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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Sun, Shiyuan Yang, Fang Mei, Weidong Song, Jian Han, Zhu Zhang, Rui University of Electronic Science and Technology of China Chengdu611731 China Department of Electronic Engineering Beijing100084 China Research Institute Tsinghua University Shenzhen518057 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Shenzhen Research Institute of Big Data Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9798350351255

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm. © 2024 IEEE.

关键词： Channel estimation

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

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Procedia computer science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Decomposition and Reorganization of Phonetic Information for Speaker Embedding Learning

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 1745-1757页

作者： Hong, Qian-Bei Wu, Chung-Hsien Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701 Taiwan Academia Sinica Taipei115 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions. © 2014 IEEE.

关键词： Speech recognition

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Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives

arXiv

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arXiv 2023年

作者： Krestinskaya, Olga Zhang, Li Salama, Khaled Nabil Electrical and Computer Engineering Program the Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge-based processing. Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications. Network compression techniques are applied to implement a neural network on limited hardware resources. Quantization is one of the most efficient network compression techniques allowing to reduce the memory footprint, latency, and energy consumption. This paper provides a comprehensive review of IMC-based Quantized Neural Networks (QNN) and links software-based quantization approaches to IMC hardware implementation. Moreover, open challenges, QNN design requirements, recommendations, and perspectives along with an IMC-based QNN hardware roadmap are provided. © 2023, CC BY.

关键词： Energy utilization

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Hybrid Global Flexible Power Point Tracking Algorithm for Photovoltaic Systems

Hybrid Global Flexible Power Point Tracking Algorithm for Ph...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Kumaresan, Anusha Tafti, Hossein Dehghani Farivar, Glen G. Pou, Josep Strunz, Kai Niyato, Dusit Nanyang Technological University Energy Research Institute at Ntu Interdisciplinary Graduate Programme Singapore Australia The University of Melbourne Department of Electrical and Electronic Engineering VIC3010 Australia Nanyang Technological University School of Electrical and Electronic Engineering Singapore Technische Universität Chair for Sustainable Electric Networks and Sources of Energy Berlin Germany Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350364446

Frequency support in grid-connected photovoltaic (PV) systems is achieved by employing flexible power point tracking (FPPT) algorithms. Subsequently, according to the grid frequency requirements, the PV power is curtailed to a power reference, unlike the traditional method of operating the PV at the maximum power point. In practical cases, the PV arrays are prone to partial shading, and hence, there is a requirement to formulate the FPPT algorithms that operates effectively during such cases. Moreover, fast transient response is a necessity, which implies that the algorithms proposed in this paper is desirable. This paper proposes a hybrid global FPPT (GFPPT) algorithm based on secant and Newton's method, to shift the PV operating point to the required power reference. The Newton's method offers quadratic convergence rate and the secant method guarantees convergence. Hence, a hybrid algorithm consisting of these two methods will provide both fast and guaranteed convergence. Validation through simulation is presented in this paper and the proposed method is compared with the recently presented perturb and observe (P&O) GFPPT algorithm in the literature. © 2024 IEEE.

关键词： Newton-Raphson method

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Sensing beyond the exceptional point for high detectivity

Sensing beyond the exceptional point for high detectivity

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Li, Zhichao Prasad, Ciril S. Wang, Xielin Zhang, Ding Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States

ISBN: (纸本)9781957171395

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity. Slightly off EP operation boosts detectivity without much loss in sensitivity. We experimentally demonstrate a high-detectivity-off-EP plasmonic-photonic hybrid sensor for anti-mouse IgG protein. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Laser beams

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Design and Implementation of 2-DOF Arm Manipulator Robot as Robotic Kinematic Learning Platform 6

Design and Implementation of 2-DOF Arm Manipulator Robot as ...

