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检索条件"机构=Inst. of Computer Engineering"

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Design and In-training Optimization of Binary Search ADC for Flexible Classifiers 25

Design and In-training Optimization of Binary Search ADC for...

引用

30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Lozano Duarte, Paula Carolina Afentaki, Florentia Zervakis, Georgios Tahoori, Mehdi Department of Computer Science Karlsruhe Inst. of Tech. Karlsruhe Germany Computer Engineering and Informatics Department Univ. of Patras Patras Greece

ISBN: (纸本)9798400706356

Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands of applications such as smart sensors and wearables, flexible devices must be small and operate at low supply voltages. Additionally, target applications often require classifiers to operate directly on analog sensory input, necessitating the use of Analog to Digital Converters (ADCs) to process the sensory data. However, ADCs present serious challenges, particularly in terms of high area and power consumption, especially when considering stringent area and energy budget. In this work, we target common classifiers in this domain such as MLPs and SVMs and present a holistic approach to mitigate the elevated overhead of analog to digital interfacing in FE. First, we propose a novel design for Binary Search ADC that reduces area overhead 2× compared with the state-of-the-art Binary design and up to 5.4× compared with Flash ADC. Next, we present an in-training ADC optimization in which we keep the bare-minimum representations required and simplifying ADCs by removing unnecessary components. Our in-training optimization further reduces on average the area in terms of transistor count of the required ADCs by 5× for less than 1% accuracy loss. © 2025 inst.tute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Budget control

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Decoding the Skin with AI: A Review of Cutting-Edge Technologies and Applications 2

Decoding the Skin with AI: A Review of Cutting-Edge Technolo...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumari, Sejal Umrao, Sandhya Kushwaha, Dileep Noida Inst. of Engg. and Technology Computer Science & Engineering Greater Noida India Greater Noida India

ISBN: (纸本)9798350371055

The skin of humans can tell stories through its textures and colors, providing insights into both health and disease. Dermatologists have been interpreting these stories for years, using their expertise to diagnose and treat conditions. They have an ally in this pursuit: artificial intelligence and machine learning (AI/ML). In this review, we will delve into the meeting point of AI/ML and dermatology, exploring the applications, challenges and the promising future. Looking ahead, we can imagine a future where emerging technologies drive advancements. In this future, AI will work hand in hand with dermatologists in procedures guided by images, speed up the process of discovering drugs, and empower individuals to keep track of their skin health using AI-powered sensors. This goes beyond upgrading technology. It signifies a revolution in how we perceive and approach skin health. It promises a future where early detection, personalized treatment and preventive measures become the standard. Join us on our journey at the intersection of skin and technology as we witness the beginning of an era in dermatology. With AI shedding light on the secrets of our skin we pave the way for a healthier tomorrow. © 2024 IEEE.

关键词： Artificial intelligence Derma-tology Diagnosis Machine learning Personalized medicine Skin cancer Treatment Wearable sensors

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Adversarial Augmentation For Adapter Learning

Adversarial Augmentation For Adapter Learning

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Chien, Jen-Tzung Sun, Wei-Yu National Yang Ming Chiao Tung University Inst. of Electrical and Computer Engineering Taiwan

ISBN: (纸本)9798350306897

The recent pre-trained models have achieved state-of-the-art results for natural language understanding (NLU) and automatic speech recognition (ASR). However, the pre-trained models likely suffer from the overfitting problem when adapting the model to a low-resource target domain. This study handles this low-resource setting by training an adversarial adapter based on a pre-trained backbone model. The adversarial training is performed by implementing the data augmentation rather than enhancing the adversarial robustness. The proposed method leverages adversarial training to collect augmented data to reinforce adapter learning with a smoothed decision boundary. The size of trainable parameters is tightly controlled to alleviate the overfitting to enhance the model capability. In the experiments, this work considerably improves the performance in NLU tasks. The adversarial adapter learning is further extended for ASR to show the merit of this method in terms of efficiency and accuracy. © 2023 IEEE.

