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9th International Conference on Advanced informatics: Concepts, Theory and Applications, ICAICTA 2022

作者： Yulianti, Lenny Putri Santoso, Judhi Trisetyarso, Agung Surendro, Kridanto Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Bina Nusantara University Program - Computer Science Bandung Indonesia

ISBN: (数字)9781665456685

ISBN: (纸本)9781665456685

Ensemble learning has gained attention due to its ability to improve predictive performance in classification and regression problems. However, there are several issues related to optimizing accuracy, diversity, and efficiency simultaneously in the ensemble learning process. Existing studies mostly focus on one or two of them, or focus on all three but only on the specific process of ensemble learning. Besides, the quantum approach has been expected to accelerate the optimization process while maintaining accuracy. The contribution of this study is to design a hybrid classical-quantum methodology for optimizing ensemble learning in a holistic way. This methodology combines classical (i.e. clustering, improved evaluation measures, and generalized weighted ensemble with internally tuned hyperparameters) and quantum (i.e. quantum annealing) approaches, and this methodology is analyzed to provide more optimal results. © 2022 IEEE.

关键词： Learning systems

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Sensing-Assisted Channel Estimation in ISAC Systems 14th

Sensing-Assisted Channel Estimation in ISAC Systems

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Yin, Rui Shi, Yexin Qi, Wei Chen, Xianfu Wu, Celimuge Ji, Yusheng School of Information and Electrical Engineering Hangzhou City University Hangzhou310018 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Shenzhen CyberAray Network Technology Company Ltd Shenzhen China Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9783031862021

In future Sixth-Generation (6G) mobile communication, satellite communication is a crucial means of extending mobile networks worldwide. However, in satellite communication systems, the transmission capacity between ground stations and users has become a bottleneck for satellite communication systems. For this issue, Integrated Sensing and Communication (ISAC) technology and Artificial Intelligence (AI), which has been widely studied, has the potential to offer excellent solutions. This paper explores a Uformer-based sensing-assisted communication system that enhances channel estimation accuracy through the design of a Uformer-based channel estimation enhancement algorithm. By formulating a global optimization problem, we seek to improve resource efficiency through finding the optimal resource allocation schemes. Initially, the system utilizes radar data to obtain reconstructed channels and employs communication data for channel estimation. The proposed neural network model then fuses these data into a new and more accurate channel state information. Finally, we present a resource allocation algorithm and validate the performance of all proposed algorithms through simulations. © ICST institute for Computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Resource allocation

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A CCII-Based Electrocardiogram Amplifier with DC Suppression

A CCII-Based Electrocardiogram Amplifier with DC Suppression

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2023 International electrical engineering Congress, iEECON 2023

作者： Pantuprecharat, Panlop Sitjongsataporn, Suchada Pawarangkoon, Prajuab Mahanakorn University of Technology The Electrical Graduate Program Faculty of Engineering and Technology Bangkok Thailand Mahanakorn University of Technology Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Pathumwan Institute of Technology Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798350332360

This paper presents the design of a CCII-based electrocardiogram (ECG) amplifier for ECG measurement using two second generation current conveyors (CCII) and five passive devices. An ECG amplifier is proposed in term of compact circuit and DC suppression for ECG measurement. The proposed circuit has the ability to suppress DC and out of band high frequency interference. The proposed circuit was simulated using a commercially available current conveyors AD844 and tested by PSPICE program. The simulation results show that the proposed amplifier attains a mid-band gain of 46 dB, Common Mode Rejection Ratio (CMRR) of 57.76 dB at 10 Hz, and input referred noise is 1.62 μVrms and the dynamic range is 66.78 dB, respectively. © 2023 IEEE.

