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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Katmah, Rateb Al-Shargie, Fares Tariq, Usman Babiloni, Fabio Al-Mughairbi, Fadwa Al-Nashash, Hasan American University of Sharjah Biomedical Engineering Graduate Program Sharjah United Arab Emirates American University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates University of Sapienza Rome Department of Molecular Medicine Rome Italy United Arab Emirates University College of Medicines and Health Sciences Al-Ain United Arab Emirates

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Mental stress is a significant factor in the development of a wide variety of psychological, emotional, behavioral, and physical illnesses. It is critical to accurately quantify mental stress, which needs reliable neuroimaging to monitor stress levels. In this work, we used a modified Stroop Color Word Task (SCWT) with time constraints and negative feedback to elicit two distinct degrees of stress in the workplace. We then used salivary alpha amylase (SAA) concurrently with functional near-infrared spectroscopy (fNIRS) to quantify the level of stress. We propose Long Short-Term Memory (LSTM) to decode the two classes of mental stress based on the fNIRS time series. Five-fold cross validation, two LSTM layers, as well as many trainable characteristics were used to construct the network. The induced mental stress increased the level of salivary alpha amylase significantly (p © 2022 IEEE.

关键词： Long short-term memory

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning 7

Improving Candle Direction Classification in Forex Market Us...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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Alignment-Free Estimation of Read to Genome Distances and Its Applications 29th

Alignment-Free Estimation of Read to Genome Distances and...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Şapcı, Ali Osman Berk Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States

ISBN: (纸本)9783031902512

Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matching references could further help downstream analyses such as taxonomic characterization or even placing the read on a reference phylogeny. Such distances can be computed using alignment. Since alignment becomes impractical for ultra-large reference databases, many use k-mer-based tools for downstream tasks, but these methods do not compute distances. We introduce krepp to compute the distance between a read and a genome using a maximum likelihood framework built around k-mers. We further introduce algorithms to place reads on a reference tree. Empirical evaluations show that krepp accurately estimates distances, scales well with large databases, and can place short reads coming from any part of the genome on a reference phylogeny. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Average nucleotide identity Metagenomics Phylogenetic placement

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CVIT: CONTINUOUS VISION TRANSFORMER FOR OPERATOR LEARNING

arXiv

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arXiv 2024年

作者： Wang, Sifan Seidman, Jacob H. Sankaran, Shyam Wang, Hanwen Pappas, George J. Perdikaris, Paris Institution for Foundation of Data Science Yale University United States Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer (CViT), a novel neural operator architecture that leverages advances in computer vision to address challenges in learning complex physical systems. CViT combines a vision transformer encoder, a novel grid-based coordinate embedding, and a query-wise cross-attention mechanism to effectively capture multi-scale dependencies. This design allows for flexible output representations and consistent evaluation at arbitrary resolutions. We demonstrate CViT’s effectiveness across a diverse range of partial differential equation (PDE) systems, including fluid dynamics, climate modeling, and reaction-diffusion processes. Our comprehensive experiments show that CViT achieves state-of-the-art performance on multiple benchmarks, often surpassing larger foundation models, even without extensive pretraining and roll-out fine-tuning. Taken together, CViT exhibits robust handling of discontinuous solutions, multi-scale features, and intricate spatio-temporal dynamics. Our contributions can be viewed as a significant step towards adapting advanced computer vision architectures for building more flexible and accurate machine learning models in the physical sciences. All data and code are publicly available at https://***/PredictiveIntelligenceLab/cvit. © 2024, CC BY-NC-SA.

