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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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Widefield Heterodyne Optical Coherence Microscopy for Volumetric Vibration Imaging

Widefield Heterodyne Optical Coherence Microscopy for Volume...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Choi, Samuel Yoshimizu, Kaito Ota, Takeru Nin, Fumiaki Hibino, Hiroshi Muramatsu, Shogo Suzuki, Takamasa Niigata University Department of Electrical and Electronics Engineering 8050 Ikarashi-2 Niigata950-2181 Japan Osaka University Graduate School of Medicine Division of Glocal Pharmacology Department of Pharmacology 2-2 Yamadaoka Osaka Suita565-0871 Japan Gifu University Graduate School of Medicine Division of Biological Principles Department of Physiology 1-1 Yanagido Gifu501-1194 Japan AMED-CREST AMED Japan

ISBN: (纸本)9798350350012

We propose a method for simultaneous tomographic vibration visualization of an entire volume of a target object using a scanning low-coherence interferometric microscope. By combining the difference frequency component produced by the phase modulated reference beam and the object vibration into the time-domain OCT signal, we could capture the internal vibration distribution at a frequency of 43 kHz, which far exceeds the frame rate of the CMOS image sensor as a detector, with 25 nm sensitivity. © IEEE 2022.

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Widefield Heterodyne Optical Coherence Microscopy for Volumetric Vibration Imaging

Widefield Heterodyne Optical Coherence Microscopy for Volume...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Choi, Samuel Yoshimizu, Kaito Ota, Takeru Nin, Fumiaki Hibino, Hiroshi Muramatsu, Shogo Suzuki, Takamasa Niigata University Department of Electrical and Electronics Engineering 8050 Ikarashi-2 Niigata950-2181 Japan Osaka University Graduate School of Medicine Division of Glocal Pharmacology Department of Pharmacology 2-2 Yamadaoka Suita Osaka565-0871 Japan Gifu University Graduate School of Medicine Division of Biological Principles Department of Physiology 1-1 Yanagido Gifu501-1194 Japan AMED-CREST AMED Japan

关键词： Time domain analysis

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40.1-GHz sub-freezing 850-nm VCSEL: microwave extraction of cavity lifetimes and small-signal equivalent circuit modeling

40.1-GHz sub-freezing 850-nm VCSEL: microwave extraction of ...

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作者： Cheng, Hao-Tien Wu, Cheng-Han Feng, Milton Wu, Chao-Hsin Graduate Institute of Electronics Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Center for Quantum Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Nick Holonyak Jr. Micro and Nanotechnology Laboratory Department of Electrical and Computer Engineering the University of Illinois at Urbana-Champaign 208 N. Wright St. UrbanaIL61801 United States Graduate School of Advanced Technology National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan

We present an 850-nm vertical-cavity surface-emitting laser (VCSEL) constructed for a wide operating temperature range from 25°C to -50°C sub-freezing temperature, demonstrating 40.1-GHz at -50°C. The optical spectra, junction temperature, and microwave equivalent circuit modeling of a sub-freezing 850-nm VCSEL between -50°C and 25°C are also discussed. Reduced optical losses, higher efficiencies, and shorter cavity lifetimes at sub-freezing temperatures are the leading causes of the improved laser output powers and bandwidths. The e-h recombination lifetime and the cavity photon lifetime are shortened to 113 and 4.1 ps, respectively. Could potentially supercharge VCSEL-based sub-freezing optical links for applications in frigid weather, quantum computing, sensing, aerospace, etc. © 2023 Optica Publishing Group.

关键词： Surface emitting lasers

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Rate-fidelity trade-off in cavity-based remote entanglement generation

arXiv

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arXiv 2024年

作者： Tanji, Kazufumi Takahashi, Hiroki Roga, Wojciech Takeoka, Masahiro Department of Electronics and Electrical Engineering Faculty of Science and Engineering Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama-shi Kanagawa223-8522 Japan Experimental Quantum Information Physics Unit Okinawa Institute of Science and Technology Graduate University 1919-1 Tancha Onna Kunigami Okinawa904-0495 Japan

The qubit scalability imposes a paramount challenge in the field of quantum computing. Photonic interconnects between distinct quantum computing modules provide a solution to deal with this issue. The fundamental part of this approach is entanglement distribution via travelling photons emitted by matter qubits. However, randomness of the spontaneous emission in the matter qubits limits both the entanglement fidelity and the generation rate. In this paper, by numerical and analytical methods, we investigate the relationship between the entanglement affected by the spontaneous emission and the waveform of the pump pulse used in the photon generation. We confirm and analyze a rate-fidelity trade-off in the entanglement swapping with Gaussian pump pulses and show that a simple extension to non-Gaussian pump pulses improves the trade-off in a certain parameter region. Furthermore we extend our analysis to entanglement distribution in the general multipartite setting and show that the analysis of the bipartite entanglement can be straightforwardly applied in this case as well. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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In Regard to Gouveia et al.