关键词： Learning systems

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Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach 6

Enhancing Hyperspectral Image Compression Through Stacked Au...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Afrin, Afsana Haque, Md. Rakibul Mamun, Md. Al Inst. of Information and Communication Technology Rajshahi University of Engineering Technology Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

Hyperspectral Image (HSI) consisting of numerous high-resolution spectral bands creates challenges of the high dimensionality problem in HSI classification which hinder real-life applications despite their abundance of information. We present a lossy compression approach using stacked autoencoders to reduce high dimensionality problem in this paper. The pro-posed method utilizes stacked autoencoders to extract features from HSIs, allowing compression and subsequent reconstruction. The study demonstrates improved Peak Signal-to-Noise Ratio (PSNR) of 70.43, 60.87 and 61.36 on three distinct HSI datasets-Salinas, Botswana, and KSC respectively compared to previous autoencoder-based compression method. Additionally, the impact of compression on classification accuracy is assessed using a 3D-2D CNN model, achieving an average accuracy of 99 % across the datasets. Improved compression along with high classification accuracy shows our proposed method's usefulness. © 2024 IEEE.

关键词： Image enhancement

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Comparative Analysis of AAS and AML as a Data Source for Integrated Risk Assessment in ICS 29

Comparative Analysis of AAS and AML as a Data Source for Int...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Bhosale, Pushparaj Kastner, Wolfgang Sauter, Thilo Institute of Computer Engineering Tu Wien Vienna Austria Inst. of Computer Technology Tu Wien Austria Integr. Sensor Syst. Danube Univ. Krems Austria

ISBN: (纸本)9798350361230

Integrated risk assessment is important to identify, evaluate, and mitigate potential risks in Industrial Control Systems (ICS). It is necessary for safety and security within the organization. The safety and security data necessary for integrated risk assessment are obtained from various data sources that structure and manage information. In this paper, we look into information models as a potential single source of data. We compare different information models: Asset Administration Shell (AAS) and Automation Markup Language (AML). The comparison highlights the strengths and weaknesses, commonalities and differences of each data source in terms of data integration, real-time capability, standardization, design capability, automation possibility, and practical application in risk assessment processes. The findings underscore the importance of selecting appropriate data sources to enhance the accuracy, provide the possibility of updates, and enhance the efficiency of risk assessments in ICS. © 2024 IEEE.

关键词： Markup languages

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Mapping ICS Vulnerabilities: Prioritization and Risk Propagation Analysis with MITRE ATT&CK Framework and Bayesian Belief Networks 29

Mapping ICS Vulnerabilities: Prioritization and Risk Propaga...

引用

29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

ISBN: (纸本)9798350361230

The introduction of Industry 4.0 and the integration of Information Technology (IT) with Operational Technology (OT) have brought significant advancements to Industrial Control Systems (ICS). With the convergence of IT/OT, ICS not only have to counteract against.faults for safety reasons, but also have to encounter new challenges stemming from the security realm with an impact on safety issues. Ensuring the security of ICS has gained importance as any breach can cause disruption in industrial processes, leading to system downtime, production loss, and endangering human lives. Vulnerability assessment has become a crucial aspect of security research in ICS. The MITRE ATT&CK framework is one of the knowledge bases used for vulnerability management. It can be used to map identified vulnerabilities to a specific tactic provided by the framework. This mapping provides organizations with a structured approach focusing on identifying potential entry points for attackers and their progression through the system. The attacker's control of ICS could lead to a safety impact on people, processes, and the environment. This paper uses the mapping as a tool to prioritize the vulnerabilities and attacker's propagation through the network and thus help in building an effective risk propagation network using the Bayesian Belief Network (BBN). The implementation of the methodology is done using a Modular Production System (MPS) as a use case. © 2024 IEEE.

关键词： Risk assessment

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MoFLeuR: Motion-based Federated Learning Gesture Recognition 4

MoFLeuR: Motion-based Federated Learning Gesture Recognition

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Jamal Seyedmohammadi, S. Sheikholeslami, S. Mohammad Abouei, Jamshid Mohammadi, Arash Plataniotis, Konstantinos N. Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada Yazd University Dept. Of Electrical Engineering Yazd Iran

ISBN: (纸本)9798350315790

Most of the recent works on Human Gesture Recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: a) the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and b) clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated Learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency. © 2024 IEEE.

关键词： Internet of things

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Towards Extraction of Validation Rules from OPC UA Companion Specifications 28

Towards Extraction of Validation Rules from OPC UA Companion...

