots and human-robot interaction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Social robots

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Optimal Adaptive Current Controller for Permanent Magnet Synchronous Motor with Performance Recovery Property 49

Optimal Adaptive Current Controller for Permanent Magnet Syn...

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49th Annual Conference of the IEEE Industrial electronics Society, IECON 2023

作者： Chan Lee, Hyo Lim, Sun Jung, Il-Kyun Kim, Seok-Kyoon Graduate School of Creative Convergence Engineering Hanbat National University Daejeon Korea Republic of Intelligent Robotics Research Center of Korea Electronics Technology Institute Bucheon Korea Republic of Hanbat National University Department of Creative Convergence Engineering Daejeon Korea Republic of

ISBN: (纸本)9798350331820

This article exhibits a current controller guaranteeing the performance recovery property despite the parameter uncertainties for interior-mounted permanent magnet synchronous motors (IPMSMs). The contributions of the proposed method are as follows. The first one is to build the current control law for the closed-loop system to asymptotically recover the target current tracking performance, while estimating the unknown parameters. The second one is to present a systematic way assigning the optimal control gains by solving an optimization problem. The third one is to rigorously analyze the internal dynamics problem caused by the speed dynamics of IPMSMs. The efficacy of the proposed method is verified by conducting experiments where the proposed method is applied to a 5-kW IPMSM. © 2023 IEEE.

关键词： Closed loop systems

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Evaluation of a Weather Plugin in Gazebo: A Case-Study of a Wind Influence on PX4-Based UAV Performance 9th

Evaluation of a Weather Plugin in Gazebo: A Case-Study of a ...

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9th International Conference on Interactive Collaborative robotics, ICR 2024

作者： Imamov, Niyaz Abbyasov, Bulat Tsoy, Tatyana Martínez-García, Edgar A. Magid, Evgeni Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlyovskaya St. Kazan420111 Russia Tikhonov Moscow Institute of Electronics and Mathematics HSE University Tallinn Street 34 Moscow Russia Institute of Engineering and Technology Department of Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Manuel Díaz H. No. 518-B Zona Pronaf Condominio Chihuahua Cuidad Juárez32315 Mexico

ISBN: (纸本)9783031713590

Unmanned aerial vehicles (UAVs) are utilized in a wide range of real-world applications including search and rescue missions, 3D mapping, building inspection and reconstruction. In a real-world environment, severe weather conditions significantly impact UAV operations. Wind is one of the most dangerous weather factors that can destabilize and even destroy a UAV. Preparing UAV flight algorithms for real-world conditions in order to minimize risks of damage and malfunctioning is a crucial part of a UAV system development. This paper analyses a wind influence on UAV virtual model performance within the Gazebo simulator. The paper focuses on gazebo_wind_plugin package used for creating a windy virtual environment. PX4-LIRS package is used for simulating a UAV within the Gazebo. Three types of experiments were run to evaluate capabilities of the plugin to simulate typical issues that arise in real-world scenarios: a UAV wind resistance, a wind effect on a UAV tilt angle and on a flight velocity. Virtual experiments demonstrated that gazebo_wind_plugin could serve for PX4 UAV flight scenarios modeling in Gazebo simulation with a satisfactory level of realism. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

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Computers, Materials & Continua 2022年第10期73卷 1845-1861页

作者： E.A.Alabdulkreem Ahmed Sedik Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafrelsheikh33511Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt

Image fusion has become one of the interesting fields that attract researchers to integrate information from different image *** is involved in several *** of the recent applications is the robotic *** application necessitates image enhancement of both infrared(IR)and visible *** paper presents a Robot Human Interaction System(RHIS)based on image fusion and deep *** basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured ***,an enhancement model is carried out on the fused image to increase its *** image enhancement models such as fuzzy logic,Convolutional Neural Network(CNN)and residual network(ResNet)pre-trained model are utilized on the fusion results and they are compared with each other and with the state-of-the-art *** results prove that the fuzzy logic enhancement gives the best results from the image quality ***,the proposed system can be considered as an efficient solution for the robotic vision problem with multi-modality images.

关键词： Deep learning fuzzy logic image fusion IR images

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Symmetric And Asymmetric Algorithms for Susceptible Harmful in Wireless Sensor Network on FPGA Platform

Symmetric And Asymmetric Algorithms for Susceptible Harmful ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： L. Saranya C. Arvind Zhu Seng Tang Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore Tamil Nadu Department of Intelligent Robotics University of Huaguoshan Huaguoshan China

关键词： Temperature sensors Wireless sensor networks Quality of service Routing Mathematical models System-on-chip Delays

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