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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Minh Tuan Luc Roman Senkerik Tran Khanh Dang Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Repulic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Vietnam Institute for Experiential Technology Van Lang University Ho Chi Minh city Vietnam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Vietnam

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated Information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements.

关键词： Accuracy Uncertainty Navigation Bitcoin Predictive models Data models Robustness Forecasting Root mean square Standards

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Diagnosis of Pediatrics Epilepsy Based on Graph Analysis of Scalp EEG Applying Mutual Information

Diagnosis of Pediatrics Epilepsy Based on Graph Analysis of ...

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Zarafshan, Elaheh Forouzannezhad, Parisa Rajaei, Hoda Morar, Ulyana Cabrerizo, Mercedes Yaylali, Ilker Pinzon, Alberto Adjouadi, Malek Florida International University Department of Electrical and Computer Engineering FL United States School of Medicine University of Washington Dept. of Radiation Oncology SeattleWA United States Massachusetts Institute of Technology Department of Brain and Cognitive Science MA United States Oregon Health and Science University Department of Neurology PortlandOR United States Neuroscience Center Baptist Hospital of Miami Dept. of Neurology FL United States

ISBN: (纸本)9798350320282

Epilepsy is a brain disorder that causes seizures, affecting nearly half a million children in the US alone. In this study, we aimed to use a nonlinear driven method to characterize scalp EEG recordings of pediatric epilepsy patients (PE: n=7) compared to pediatric control subjects (PC: n=7) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) of all subjects. Next, the FCNs are mapped into the form of undirected graphs that are subjected to the extraction of graph theory-based features. An unsupervised clustering technique based on K-mean is used to delineate the PE from the PC group. Our findings show a statistically significant difference in the mean FCNs between PC and PE groups (t(340)=- 15.9899, p © 2022 IEEE.

关键词： Graph theory

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Convolutional LSTM for Planetary Boundary Layer Height (PBLH) prediction

Convolutional LSTM for Planetary Boundary Layer Height (PBLH...

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AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021

作者： Ziaei, Dorsa Sleeman, Jennifer Halem, Milton Caicedo, Vanessa Delgado, Ruben Demoz, Belay University of Maryland Baltimore County Dept. of Computer Science and Electrical Engineering BaltimoreMD21250 United States University of Maryland Baltimore County Department of Physics BaltimoreMD21250 United States Joint Center for Earth Systems Technology BaltimoreMD21250 United States

We describe new work that uses deep learning to learn temporal changes in Planetary Boundary Layer Height (PBLH). This work is performed in conjunction with a deep edge detection method that identifies edges in imagery based on ceilometer backscatter signal from LIDAR observations. We implement a convolutional Long Short Term Memory (LSTM) to predict small temporal changes in PBLH estimates. In the presence of rain, clouds, and other unfavorable conditions, PBLH heights are challenging to estimate. The convolutional LSTM acts as an internal state representation of the external partially observable environment, supplementing the deep edge detection method, providing a prediction of PBLH in the absence of a reliable estimation. Convolutional LSTMs trained on image-based frames that define the movements of artifacts in the images, such as Moving MNIST digits, have been used to predict the movement of these artifacts for a set of frames in a sequence. We show how a similar network could be extended to learn more complex movement across frames and learn new information introduced at each frame. Utilizing the convolutional LSTM model with our proposed augmentation methodology applied to ten-minute frames, we predicted the change of the movement of edges identified as the PBL over time with favorable accuracy. We show the result of the prediction of PBL-based edges and evaluate the performance using three different metrics. Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Long short-term memory

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Feedback control in RF photonic transversal filter systems based on optical microcombs

Feedback control in RF photonic transversal filter systems b...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Li, Yang Wu, Jiayang Sun, Yang Ren, Guanghui Tan, Mengxi Xu, Xingyuan Corcoran, Bill Chu, Sai Tak Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Integrated Photonics and Applications Centre School of Engineering RMIT University MelbourneVIC3000 Australia School of Electronic and Information Engineering Beihang University Beijing100191 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia Department of Physics City University of Hong Kong Hong Kong QXP Technology Inc. Xi'an China INRS - Énergie Matériaux et Télécommunications VarennesQCJ3X 1S2 Canada

We propose and experimentally demonstrate four different feedback control methods employed in microcomb-based RF photonic transversal filter systems to enhance their accuracy and stability. Detailed characterizations are carried out to compare the performance of different methods. © Optica Publishing Group 2024

关键词： Transversal filters

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Maximizing the accuracy of microcomb-based microwave photonic transversal signal processors