引用

28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Tufek, Nilay Sree Thuluva, Aparna Sai Philipp Just, Valentin Sabou, Marta Siemens AG Technology Department Munich Germany Vienna University of Technology Inst. Computer Engineering Austria Vienna University of Economics and Business Inst. Data Process Knowledge Management Austria

ISBN: (纸本)9798350339918

Interoperability is the key to industrial automation. OPC UA aims to provide interoperability between industrial machines at the network, protocol, and semantic layers. In order to achieve it, the implementation of a machine and its nodeset file should comply with the OPC UA standard. However, there are a few areas for improvement in technology to automatically check compliance since the compliance rules/validation rules are expressed in textual format in specification documents. In other words, they are not machine-interpretable. Converting the text-based specification into machine-readable form is a complex problem since each companion specification is domain-specific and written by a set of industry experts from all over the world from diverse language backgrounds. The specifications are majorly unique, with little commonality between them. Because of these reasons, it is challenging to develop a generic information extraction approach to retrieve rules that work for all specifications. In this study, we aim to handle this challenge to extract the textual rules from specifications automatically by applying Natural Language Processing and Machine Learning technologies. We present our methodology based on Named Entity Recognition for OPC UA documents for information extraction and text classification to identify rules. To achieve this goal, we created five named entity data sets from five selected OPC UA companion specifications. The sentences in the data set and the entities in the sentences were annotated by two OPC UA experts. We point out a deeper analysis of the data sets to highlight common and unique entities in them and show their usage in identifying the rules in the specifications. © 2023 IEEE.

关键词： Classification (of information)

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls

How to Construct Pseudorandom Bit Sequences from Mazes by a ...

引用

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

作者： Miyazaki, Takeru Araki, Shunsuke Uehara, Satoshi Kyushu Inst. of Information Sciences Faculity of Management and Information Sciences Fukuoka Japan Kyushu Inst. of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan The Univ. of Kitakyushu Faculty of Environmental Engineering Fukuoka Japan

ISBN: (纸本)9798350324174

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes. © 2023 IEEE.

关键词： Digging Out the Walls Maze Generator Pseudorandom Bit Sequence

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Eyes on Poverty: A Comprehensive Review of computer Vision and Textual Data Integration for Poverty Prediction in India

Eyes on Poverty: A Comprehensive Review of Computer Vision a...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Khare, Pragati Jadhav, Dipti Ramrao Adik Inst. of Tech. Dy Patil Deemed to Be University Department of Computer Engineering Maharashtra Nerul India Ramrao Adik Inst. of Tech. Dy Patil Deemed to Be University Department of Information Technology Maharashtra Nerul India

ISBN: (纸本)9798350348460

Poverty, a pervasive and multidimensional societal issue, continues challenging policymakers, researchers, and humanitarian organizations. In the quest to address this complex problem, leveraging advancements in data science and computer vision has emerged as a promising avenue. This review paper delves into the fusion of multimodal data - comprising images and text - as a transformative approach for poverty prediction, focusing on India, a nation marked by its rich diversity and unique challenges. The paper elucidates poverty's multidimensional nature, emphasizing its economic, social, and spatial dimensions. Traditional poverty assessment methods are scrutinized, revealing their inherent limitations, particularly in a dynamic and rapidly evolving nation like India. Recognizing the limitations of these conventional approaches, the paper sets the stage for exploring the novel paradigm of multimodal data analysis. The article surveys prominent datasets employed in the field, illustrating how these information repositories empower researchers to build predictive models capable of dissecting the intricate nuances of poverty. The core of this review revolves around a comprehensive evaluation of cutting-edge methodologies and techniques underpinning poverty prediction, particularly in the Indian context, revealing how they unlock insights into housing conditions, agricultural productivity, healthcare access, and socio-economic disparities. This review paper transcends the boundaries of traditional poverty assessment methodologies by venturing into the realm of multimodal data analysis. By weaving together, the intricate threads of images and text, it unravels fresh insights into poverty dynamics, particularly within the unique socio-economic landscape of India. This synthesis of multidisciplinary knowledge serves as a guiding beacon for researchers, practitioners, and policymakers alike, beckoning them to harness the transformative power of data science in the relentless

关键词： Computational Intelligence computer Vision Multimodal Data Poverty Prediction Sustainable Development Goals

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