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6th International Conference on Vocational Education and electrical engineering, ICVEE 2023

作者： Widayaka, Parama Diptya Puspitaningayu, Pradini Alamsyah, Sayyidul Aulia Muktiadji, Rifqi Firmansyah Widodo, Arif Abdillah, Rifqi Universitas Negeri Surabaya Dept. of Electrical Engineering Surabaya Indonesia King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia Okayama University Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayamas Japan Institut Teknologi Sepuluh Nopember Surabaya Dept. of Informatics Surabaya Indonesia

ISBN: (纸本)9798350326642

Technological developments have impacts and challenges in human life. Technological advances have made human life easier. The impact of technological developments is the industrial manufacturing process getting faster with the help of robotics technology. Robots can shorten manufacturing process time and efficient. On the other hand, challenges with technological developments force us to keep learning and adaptive to every changes around us, especially in the education sector. In education sector, formal educational institutions or campuses must be able to facilitate students to be able to learn comprehensively in the field of technology, especially robotics, where we can make an innovative learning platform based on robotics technology. In this study, we discussed a 2-DOF arm manipulator robot as kinematics learning platform of industrial robot to increase student competencies in preparation for facing the industrial environment in century. Inverse kinematics equation has been applied in the robot by providing input coordinates on the x-Axis and y-Axis where the accuracy value on the x-Axis is 77% and the accuracy on the y-Axis is 83.1%. © 2023 IEEE.

关键词： Machine design

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Brain Tumor Detection and Segmentation Using Deep Learning Models 6

Brain Tumor Detection and Segmentation Using Deep Learning M...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Abiodun, Moses Kazeem Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Aworinde, Halleluyah Tinuoye, George Imoize, Agbotiname Lucky Li, Chun-Ta Lee, Cheng-Chi Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bowen University Department of Computer Science Osun State Iwo Nigeria University of Ilorin Department of Computer Science Kwara State Ilorin Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Nigeria Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City Taiwan Fu Jen Catholic University Department of Library and Information Science New Taipei City Taiwan

ISBN: (纸本)9798350374162

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Optically Controlled Charge Trapping Memory Based on Spin Coated Hafnium Diselenide Flakes

Optically Controlled Charge Trapping Memory Based on Spin Co...

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IEEE Conference on Nanotechnology

作者： Bashayr Alqahtani Nazek El Atab King Abdullah University of Science and Technology Program of Electrical and Computer Engineering Computer Electrical Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia

This paper demonstrates the seamless fabrication of optoelectronic memory by integrating HfSe 2 as a charge-trapping layer in a MOS memory structure. Through a spin coating technique, solution-processable HfSe 2 flakes with average thicknesses of 2 nm were deposited between the tunneling and blocking oxide layers. The charge-trapping material distribution and thickness were explored by Atomic Force Microscopy and X-ray Diffraction Spectroscopy. The electrical characterization of MOS memory revealed a memory window of 5.5 Volts under ±16 Volts biasing. Furthermore, the memory endurance exceeds 10 4 electrical programming and erasing cycles. The retention test performed at room temperature showed that the memory device is expected to lose only 10% of the stored charges after 10 years. Under light stimuli (405nm wavelength and output power ~ 20 mW) with electrical readout voltage, the MOS memory showed an increase in the memory window from 5.5 Volts to 6.5 Volts.

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Physics-aware cross-domain fusion aids learning-driven computer-generated holography

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Photonics Research 2024年第12期12卷 2747-2756页

作者： GANZHANGQIN YUAN MI ZHOU FEI LIU MU KU CHEN KUI JIANG YIFAN PENG ZIHAN GENG Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518071China School of Optoelectronic Engineering Xidian UniversityXi’an 710071China Department of Electrical Engineering City University of Hong KongHong Kong SARChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electrical and Electronic Engineering The University of Hong KongHong Kong SARChina Department of Computer Science The University of Hong KongHong Kong SARChina

The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent ***,a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram(POH)*** computational models often struggle to address the inherent complexities of optical phenomena,compromising the conversion *** this study,we present the cross-domain fusion network(CDFN),an architecture designed to tackle the complexities involved in POH *** CDFN employs a multi-stage(MS)mechanism to progressively learn the translation from amplitude to phase domain,complemented by the deep supervision(DS)strategy of middle features to enhance task-relevant feature learning from the initial ***,we propose an infinite phase mapper(IPM),a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of *** simulations,our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset,achieving an average PSNR of 31.68 dB and SSIM of ***,we realize high-quality color image reconstruction in optical *** experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.

关键词： computer cross holography

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