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International Journal of Radiation Oncology Biology Physics 2023年第1期115卷 247-248页

作者： Chang, En-Wei Liu, Chia-Wen Dong, Chen Kao, Yung-Shuo Ma, Kevin Sheng-Kai Wei, James Cheng-Chung School of Medicine Chung-Shan Medical University Taichung Taiwan Department of Radiation Oncology China Medical University Hospital Taichung Taiwan Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Department of Dentistry Chung Shan Medical University and Chung Shan Medical University Hospital Taichung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan

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Color Retention Assessment of Duhat Dye to Cotton Fabric Using Color Fastness Test and Digital Image Processing

Color Retention Assessment of Duhat Dye to Cotton Fabric Usi...

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Computing (ICOCO), IEEE International Conference on

作者： Jilbert Novelero Rennel Mallari Jennifer Dela Cruz Donna Gomez Ricardo Banal School of Electrical Electronics and Computer Engineering Mapua University Metro Manila Philippines College of Engineering and Architecture Angeles University Foundation Angeles City Philippines School of EECE and School of Graduate Studies Mapua University Manila Philippines ECE Department Systems Plus College Foundation College of Engineering Angeles City Philippines

Synthetic dyes have changed perceptions of natural dyes, but their chemical origins pose environmental risks. Seeking an eco-friendly alternative, the indigenous fruit duhat from the Philippines shows promise as a potential dye for cotton fabrics. This study aimed to explore duhat's dyeing capability using a cost-effective and non-invasive testing technique. The evaluation began by creating a duhat filtrate and dyeing white cotton fabrics following standard procedures from the AATCC 61 1A handbook for color fastness testing through washing. The results and statistical analysis showed that when 150g of duhat skin was mixed with 50 mL of ethyl alcohol, it produced a filtrate with commendable color retention. An image enhancement technique converted RGB images to grayscale, aiding visual grading of color changes in cotton fabrics. The duhat skin dye scored a grade of 4 for noticeable color change and a grade of 3 for moderate staining on adjacent undyed fabric, according to the results from the gray scale for color change and staining. Furthermore, a paired t-test on the numerical data indicated no significant color change after the color fastness test compared to the fabric's original color. In conclusion, the experimentation suggests that duhat could serve as a potential natural dye source for cotton fabrics. Embracing duhat's coloring properties may reduce our reliance on synthetic dyes and help protect the environment.

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An Investigation of Heuristic Control Strategies for Multi-Electrolyzer Wind-Hydrogen Systems Considering Degradation

An Investigation of Heuristic Control Strategies for Multi-E...

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Control Technology and Applications (CCTA),

作者： Zachary Tully Genevieve Starke Kathryn Johnson Jennifer King Advanced Energy Systems Graduate Program Colorado School of Mines Golden CO USA National Renewable Energy Laboratory Golden CO USA Electrical Engineering Department Colorado School of Mines Golden CO USA

With the growing demand for renewable-energy-powered hydrogen generation and the corresponding increase in plant capacity, individually controlling many electrolyzer stacks will be critical for increasing the plant’s lifetime and efficiency. This paper introduces a rule-based controller framework targeting electrolyzer degradation to explore the opportunity space in multi-stack hydrogen plant control. A novel control-oriented degradation model is also presented in this work, which quantifies the impact of steady power input, fluctuating power input, and the number of startup/shutdown cycles on electrolyzer degradation. Using a wind power input signal, 13 controller configurations are tested on 5 MW hydrogen plants consisting of 2, 5, 10, and 25 stacks. These configurations are evaluated on their capability to mitigate electrolyzer degradation and efficiently produce hydrogen from the time-varying input power. The results show that multi-stack control for degradation can extend the plant’s lifetime by more than a factor of 3 with minimal impact on instantaneous hydrogen production.

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Towards a Machine Learning Based Method for Indirect Test Generation of Mixed-Signal Circuits

Towards a Machine Learning Based Method for Indirect Test Ge...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Állan F. G. Ferreira Lucas B. Zilch Vinícius Navarro Marcelo S. Lubaszewski Tiago R. Balen Electrical Engineering Department Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Microelectronics - PGMICRO Universidade Federal do Rio Grande do Sul Porto Alegre Brazil

This work presents the proposal and feasibility evaluation of a machine learning (ML) based method for test generation of analog and mixed-signal circuits. The method first relies on the use of a previously proposed low-cost automatic test generation tool (based on spice simulation) to simulate the circuit specifications and low cost parametric measures (in this case, DC values of circuit nodes under different input stimuli). Variability data, based on Monte Carlo Simulation is also generated by the tool, by using an open source spice simulator and the appropriate foundry models of the transistors. With this dataset in hand, the method consists on training ML models to relate the low cost DC node measurements with the circuit specifications, configuring a well-known indirect test strategy. The models are evaluated and those with the best performance are selected, resulting in a set of low-cost measurements and ML algorithms that represent an indirect test strategy to predict the CUT tested specifications. The considered case study is a fully-differential integrated filter designed on a commercial 180 nm process. Results show a R 2 score equal to 0.76, 0.76 and 0.98 for the DC gain, cutoff frequency and slew rate parameters, respectively. A total of 19 indirect measurements were selected for performing the tests. Finally, the presented method facilitates the implementation of the indirect test in analog circuits. A wide range of design parameters can be explored, selecting those that best contribute to the quality of the indirect test.

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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