关键词： Electrocardiograms

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Mechanism of Threshold Voltage Instability in Double Gate α-IGZO Nanosheet TFT under Bias and Temperature Stress

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IEEE Journal of the Electron Devices Society 2024年 12卷 464-471页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan City701401 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for CMOS-compatible, ultra-scaled, reliable, and high-performing devices, we fabricate and analyze scaled-channel a-IGZO-TFTs with an optimal double-gate structure, a thin nanosheet-based channel, and an effective high- κ dielectric namely HfO2. The reliably reported double gate IGZO nanosheet TFTs (DG-IGZO-NS-TFTs) are tested under positive and negative bias stress at variant temperatures, and the resulting modulations are analyzed critically. The reported DG-IGZO-NSTFTs exhibit a negative side threshold voltage shift (Δ Vth) accompanied by an increase in Ion/Ion(0) under negative bias temperature stress (NBTS) at elevated temperatures, which indicates the presence of additional charges. An anomalous negative side shifting of the Vth is observed under positive bias temperature stress (PBTS), where diffused hydrogen atoms are identified as introducing excess n-type carriers into the channel and causing the observed Δ Vth. The spectroscopic analysis is performed to establish evidence for the assumed mechanisms, and the role of individual gates is investigated in the context of performance variance under temperature-bias stress. Moreover, the partial reversibility of the stress-induced degradation is experimentally established and methodically discussed. Overall, the reported results offer a comprehensive understanding of scaled-channel DG-NS-IGZO-TFTs, which help shape performance-enhancement strategies, control degradation mechanisms, and define appropriate application scenarios. © 2013 IEEE.

关键词： Thin film transistors

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EfficientSpeech: An On-Device Text to Speech Model

EfficientSpeech: An On-Device Text to Speech Model

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rowel Atienza Electrical and Electronics Engineering Institute and AI Graduate Program University of the Philippines

State of the art (SOTA) neural text to speech (TTS) models can generate natural-sounding synthetic voices. These models are characterized by large memory footprints and substantial number of operations due to the long-standing focus on speech quality with cloud inference in mind. Neural TTS models are generally not designed to perform standalone speech syntheses on resource-constrained and no Internet access edge devices. In this work, an efficient neural TTS called EfficientSpeech that synthesizes speech on an ARM CPU in real-time is proposed. EfficientSpeech uses a shallow non-autoregressive pyramid-structure transformer forming a U-Network. EfficientSpeech has 266k parameters and consumes 90 MFLOPS only or about 1% of the size and amount of computation in modern compact models such as Mixer-TTS. EfficientSpeech achieves an average mel generation real-time factor of 104.3 on an RPi4. Human evaluation shows only a slight degradation in audio quality as compared to FastSpeech2.

关键词： Performance evaluation Degradation Computational modeling Signal processing Transformers Real-time systems Computational efficiency

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Proposal of a Method for Inferring the Road Surface from Vibration Using Deep Learning on CanSat

Proposal of a Method for Inferring the Road Surface from Vib...

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

作者： Saito, Takuya Akiyama, Miho Tokyo University of Information Sciences Faculty of Informatics Chiba Japan Graduate School of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Japan

ISBN: (纸本)9798350320282

This paper describes a method for accurately classifying whether a CanSat is running on a hard surface or a soft, uneven surface based on acceleration data from vibration during running, using FFT and Deep Learning Image Classification. The CanSat cannot run smoothly on a hard surface, as in the case of ARLISS, and on a soft and uneven surface, as in the case of the Noshiro space event, unless the running speed and steering wheel angle settings are changed. Therefore, we considered the possibility of inferring whether the road surface is hard or soft from the vibration during running. This paper describes a method for converting acceleration data from running data into frequency-domain images by FFT analysis, and for classifying these images with high accuracy by using deep learning image classification. We found that this method can achieve a high classification accuracy of 100% for running data used for training, and more than 92% for other running data that was not used for training. © 2022 IEEE.

