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A Low-Cost Pipelined Architecture Based on a Hybrid Sorting Algorithm

IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 717-730页

作者： Chen, You-Rong Ho, Chien-Chia Chen, Wei-Ting Chen, Pei-Yin The Digital Integrated Circuit Design Laboratory Department of Computer Science and Information Engineering National Cheng Kung University Tainan70101 Taiwan MediaTek Inc. Hsinchu30078 Taiwan

In this paper, a low-cost pipelined architecture based on a hybrid sorting algorithm is proposed. The proposed architecture is constructed with a bitonic sorter and several cascaded bidirectional insertion sorting units. The bidirectional insertion sorting unit uses the segmented sorted subsequence generated by the bitonic sorter as input, and records the maximum and minimum values of the subsequence. After all segmented subsequences are processed through the cascaded bidirectional insertion sorting units, a sorted sequence is obtained. The proposed architecture is implemented using the Verilog hardware description language (HDL) and synthesized using the Synopsys design Compiler with a TSMC 90-nm cell library. The experimental results indicate that the proposed architecture can not only shorten sorting cycles but also reduce hardware area costs. Moreover, sorting cycles can be further shortened by increasing the parallelism of the proposed architecture. Under the configuration that 2048 32-bit data to be sorted and 16 data have to be processed simultaneously, the proposed architecture can improve the throughput-to-gate-count ratio by 16%, and throughput-to-power-consumption-ratio by 25% compared to the existing sorting design. The proposed architecture makes the most efficient use of hardware resources. © 2023 IEEE.

关键词： Costs

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Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

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Interactive Technology and Smart Education 2024年第2期22卷 304-331页

作者： Wang, Chun-Chia Chen, Hsuan-Chu Hung, Jason C. School of Information and Design Chang Jung Christian University Tainan Taiwan Department of Digital Media Design Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

Purpose: This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools. design/methodology/approach: This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process. Findings: The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments. Originality/value: Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning. © 2024, Emerald Publishing Limited.

关键词： Affective computing Convolutional neural networks digital game-based learning Eye-tracking technology Transfer learning

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Assertion-Based Trojan Localization Using Iterative Path Sensitization 26

Assertion-Based Trojan Localization Using Iterative Path Sen...

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26th International Symposium on Quality Electronic design, ISQED 2025

作者： Srinivasan, Suriya Jones, Andrew Hingson, Cameron Darrach, Gannon Vemuri, Ranga University of Cincinnati Digital Design and Environments Lab Electrical and Computer Engineering Department Cincinnati 45221-0030 OH United States

ISBN: (纸本)9798331509422

Abtract-The globalization of system-on-chip (SoC) designs in the IC supply chain has compromised security by allowing attackers to inject malicious circuitry. This introduces security violations, including critical information leakage or denial of service attacks. In this paper, we propose an automated technique to localize Trojans responsible for causing the failure of assertions that capture the design's security specifications. Our approach involves sensitizing paths for Trojan activation and assertion failure from various counterexamples, performing root cause analysis on assertion failure. These paths are expressed as temporal logic constraints, which can be negated and applied to obtain more insightful paths. We effectively localize Trojans with fewer false positives by identifying the common path activated for assertion failure. We have conducted experiments on various types of Trojans, including benchmarks from Trust-HUB, using assertions developed for test cases of different sizes and complexities to demonstrate the technique's scalability. In addition, we conducted a figure-of-merit comparative study against traditional localization methods. © 2025 IEEE.

关键词： Assertion-Based Verification Condition Activation Model Checking Path Explosion Path Sensitization Trojan Localization

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HiSkew: a novel histogram skewness classification applied to face anti-spoofing

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Neural Computing and Applications 2025年第19期37卷 13469-13496页

作者： da Silva, Vitor Luiz Giné, Francesc Valls, Magda LleidaNetworks Serveis Telemàtics Parc Agrobiotech Edifici H1 Lleida25003 Spain Department of Computer Engineering and Digital Design University of Lleida C/Jaume II 69 Lleida25001 Spain Department of Mathematics University of Lleida C/Jaume II 69 Lleida25001 Spain

Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems based on video replay. Recent works have revealed the usefulness of 3D convolutional neural networks (3D-CNNs) to classify videos between bonafide and attacks. These 3D-CNN are characterized to obtain a high accuracy at expense of a high computational cost. In this context, this work presents a new strategy for visualizing and classifying video data using 3D-CNN applied to FAS challenge. It describes an original visualization method, named feature map cube visualization, based on using histograms of the inner-layer network output (feature map). This visualization method has served as the motivation for developing a novel FAS classification method, called histogram skewness classification (HiSkew), which classifies FAS videos based on skewness histograms derived from spatiotemporal feature maps. The accuracy and computational cost of HiSkew are compared with some representative spatiotemporal convolutional models of the state of the art. This experimentation reveals that HiSkew is able to obtain an accuracy similar to the state of the art with a much lower computational cost. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Flow visualization

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Integrating Image Recognition and Board Games into Financial Education 16

Integrating Image Recognition and Board Games into Financial...

