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52nd European Microwave Conference, EuMC 2022

作者： Arenare, D. Pelorossi, F. Concaro, F. Pasian, M. University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy European Space Agency European Space Operations Centre Directorate of Operations Gsa Section Darmstadt Germany

ISBN: (数字)9782874870699

ISBN: (纸本)9782874870699

Large reflector antennas require struts to mechanically support feeds or sub-reflectors. In many cases, these affect the antenna performance. This paper proposes a comparison between the European Space Agency Deep Space Antennas (DSAs) 3 and 4, in terms of impact of struts on antenna efficiency and sidelobes. The numerical simulations, carried out on the entire antenna model using Physical Optics, indicates that the struts of the new DSA4 can provide a reduced impact due to the different design. © 2022 European Microwave Association (EuMA).

关键词： Efficiency

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Speeding up Detection and Imaging Using Quantum Radars

Speeding up Detection and Imaging Using Quantum Radars

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： David Luong Bhashyam Balaji Sreeraman Rajan Department of Systems and Computer Engineering Carleton University Radar Sensing and Exploitation Section Defence Research and Development Canada

In this paper, we provide a simple introduction to the enhancement achievable by quantum radars. We show that an important type of quantum radar, known as quantum two-mode squeezing (QTMS) radar, is mathematically connected to noise radars. The quantum advantage stems from QTMS radar’s ability to mitigate the effect of quantum noise, leading to greater contrast in the matched filter output between the target present and target absent cases. This reduced the integration time needed to detect a target. For imaging applications that rely on matched filtering, the quantum enhancement leads to a speedup in imaging as well.

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Machine Learning and Intelligent Network System based Covid 19 Health Care System 3

Machine Learning and Intelligent Network System based Covid ...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Basha, Amjath Ali Jakul Saravanan, K. Mangayarkarasi, T. Balarengadurai, C. University of Technology and Applied Sciences Ibra Sultanate of Oman RCC - ENGG Department Computer Engineering Electrical and Electronic Section Department of Engineering Oman R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Tamil Nadu Chennai India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665462754

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

关键词： COVID-19

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3D characterization of tree architecture for apple crop load estimation

3D characterization of tree architecture for apple crop load...

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2022 ASABE Annual International Meeting

作者： Qiu, Tian Cheng, Lailiang Jiang, Yu School of Electrical and Computer Engineering College of Engineering Cornell University IthacaNY14850 United States Horticulture Section School of Integrative Plant Science Cornell University IthacaNY14853 United States Horticulture Section School of Integrative Plant Science Cornell University GenevaNY14456 United States

Tree architecture is indicative of the apple tree growth and apple crop load potential. While many studies have widely used 2D imaging to measure architectural traits of tree crops, the measurement accuracy was largely limited by occlusion and scene ambiguity due to a single perspective projection in 2D images. The goal of this study was to develop a 3D point cloud-based approach for apple tree segmentation and architecture characterization. Specifically, a biological-constrained graph-based algorithm was developed to segment the trunk and branch and characterize the architectural phenotypic traits of apple trees. A Laplacian-based skeletonization algorithm was used to extract a curve skeleton from the 3D point cloud and the topological structure of the skeleton was optimized by a weighted minimum spanning tree algorithm. The trunk was segmented from the skeleton by searching for an optimal path starting from the root point and branches were divided into groups by 3D DBSCAN clustering algorithm. Tree height and trunk diameter were estimated based on the geometric information of the tree trunk. Quantitative branch patterns were revealed by estimating the branch length, branch diameter, and branch angles. Point cloud data of 9 apple trees were collected under the field condition using a terrestrial LiDAR at Cornell Orchard in Ithaca, NY, USA. Preliminary experiments showed that the proposed algorithm achieved a higher accuracy for measuring morphological traits. The extracted traits can be used for monitoring apple tree growth and apple crop load estimation. © 2022 ASABE. All Rights Reserved.