关键词： Image classification

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OSINT Methods in the Intelligence Cycle

OSINT Methods in the Intelligence Cycle

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International Conference on Computer Science, Electronics, and Industrial engineering, CSEI 2022

作者： Tanabe, Roberto de-Oliveira-Albuquerque, Robson da-Silva-Filho, Demétrio Alves-da-Silva, Daniel Costa-Gondim, João-Jose Professional Post-Graduate Program in Electrical Engineering Department of Electrical Engineering University of Brasília Brasília70910-900 Brazil Institute of Physics University of Brasilia DF Brasilia70910-900 Brazil DF Brasília70910-900 Brazil

ISBN: (纸本)9783031305917

The process for producing intelligence is traditionally represented by a series of steps forming a cycle. Collection is one of these stages and is characterized by the application of a set of disciplines to obtain information that will be analyzed for the production of an intelligence product. These disciplines are characterized according to the type of source, its methods and techniques. Open-Source Intelligence (OSINT) is the collection discipline focused on publicly available information. Open-source collection methods are usually represented by working diagrams that draw a flow according to the type of information. This paper makes a comparative study of the intelligence cycles of some relevant actors in the international OSINT scene, locates open-source collection in the intelligence cycle, and presents a workflow that combines this cycle with the techniques that form a method for OSINT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intelligence Methodology OSINT

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. This investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. The study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). The experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. The Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. The key findings are as follows. (1) The participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) The consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) The K-factor mean across participants tended toward K > 0. (5) The average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Enhancing the Performance of E-Mode AlGaN/GaN HEMTs With Recessed Gates Through Low-Damage Neutral Beam Etching and Post-Metallization Annealing

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 150-155页

作者： Chen, Yi-Ho Ohori, Daisuke Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan Tohoku University Institute of Fluid Science Sendai980-8577 Japan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Pioneer Semiconductor Innovation Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan

This study investigated the electrical properties of AlGaN/GaN high-electron-mobility transistors (HEMTs) with varied recess depths under the gate electrode. We demonstrated a recess depth of approximately 6 nm, which was achieved through neutral beam etching (NBE) technique with a low etch rate of 1.8 nm/min, resulting in device enhancement-mode (E-mode) behavior with threshold voltage (Vth) of 0.49 V. The effects of post-metallization annealing (PMA) on the device performance were also examined. The results revealed that PMA treatment improves the DC characteristics of the devices, including maximum drain current (IDMAX), transconductance (gm), subthreshold swing (SS), on-off ratio, and off-state leakage current, with maximum enhancement percentage of 18.3% for IDMAX, 3758% for on-off ratio, and 54.3% for SS. Moreover, this study compared the recess depths of metal-insulator-semiconductor high-electron-mobility transistors (MIS-HEMTs) with the SiN dielectric layer. The results showed that MIS-HEMTs exhibit more negative Vth values, which can be attributed to the controlled surface states achieved through passivation. © 2020 IEEE.

关键词： Etching

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TreeXGNN: can gradient-boosted decision trees help boost heterogeneous graph neural networks? 48

TreeXGNN: can gradient-boosted decision trees help boost het...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hong, Ming-Yi Chang, Shih-Yen Hsu, Hao-Wei Huang, Yi-Hsiang Wang, Chih-Yu Lin, Che Academia Sinica Research Center for Information Technology Innovation Taiwan National Taiwan University and Academia Sinica Graduate Program of Data Science Taiwan National Taiwan University Graduate Institute of Communication Engineering Taiwan National Taiwan University Department of Electrical Engineering Taiwan

ISBN: (纸本)9781728163277

Graph neural networks are a promising deep learning method that can apply graph structures to various tasks. In real-world scenarios, we often have heterogeneous graphs, wherein different node and edge types capture complex interactions between nodes. High-dimensional node features provide rich information about the target nodes. Conventional heterogeneous graph neural networks (HGNN) focus more on graph structures than node features and may have difficulties extracting knowledge from complex node features. In this study, we propose a novel framework, the tree-boosted heterogeneous graph neural network abbreviated as TreeXGNN, which could efficiently and automatically extract target node features via gradient-boosted decision trees (GBDT). It integrates community structure information with proper fusion modules and a shared feature space design on HGNN. We achieved state-of-the-art performance on the three well-known heterogeneous graph benchmark datasets, IMDB, DBLP, and ACM, and significantly improved performance compared to previous studies. Our work paves the foundation for integrating tree-based models to boost HGNNs for general community analysis. © 2023 IEEE.

关键词： Classification (of information)

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