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16th Asia-Pacific Workshop on Mixed and Augmented Reality, APMAR 2024

作者： Hung, Yu-Han Chao, Hao-Bin Liou, Jie-Sin Han, Ping-Hsuan Hsieh, Yi-Zeng Fan, Ping-Lin Chou, Chien-Hsing Department of Mathematics and Information Education National Taipei University of Education Taiwan Department of Interaction Design National Taipei University of Technology Taiwan Department of Electrical and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Digital Technology Design National Taipei University of Education Taiwan

As financial products diversify and market structures become more complex, promoting financial education has become increasingly important. Given that the foundation of financial literacy should be established from childhood, how to cultivate students' correct financial management concepts from a young age has become a significant issue in modern society. Considering the substantial advantages of game-based learning in providing high interactivity and real-world scenario simulations, this paper aims to develop an innovative financial education board game that incorporates elements of risk assessment and insurance planning, and digitalizes it. With the assistance of an app and the application of image recognition technology, we could enhance the convenience and fun of the gameplay. Research results show that integrating digital apps into board games as a game-based learning strategy significantly improves the learning outcomes and motivation of upper elementary school students in the areas of risk and insurance knowledge, compared to traditional lecture methods. It also enhances their understanding and application abilities. Students hold a positive attitude toward this teaching approach, demonstrating its potential and advantages in financial education. © 2024 Copyright for this paper by its authors.

关键词： Decentralized finance

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PROS 2.0: A Plug-In for Routability Optimization and Routed Wirelength Estimation Using Deep Learning

PROS 2.0: A Plug-In for Routability Optimization and Routed ...

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作者： Chen, Jingsong Kuang, Jian Zhao, Guowei Huang, Dennis J.-H. Young, Evangeline F. Y. The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Cadence Design Systems Digital & Signoff Group San JoseCA95134 United States

Recently, the topic of how to utilize prior knowledge obtained by machine-learning (ML) techniques during the EDA flow has been widely studied. In this article, we study this topic and propose a practical plug-in named PROS for both routability optimization and routed wirelength estimation which can be applied in the state-of-the-art commercial EDA tool or an academic EDA flow with negligible runtime overhead. PROS consists of three parts: 1) an effective fully convolutional network (FCN)-based predictor that only utilizes the data from placement result to forecast global routing (GR) congestion;2) a parameter optimizer that can reasonably adjust GR cost parameters based on the prediction result to generate a better GR solution for detailed routing (DR);and 3) a convolutional neural network (CNN)-based wirelength estimator which can report accurate routed wirelength at the placement stage by using the predicted GR congestion. Experiments show that on the industrial benchmark suite in the advanced technology node, PROS can achieve high accuracy of GR congestion prediction and significantly reduce design rule checking (DRC) violations by 11.65% on average, and on the DAC-2012 benchmark suite, PROS can achieve a very low error rate (1.82%) for wirelength estimation which greatly outperforms that of FLUTE (21.52%) by 19.70%. © 1982-2012 IEEE.

关键词： Deep learning

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Assertion-Based Trojan Localization Using Iterative Path Sensitization

Assertion-Based Trojan Localization Using Iterative Path Sen...

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IEEE International Symposium on Quality Electronic design

作者： Suriya Srinivasan Andrew Jones Cameron Hingson Gannon Darrach Ranga Vemuri Electrical and Computer Engineering Department Digital Design and Environments Lab University of Cincinnati Cincinnati OH USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

关键词： Location awareness Root cause analysis Scalability Supply chains Model checking System-on-chip Trojan horses Security Logic Iterative methods

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Artificial Intelligence Based Segmentation for Skin-Layer Visualization from Optical Coherence Tomography Images

Artificial Intelligence Based Segmentation for Skin-Layer Vi...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Ramadhan, Gilang Titah Pusparani, Yori Prisilla, Ardha Ardea Shen, Wei-Cheng Cheng, Hsu-Tang Liau, Ben-Yi Jan, Yih-Kuen Chang, Wen-Thong Lin, Chih-Yang Lung, Chi-Wen Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana United States Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (纸本)9798331521165

Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92% accuracy in predicting SC and 95% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications. © 2025 IEEE.

关键词： Optical coherence tomography

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Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Prisilla, Ardha Ardea Pusparani, Yori Chang, Wen-Thong Liau, Ben-Yi Jan, Yih-Kuen Ardhianto, Peter Lin, Chih-Yang Lung, Chi-Wen Asia University Taichung Department of Digital Media Design Taiwan LaSalle College Jakarta Department of Fashion Design Jakarta Indonesia Budi Luhur University Department of Visual Communication Design Jakarta Indonesia Department of Computer Science and Information Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Asia University Department of Creative Product Design Taichung Taiwan Soegijapranata Catholic University Department of Visual Communication Design Semarang Indonesia National Central University Department of Mechanical Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Combined Split Manufacturing and Logic Obfuscation Based on Emerging Technologies at High Level for Secure 3D IC design

Combined Split Manufacturing and Logic Obfuscation Based on ...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Haimanti Chakraborty Ranga Vemuri Electrical and Computer Engineering Department Digital Design Environments Laboratory University of Cincinnati Cincinnati Ohio USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Split Manufacturing (SM) and Logic Obfuscation (LO) are potent design-for-trust solutions to mitigate hardware security threats when fabricating Integrated Circuits (ICs) at untrusted foundries. However, many such defense methods are subject to attacks such as Network Flow Attack and Boolean Satisfiability Attack, that have attempted to reconstruct the missing BEOL (Back End Of Line) signals and decrypt the design. Hence, it is crucial to select BEOL signals carefully. Although several defense strategies have been proposed to protect the design against the attacks, many have been implemented at lower abstraction levels that incur expensive re-synthesis cycles. In this paper, we combine SM and LO for enhanced security of 3D ICs by proposing a BEOL signal selection method at high level based on assigning edge-weights to prioritize Data Flow Graph edges, that also identify locations for Polymorphic Switch Boxes (PSBs) for LO. A PSB has more key-bit combinations compared to a CMOS SB for an attacker to correctly unlock it. Our method decreases attack correctness with increase in the percentage of edges lifted and has minimal impact on design performance in terms of area and wirelength.

关键词： Three-dimensional displays Circuits and systems Hardware security Switches Foundries Manufacturing Logic

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