关键词： Robotics

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Enhancing Cyclist Safety: Real-Time Fallen Cyclist Detection and Emergency Notification System

Enhancing Cyclist Safety: Real-Time Fallen Cyclist Detection...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Sri Banu Munisamy Roziyani Rawi Hakim bin Mohd Yasim Nur Farahwahida Ab Aziz Sayed Aziz Sayed Hussin Norilawati Binti Md Jali Computer Engineering Department Universiti Kuala Lumpur Kuala Lumpur Malaysia Foundation Centre Section Universiti Kuala Lumpur Kuala Lumpur Malaysia

1.35 million people die every year due to road accident including cyclist. One of the main reason that contribute to this statistic is delayed response by the rescue team. This project aims to detect the precise location of the victims as well as to notify their next-of-kin immediately about their status. A GPS module, TTGO T- Call with built-in GSM Module and 3-Axis Accelerometer are used to develop the project prototype. The accelerometer sensor detect any fall and send the location of the fallen cyclist to the next-of-kin via SMS. This project benefits the cyclist to get immediate assistance in case of any mishap that is not within the vicinity of other road users.

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Dynamic Network Slicing Control Framework in AI-Native Hierarchical Open-RAN Architecture

Dynamic Network Slicing Control Framework in AI-Native Hiera...

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International Conference on Information Networking

作者： Jongwon Han Minhyun Kim Jung Mo Moon Jeongho Kwak Department of Electrical Engineering and Computer Science DGIST Daegu South Korea ETRI Intelligent Small Cell Research Section Daejeon South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Network slicing is a promising technology in next-generation wireless networks that enables the division of a physical network infrastructure into multiple virtual networks, each of which is tailored for specific service requirements. This approach enables a more flexible allocation of network resources such as beamforming vector, bandwidth and transmit power; thereby effectively supporting services that require high data transmission rates. However, in dynamic network environments where multiple users dynamically move around; hence the interference relationships are dynamically varying, traditional static network slicing solution has critical drawbacks. To this end, for the effective implementation and performance improvement in practical and dynamic network environments, we first propose a dynamic network slicing control framework in AI-native hierarchical Open-RAN (Radio Access Network) architecture where mobility prediction and network controls are designed by multiple timescale decomposition. The proposed framework can facilitate effective network controls, enabling the generation of finely tuned QoS management decisions (power/ bandwidth allocation, user scheduling, beam activation) in different timescales. On top of this framework, we compare the performance of a simple dynamic network slicing algorithm and an existing static network slicing scheme via simulations.

关键词： Array signal processing Network slicing Heuristic algorithms Wireless networks Quality of service Vectors Resource management

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FPGA Implementation of Textural Feature Extractor and Distance Based Classifier for Multi Modal Biometric Pattern Recognition System Using Multi Resolution Approach 3

FPGA Implementation of Textural Feature Extractor and Distan...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Hariprasath, S. Santhi, M. Prabakar, T.N. Koushick, V. Aravind, P. Gunasekaran, T. Saranathan College of Engineering Department of Ece Trichy India Sri Sairam College of Engineering Department of Ece Bangalore Anekal India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Mattu University Department of Electrical and Computer Engineering Ethiopia Electrical and Electronics Engineering Section University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798350322842