关键词： Computer vision

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Identification of D- and L-Phenylalanine Enantiomeric Mixtures by Employing Deep Neural Network Models

SSRN

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SSRN 2023年

作者： Nigdelioglu, Ebru Toprak, Elif Guney Akkurt, Melike Erol Barkana, Duygun Kazanci, Murat Uyaver, Sahin Calik, Nurullah Istanbul Medeniyet University Nanoscience and Nanoengineering Program Graduate School Istanbul Medeniyet University Istanbul34700 Turkey Istanbul Medeniyet University School of Engineering and Natural Sciences Department of Biomedical Engineering Istanbul Turkey Istanbul34700 Turkey Yeditepe University Faculty of Engineering Department of Electrical and Electronics Engineering Kayışdagı Istanbul34755 Turkey Marmara University Faculty of Technology Department of Computer Engineering Maltepe Istanbul34854 Turkey

Phenylalanine is an aromatic essential amino acid that exhibits the tendency to self-aggregate into fibrillar structures in its enantiomerically pure form. This observation was indicated as the underlying mechanism of phenylketonuria, which is a genetic condition associated with various neurological, physical, and developmental issues, characterized with phenylalanine buildup in the brain. The presence of D-phenylalanine was demonstrated previously to inhibit the formation of fibrils by L-phenlyalanine, indicating its potential use in phenylketonuria treatment. In this study, several combinations of D and L-phenylalanine were examined with the help of state-of-the-art deep learning methods for their fibril forming capacity, demonstrating the usefulness and accuracy of deep learning methods in distinguishing between different self-assembled structures. © 2023, The Authors. All rights reserved.

关键词： Self assembly

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Joint Design of Spirograph Method and Reconfigurable Intelligent Surface for Future Communication Systems

Joint Design of Spirograph Method and Reconfigurable Intelli...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Balqis Yafis Jane-Hwa Huang Chih-Min Yu Li-Chun Wang EECS Int’l Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Dept. of Electrical Engineering National Chi Nan University Taiwan Department of Information and Computer Engineering Chung Yuan Christian University Taiwan Dept. of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead.

关键词： Wireless communication Quantization (signal) Array signal processing Simulation Design methodology Reconfigurable intelligent surfaces Reflection coefficient Global communication

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Circular DGS Resonator for Non-Invasive Sensing of Diabetes

Circular DGS Resonator for Non-Invasive Sensing of Diabetes

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European Conference on Antennas and Propagation, EuCAP

作者： Hala M. Marzouk Anwer S. Abd El-Hameed Ahmed Allam Ramesh K. Pokharel Adel B. Abdel-Rahman Electronics and Communications Engineering Department Egypt-Japan University of Science and Technology E-JUST Alexandria Egypt Electronics and Communications Engineering Department Zagazig University Zagazig Egypt Microstrip Circuits Department Electronics Research Institute ERI Cairo Egypt Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan South Valley University Qena Egypt

A millimeter wave defected ground structure (DGS) resonator is introduced for noninvasive blood glucose concentrations detection. The unloaded sensor is designed to operate at 16 GHz. The sensing process is carried out by measuring the variations in the resonant frequency, and S-parameters phase and magnitude. The planned resonator is justified by time domain and frequency domain measurements. Several glucose solution concentrations are considered through experiments from 0 to 1000 mg/dL, providing high sensitivity of 1.4 dB/ (100mgdL -1 ) and quality factor of 61.08. Three volunteers assisted to check the potential sensor sensitivity by measuring their blood glucose levels. The actual permittivity of the human body affects the measurements and shifts down the frequency from 16 GHz to 5 GHz, while in simulation the frequency of the loaded sensor is 12 GHz. The presented sensor reveals high sensitivity levels in both simulations and measurements.

