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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Tsikelis, Ioannis Chatzilygeroudis, Konstantinos University of Patras Computer Engineering and Informatics Department PatrasGR-26504 Greece Department of Electrical & Computer Engineering PatrasGR-26504 Greece Department of Mathematics PatrasGR-26110 Greece

ISBN: (纸本)9798350373578

Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems. To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling. © 2024 IEEE.

关键词： Cost functions

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A two-stage progressive deep segmentation network for tumor detection in breast ultrasound images

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Multimedia Tools and Applications 2024年 1-23页

作者： Zaidkilani, Nadeem Abdel-Nasser, Mohamed Garcia, Miguel Angel Puig, Domenec Department of Computer Engineering and Mathematics University Rovira i Virgili Tarragona43007 Spain Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronic and Communications Technology Autonomous University of Madrid Madrid Spain

Segmenting tumorous regions in breast ultrasound images is a challenging problem due to several factors, including the relatively low contrast of the available images, the presence of speckle noise, and the considerable variations in breast mass sizes and shapes. Current methods are not precise enough and prone to misdetections. An efficient deep neural model is proposed for automatically segmenting tumorous regions in breast ultrasound images. The model is constituted by two consecutive encoder-decoder (autoencoder) networks. The first autoencoder extracts a preliminary binary mask from the given image. The second autoencoder refines that mask after concatenating it with the original image. The encoders within each autoencoder can be defined by applying any state-of-the-art network. In addition, cost-sensitive learning has been used in order to focalize training on the segmentation errors of the minority class (tumor). Semantic segmentation based on advanced deep learning methods is thus applied in order to enhance tumor segmentation in breast ultrasound images. The proposed model offers advanced capabilities for automated segmentation with the aim of helping physicians identify and diagnose tumors using state-of-the-art techniques. This model outperforms recent tumor segmentation methods in the experiments conducted on two public datasets of breast ultrasound images (UDIAT and BUSI). The largest improvement for both datasets was achieved by using CoAtNet as baseline model (Dice index equal to 84.49% and 78.94%, respectively). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Ultrasonic imaging

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Leveraging Noise Recycling in Soft Detection Decoding Using ORBGRAND

Leveraging Noise Recycling in Soft Detection Decoding Using ...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Kizilates, Zeynep Ece Riaz, Arslan Coraluppi, Giacomo F. Medard, Muriel Duffy, Ken R. Tugce Yazicigil, Rabia Boston University Department of Electrical and Computer Engineering BostonMA United States Mit Department of Electrical Engineering and Computer Science CambridgeMA United States Northeastern University Department of Electrical and Computer Engineering Department of Mathematics BostonMA United States

ISBN: (纸本)9781665475549

For communications subject to correlated channel effects, noise recycling has recently been shown to enhance channel capacity with receiver-side-only changes. Using a taped-out chip, in a hard-detection scenario with guessing random additive noise decoding (GRAND), noise recycling has been established to both increase decoding accuracy and decrease decoding energy in single communication channels that employ interleavers. This paper presents results for the related soft-detection scenario by investigating noise recycling with an in-silicon realization of Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND). Measurements demonstrate that noise recycling leads to a significant reduction in the block error rate (BLER) and substantial improvements in latency and energy consumption by reducing the number of queries required for decoding. We also discuss dynamic lead channel selection for the soft detection scenario and show the importance of lead channel on overall decoding performance. © 2023 IEEE.

关键词： Additive noise

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Impact of Virtual Synchronous Generators on Power System Frequency Dynamics

Impact of Virtual Synchronous Generators on Power System Fre...

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2023 International Conference on Smart Energy Systems and Technologies, SEST 2023

作者： Fu, Aihui Lekic, Aleksandra Kontis, Eleftherios O. Malamaki, Kyriaki-Nefeli D. Kryonidis, Georgios C. Mauricio, Juan Manuel Demoulias, Charis S. Cvetkovic, Milos Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Aristotle University of Thessaloniki Department of Electrical & Computer Engineering Thessaloniki Greece University of Seville Department of Electrical Engineering Spain

ISBN: (纸本)9798350397901

This paper deals with a systematic assessment of the power system frequency dynamics under high penetration of converter-interfaced renewable energy sources (CI-RESs). Specifically, the concept of the virtual synchronous generator (VSG) is implemented in the CI-RESs located at the transmission system (TS) side and/or the distribution network (DN) side. Dynamic RMS simulations are performed on a testbed consisting of the IEEE 9-bus TS grid and the CIGRE medium-voltage DN grid under different CI-RES penetration levels and VSG control parameters to assess the VSG impact on the power system frequency dynamics. It is shown that the decommissioning of conventional power plants coupled via synchronous generators can be safely performed in case the VSG concept is adopted correctly. © 2023 IEEE.