The design of biometric pattern recognition with better accuracy, high robustness and good permanence is a significant design issue in pattern recognition system. A pattern recognition system in which the biological and physiological traits of the user are employed to authenticate the users is known as biometric pattern recognition system. Unimodal biometric systems do not have the required characteristics like solidity, circumvention and permanence. When noisy data acquired from the sensor is given as input, the uni modal biometrics system's accuracy may reduce. Besides the noisy input, less variation of parameters of input data belonging to same class may be a cause for decrease in accuracy of the system. Several such limitations may be removed by applying input data from multi biometric sources. In this work, the design of a textural feature extractor and the classifier for a bimodal biometric system is described. The bimodal biometric system is constructed by fusion of iris code vector and palmprint code vector at feature level. In iris pattern, using the gray level dependent matrix values obtained from wavelet packet decomposed sub-images, the features are extracted Using gabor kernel, features are extracted from the palmprint pattern. The feature vector is formed by the concatenation of both the features after normalization is employed. By binarisation, the binary feature vector is created Using Hamming Distance and its variant, the classifier produces the classification result. The RTL description of the Hamming Distance Classifier (HDC) is written using VHDL 2008 language, Verilog HDL, and the implementation is performed on Spartan 6 FPGA. With a speed grade of 4, the propagation delay of the critical path is 330.12 ns. The obtained delay is sufficient for implementing the classifier as an off-chip design for real-time implementation. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Eight-direction least-square phase unwrapping algorithm for optical three-dimensional measurement 3

Eight-direction least-square phase unwrapping algorithm for ...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： He, Yantao Liu, Sijia Pan, Minling Wang, Rongfu Li, Xueri Zhang, Rong Department of Computer Science Guangdong University of Science and Technology No. 2 Songshan Lake Section 523668 China School of Mechanical Engineering Hunan University of Science and Technology Taoyuan Road 411201 China School of Electronics and Information Engineering Beibu Gulf University No. 12 Binhai Avenue 535011 China

ISBN: (纸本)9781510668522

The least-square method is widely used in the fast phase unwrapping of optical three-dimensional measurement due to its high-speed and comprehensive characteristics. In order to solve the problem of measurement error propagation in the phase unwrapping algorithm based on least-square method, an improved eight-direction least-square phase unwrapping algorithm is proposed in this paper. This algorithm not only researches the gradient difference between the target phase and its eight-neighbor phases, improving measurement accuracy and reducing measurement errors. The superiority and robustness of this algorithm have been demonstrated through simulation data and measurement of industrial bearing roller end faces © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Measurement errors

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Parkinson’s Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

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computers, Materials & Continua 2022年第7期72卷 871-886页

作者： Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abubakar Elsafi Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityMahayil62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Science and Artsat MahayilKing Khalid UniversityMahayil62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical *** machine learning algorithms were developed to detect PD ***,these ML methods are lack in generalization and reduced classification performance due to subject *** overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech *** proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden *** consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech *** on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are *** PD datasets is evaluated under 10 fold cross validation without sample *** proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation *** evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

关键词： Dimensionality reduction LDA feature extraction auto encoder classification recurrent neural network LSTM and optimization

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Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

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Evolving Systems 2024年第2期15卷 669-686页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Garfan, Salem Albahri, A.S. Zaidan, B.B. Chen, Juliana Perak Malaysia Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunrlin Douliou64002 Taiwan Discipline of Nutrition and Dietetics Susan Wakil School of Nursing and Midwifery Faculty of Medicine and Health The University of Sydney CamperdownNSW2006 Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Charles Perkins Centre The University of Sydney CamperdownNSW2006 Australia

Mobile apps for sign language are a fascinating area of research that merits a lot of attention. These apps are widely used due to their affordability and usability. Nevertheless, the quality of these apps needs to be evaluated if they are to have a significant influence. The outcomes of app quality assessment can also stimulate app development efforts and improvements to their functionality and accuracy. Therefore, to gain more in depth understanding, four academic databases (Science Direct, IEEE, Association for Computing Machinery, and Web of Science) were searched as part of a systematic literature review. The review highlighted that sign language apps require further development to support users in achieving the greatest possible usage and learning experience. We present a solution framework based on a quality assessment criterion for sign language mobile apps that could be adopted by academics and developers. Alternative identification, criteria, and development are the three primary aspects of the framework. The results demonstrate that the new assessment approach can establish a detailed set of criteria for sign language mobile app development. Future academics could gain a thorough understanding of mobile app development criteria from this study. The results are advantageous and open up a new field where researchers and developers could collaborate on sign language mobile app development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Audition

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