Maximizing the accuracy of microcomb-based microwave photoni...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Sun, Yang Wu, Jiayang Li, Yang Ren, Guanghui Tan, Mengxi Xu, Xingyuan Corcoran, Bill Chu, Sai Tak Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Integrated Photonics and Applications Centre School of Engineering RMIT University MelbourneVIC3000 Australia School of Electronic and Information Engineering Beihang University Beijing100191 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia Department of Physics City University of Hong Kong Hong Kong QXP Technology Inc. Xi'an China INRS - Énergie Matériaux et Télécommunications VarennesQCJ3X 1S2 Canada

ISBN: (纸本)9781957171395

We experimentally implement microcomb-based microwave photonic transversal signal processors and test their accuracy for differentiation, integration, and Hilbert transform. A global picture quantifying the impact of different error sources on the overall performance is provided. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Microwave photonics

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Feedback control in RF photonic transversal filter systems based on optical microcombs

Feedback control in RF photonic transversal filter systems b...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Transversal filters

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Repairing with Zero Skip Cost

Repairing with Zero Skip Cost

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IEEE International Symposium on Information Theory

作者： Yeow Meng Chee Son Hoang Dau Tuvi Etzion Han Mao Kiah Yuan Luo Wenqin Zhang Dept. of Industrial Systems Engineering and Management National University of Singapore School of Computing Technologies RMIT University Melbourne VIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio... 详细信息

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

关键词： Measurement Costs Codes Maintenance engineering

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Maximizing the accuracy of microcomb-based microwave photonic transversal signal processors

Maximizing the accuracy of microcomb-based microwave photoni...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Microwave photonics

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Beam Quality Analysis of Mid Infrared Tree-Array Quantum Cascade Lasers (QCLs) Based on Multi-Mode Interference (MMI) Couplers and Broad-Area Emitters 22

Beam Quality Analysis of Mid Infrared Tree-Array Quantum Cas...

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Novel In-Plane Semiconductor Lasers XXII 2023

作者： Milbocker, Luke Shu, Hong Desai, Dhruvkumar Li, Guifang Sanchez, Enrique Lyakh, Arkadiy Dept. of Electrical and Computer Engineering University of Central Florida 4000 Central Florida Blvd OrlandoFL32816 United States IRGLARE LLC 3259 Progress Drive OrlandoFL32826 United States University of Central Florida 4000 Central Florida Blvd OrlandoFL32816 United States Nanoscience Technology Center University of Central Florida 4000 Central Florida Blvd OrlandoFL32816 United States

ISBN: (纸本)9781510659858

The beam quality of ridge-waveguide quantum cascade laser arrays with broad-area emitters and multi-mode interference (MMI) couplers is investigated both experimentally and numerically. Previous demonstrations of MMI QCL arrays had narrow ridge waveguides to ensure fundamental mode operation and phase locking between elements of the array. In the interest of scaling optical power with lateral waveguide dimensions, we demonstrate broad area tree-arrays with MMI couplers at a wavelength of 4.65µm and ridge widths between 13 and 17µm. The emitted beams from the stem’s side are characterized with M2 measurements. We show that the MMI coupled arrays generally have significantly improved beam quality compared to Fabry Perot resonators with the same dimensions. Optimized tree-array devices will be the cornerstone of the next generation high power infrared systems. © 2023 SPIE.

关键词： Quantum cascade lasers

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Speaker Identification from Emotional and Noisy Speech Using Learned Voice Segregation and Speech Vgg

SSRN

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SSRN 2022年

作者： Hamsa, Shibani Shahin, Ismail Iraqi, Youssef Damiani, Ernesto Nassif, Ali Bou Werghi, Naoufel Center for Cyber-Physical Systems Dept. of ECE Khalifa University Abu Dhabi United Arab Emirates Dept. of Electrical Engineering University of Sharjah Sharjah United Arab Emirates School of Computer Science Mohammed VI Polytechnic University Morocco Dept. of Computer Science Engineering University of Sharjah Sharjah United Arab Emirates

Speech signals are subjected to more acoustic interference and emotional factors than other signals. Noisy emotion-riddled speech data is a challenge for real-time speech processing applications. It is essential to find an effective way to segregate the dominant signal from other external influences. An ideal system should have the capacity to accurately recognize required auditory events from a complex scene taken in an unfavorable situation. This paper proposes a novel approach to speaker identification in unfavorable conditions such as emotion and interference using a pre-trained Deep Neural Network mask and speech VGG. The proposed model acquired superior performance over the recent literature using English and Arabic emotional speech data and reported an average speaker identification rate of 85.2\%, 87.0\%, and 86.6\% using the Ryerson audio-visual dataset (RAVDESS), speech under simulated and actual stress (SUSAS) dataset and Emirati-accented Speech dataset (ESD) respectively. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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