关键词： Synchronous generators

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A Dynamic Frequency Sweeping Based Parameter Estimation Method for Wireless Power Transfer 49

A Dynamic Frequency Sweeping Based Parameter Estimation Meth...

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

作者： Zhu, Gangwei Dong, Jianning Bauer, Pavol Delft University of Technology Department of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9798350331820

It is ideal for the wireless power transfer (WPT) systems to operate at the resonance state for better transmission performance. In practice, however, the parameters of the resonant circuits often deviate because of the capacitance drift and coil misalignment. To this end, this paper proposes a new parameter estimation method for the WPT systems, which is able to facilitate the power flow control and active impedance tuning of the WPT systems under parameter deviations. Distinct from the traditional parameter identification methods, the proposed method is implemented with an short-circuited rectifier, and therefore, the whole estimation process is independent of the load variations. Furthermore, to avoid severe system detuning during the frequency-sweeping process, a dynamic frequency sweeping method is proposed to efficiently and safely extract the data of the primary and secondary coil currents. Based on the extracted data, a mathematical model is established, and the JAYA algorithm is utilized to identify the unknown parameters. Experimental results are presented to verify the estimation accuracy of the proposed method. © 2023 IEEE.

关键词： Energy transfer

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Nanowatt Integrated Circuits and Systems

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IEEE NANOTECHNOLOGY MAGAZINE 2022年第3期16卷 2-3页

作者： Sheu, Bing Kao, Shao-Ku Electronics Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace Greece Computer Engineering Laboratory Electrical Engineering Mathematics and Computer Science Faculty Delft University of Technology Delft The Netherlands

The scaling of CMOS technology continues to follow Moore’s law through clever designs at the transistor, circuit, and system levels, from both manufacturing and packaging. The integration of 50 billion-plus transistors into a single silicon chip has become quite common. Power consumption is now one of the key design factors in advancing nanoelectronics. It is our great pleasure to introduce Prof. Kiat Seng Yeo and Prof. Chao-Sung Lai as the guest editors of this special issue of IEEE Nanotechnology Magazine .

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Optimizing QoE in IoT-Based Video Streaming through Deep Learning Algorithms 6

Optimizing QoE in IoT-Based Video Streaming through Deep Lea...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350344349

The demand for video streaming services over Internet of Things (IoT) networks has surged, yet maintaining a high Quality of Experience (QoE) remains challenging due to network heterogeneity and resource constraints. This paper presents an innovative deep learning-based approach to optimize QoE in IoT-based video streaming. Leveraging convolutional neural networks (CNN) and long short-Term memory (LSTM) networks, the proposed framework dynamically adapts video streaming parameters in real-Time, such as bit rate, frame rate, and resolution, based on the existing network conditions and device capabilities. Through extensive simulations and real-world deployments, our approach exhibited a 24.5% reduction in re-buffering ratio, a 12.5% increase in average bitrate, and a 15% improvement in video quality measured by SSIM, compared to conventional methods. Furthermore, it reduced network bandwidth consumption by up to 150% without compromising on video quality. These results underscore the effectiveness of applying deep learning algorithms to optimize video streaming in complex IoT environments. © 2024 IEEE.

关键词： Video streaming

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A System for Monitoring Hydroponic Plants in Greenhouse Using WSN, IoT and RnE 7th

A System for Monitoring Hydroponic Plants in Greenhouse Usin...

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7th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2023

作者： Touhami, Achouak Bourouis, Amina Touhami, Nawal Touhami, Sana Benahmed, Tariq Benahmed, Khelifa Department of Mathematics and Computer Science Ali Kafi University Center Tindouf Algeria Department of Mathematics and Computer Science Tahri Mohammed University Bechar Algeria Department of Electrical Engineering Ahmed Draia University Adrar Algeria Department of Mathematics and Computer Science Tamaghasset University Tamaghasset Algeria

ISBN: (纸本)9783031606281

Hydroponic cultivation offers a promising solution to address environmental and food-related challenges by minimizing the use of fertilizers and water. Our research presents a greenhouse-based design for monitoring hydroponic plants, using wireless sensor networks (WSN), hydroponic irrigation system, Internet of Things (IoT) technologies and renewable energy system (RnE). The WSN consists of diverse sensors, including those for water level, water temperature, and pH levels. The sensors are connected to a cluster head, which is linked to a microcontroller. Through the Internet, the microcontroller transmits the collected data to the farmer. The farmer evaluates the data by comparing it to predetermined thresholds and, using a laptop or smartphone, sends commands to the associated actuator. The cloud serves as the storage system for the collected data. The system features automated control capabilities, allowing for adjustments in temperature, pH levels, and water levels. Furthermore, the proposed design can be easily customized to accommodate different hydroponic conditions and systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Hydroponics

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