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Mitigation of PAM4 rolling shuttered pattern grayscale ambiguity in demodulation utilizing long short term memory neural network (LSTM-NN) in optical wireless communication systems

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Optics Communications 2023年 532卷

作者： Peng, Ching-Wei Chow, Chi-Wai Tsai, Deng-Cheng Liu, Yang Yeh, Chien-Hung Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Chiao Tung University Hsinchu30010 Taiwan Philips Electronics Ltd. Shatin Hong Kong Department of Photonics Feng Chia University Taichung40724 Taiwan

One realization of optical wireless communication (OWC) is optical camera communication (OCC), which utilizes mobile-phone and surveillance cameras for communication. One main limitation of OCC system is the low frame rate of typical cameras. To enhance the OCC data rate with limited frame rate cameras, rolling shuttered camera mode can be utilized. However, decoding the rolling shuttered pattern is very challenging. In addition, utilizing multi-level modulation formats, such as four-level pulse-amplitude-modulation (PAM4) can increase the OCC bit-per-symbol;however, it would be a great challenge to identify the multiple intensity levels owing to the uneven aggregated light exposure of each pixel-row. In this work, we put forward and demonstrate utilizing the Long-Short-Term Memory neural-network (LSTM-NN) to alleviate the decoding of high inter-symbol-interference (ISI) of PAM4 rolling shuttered pattern. The experiment is performed in a practical room with 3-m free-space transmission distance. Results show that the proposed LSTM-NN method can significantly enhance the decoding of multiple intensity level rolling shuttered pattern. The proposed scheme also allows a large tilting angle of ±70° between the transmitter (Tx) and receiver (Rx). © 2023 Elsevier B.V.

关键词： Long short-term memory

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Study on electrical characterization of hexagonal-BN

Study on electrical characterization of hexagonal-BN

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IEEE CPMT Symposium Japan

作者： Riki Yamanishi Soshi Motoda Satoko Shinkai Masataka Hasegawa Department of Electrical Engineering and Electronics Graduate School of Kyushu Institute of Technology Fukuoka Japan Department of Advanced Infomatics Graduate School of Kyushu Institute of Technology Fukuoka Japan Department of Electrical Engineering and Electronics National Institute of Advanced Industrial Science and technology Ibaraki Japan

ISBN: (纸本)9781665486149

Hexagonal-BN (h-BN) based Silicon on Insulator(SOI) structure with Through Silicon Via (TSV) shows higher heat dissipation performance without degradation electrical characteristics compared with the conventional SOI structure. The electrical characteristics and the results are not clear. It is important to clarify the electrical characteristics of h-BN to implement 3D-power Supply on Chip(SoC). This paper investigates electrical characterization of h-BN.

关键词： Degradation Dielectric constant Electric breakdown Silicon-on-insulator Silicon Through-silicon vias Permittivity

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Static Output Feedback Stabilizing Control for Takagi-Sugeno Fuzzy Systems 10

Static Output Feedback Stabilizing Control for Takagi-Sugeno...

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Joint 10th International Conference on Informatics, electronics and Vision, ICIEV 2021 and 2021 5th International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2021

作者： Asai, Yuto Itami, Taku Yoneyama, Jun Graduate School of Science and Engineering Aoyama Gakuin University Kanagawa Sagamihara252-5258 Japan Department of Electrical Engineering and Electronics Aoyama Gakuin University Kanagawa Sagamihara252-5258 Japan

ISBN: (纸本)9781665449212

This paper introduces a new static output feedback controller design method for continuous-time Takagi-Sugeno fuzzy systems, which can represent a wide class of nonlinear systems. Although many papers discuss state feedback controller design for fuzzy sytems, a fewer number of papers consider an output feedback controller design due to its difficulty. In this paper, our target is to propose an output feedback controller design method. In our control design, the integral structure of the membership functions, which are the same properties as the original membership functions, is utilized. A similar approach for state feedback controller design methods has been in the literature. Our proposed output feedback controller is designed based on a new fuzzy Lyapunov function which includes the double integral of the original membership functions of the system, and it shows a wider stabilizing area than the existing methods. At the end of the paper, a numerical example is provided to illustrate the newly proposed controller design method and to asymptotically stabilize the system. Contribution-Output feedback control based on new Lyapunov function including double integral function. © 2021 IEEE

关键词： Linear matrix inequalities

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