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Electronics System-Integration Technology Conference, ESTC

作者： Serguei Stoukatch Francois Dupont Jean-Michel Redouté Department of Electrical Engineering and Computer Science Microsys lab Liege University Liege Belgium

ISBN: (数字)9798350390360

ISBN: (纸本)9798350390377

A typical process flow for micro-hotplate sensors includes the fabrication of the sensor wafer followed by functionalization. Functionalization involves the local deposition of a sensing material, or several materials in the case of a multi-gas analyzer. Functionalization makes the analyzer sensitive to specific gases. Typically, functionalization is a wafer-level process, of which screen and inkjet printing, dispensing and direct atomic layer processing are good examples. Once the sensor wafer is fully processed and functionalized, it must be singulated onto individual sensor dies. After that the individual sensor die will be assembled. Various die singulation methods have been reported, but they can pose serious problems for the functionalized sensor wafer. The sensing materials used for functionalization are dominated by metal-oxides, which are sensitive to environmental factors, including thermal exposure, humidity uptake, etc. In this paper, we reviewed various die singulation techniques and methods, in function of their suitability for singulating fully functionalized metal-oxide (MOX) micro-hotplates sensor silicon wafers, including wafer sawing, laser ablation and dicing, scribe-and-break, stealth dicing, plasma dicing, dicing before grinding and others. Finally, we identified the most suitable wafer singulation method for the selected application and presented an outlook.

关键词： Thermal factors Humidity Thermal sensors Packaging Sawing Silicon Sensor systems Plasmas Laser ablation Ink jet printing

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures 27

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Agarwal, Saurabh Kumar, Rajeev Devi, Abha Jung, Ki-Hyun Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Blockchain Technology Research Lab Department of Computer Science and Engineering Delhi Technological University New Delhi110042 India Department of Software Convergence Andong National University Gyeongbuk36729 Korea Republic of College Uttar Pradesh Sikandrabad India

ISBN: (纸本)9791188428137

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics;hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Fake detection

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Accelerating 3D Scene Development for the Metaverse: Lessons from Photogrammetry and Manual Modeling

Accelerating 3D Scene Development for the Metaverse: Lessons...

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Viviana Pentangelo Dario Di Dario Vincenzo De Martino Marco Dello Buono Stefano Lambiase Department of Computer Science Software Engineering (SeSa) Lab University of Salerno Salerno Italy

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

The metaverse, a 3D immersive digital environment, is gaining significant interest due to its ability to connect people globally engaging and immersively, thanks to recent technological advancements. Developing high-quality 3D models is crucial for achieving realism and immersivity in the metaverse. However, this process is complex and resource-intensive, demanding specialized skills and substantial time. The emergence of novel automation tools and technologies, such as photogrammetry, which uses computer vision algorithms to reconstruct 3D models from 2D images, is beginning to address such challenges. Our research focused on analyzing the current state of such technologies in automating 3D scenes for the metaverse, comparing them to traditional manual modeling techniques. We conducted an experiment in which we built the same 3D scene using two techniques: a manual approach with Blender and a photogrammetry approach exploiting the Polycam tool on a mobile device. Our results have provided insights into the main strengths and limitations of using 3D automation techniques. The photogrammetry approach has significantly sped up the entire process, producing textures and models that accurately replicate real objects. However, it cannot wholly replace manual modeling approaches, without which it is impossible to obtain complete and efficient models. Lessons learned will serve as a foundation to guide developers in developing 3D scenes for the metaverse.

关键词： Solid modeling computer vision Three-dimensional displays Automation Metaverse Computational modeling Manuals Mobile handsets Photogrammetry Image reconstruction

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LEASE: Leveraging Energy-Awareness in Serverless Edge for Latency-Sensitive IoT Services

LEASE: Leveraging Energy-Awareness in Serverless Edge for La...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Aastik Verma Anurag Satpathy Sajal. K. Das Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka Surathkal India Department of Computer Science and Engineering Cloud and Smart System Services Lab Missouri University of Science and Technology USA

Resource scheduling catering to real-time IoT services in a serverless-enabled edge network is particularly challenging owing to the workload variability, strict constraints on tolerable latency, and unpredictability in the energy sources powering the edge devices. This paper proposes a framework LEASE that dynamically schedules resources in serverless functions catering to different microservices and adhering to their deadline constraint. To assist the scheduler in making effective scheduling decisions, we introduce a priority-based approach that offloads functions from over-provisioned edge nodes to under-provisioned peer nodes, considering the expended energy in the process without compromising the completion time of microservices. For real-world implementations, we consider a testbed comprising a Raspberry Pi cluster serving as edge nodes, equipped with container orchestrator tools such as Kubernetes and powered by OpenFaaS, an open-source serverless platform. Experimental results demonstrate that compared to the benchmarking algorithm, LEASE achieves a 23.34% reduction in the overall completion time, with 97.64% of microservices meeting their deadline. LEASE also attains a 30.10% reduction in failure rates.

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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Towards Connecting Bugs and Architecture in Software Systems: A Perspective

Towards Connecting Bugs and Architecture in Software Systems...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： A. Eashaan Rao Sridhar Chimalakonda Department of Computer Science & Engineering RISHA Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9798350366259

ISBN: (纸本)9798350366266

The phenomenon of software architecture degradation over time highlights how systems tend to become increasingly error-prone and resistant to change as they evolve. Changes in architecture may arise from existing bugs and their subsequent fixes. Therefore, it is important to understand which bugs might lead to changes in the existing architecture upon resolution. In this paper, we present a framework aimed at comprehending this degeneration by delving into the origins of bugs. With a better understanding of these origins, we can pinpoint the emergence of bugs resulting from pre-existing flaws or changes within the software architecture, which can lead to architecture decay. This understanding enables us to devise targeted bug management strategies, including bug prediction, localization, and detection. Through our framework, our objective extends beyond merely enhancing bug-fixing processes towards shaping the trajectory of software architecture evolution. By proactively addressing bugs at their root causes, particularly those intertwined with the software architecture, we can aim to slow the degradation of software systems and foster the development of more resilient and adaptable architectures.

关键词： Location awareness Degradation Resistance Software architecture computer bugs computer architecture Software quality

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COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with Adaptive Spike Speculation 57

COMPASS: SRAM-Based Computing-in-Memory SNN Accelerator with...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Wang, Zongwu Liu, Fangxin Yang, Ning Huang, Shiyuan Li, Haomin Jiang, Li Shanghai Jiao Tong University Department of Computer Science and Engineering China Shanghai Qi Zhi Institute China AI Institute Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence China

ISBN: (纸本)9798350350579

Brain-inspired spiking neural networks (SNNs) are considered energy-efficient alternatives to conventional deep neural networks (DNNs). By adopting event-driven information processing, SNNs can significantly reduce the computational demands associated with DNNs, while still achieving comparable performance. However, current SNNs primarily prioritize high accuracy by constructing complex neuron models that generate sparse spikes. Unfortunately, this approach results in low energy efficiency and high latency, posing a significant challenge for deploying SNNs at the edge. Furthermore, the dominant computation in SNNs, which involves spike-wise Accumulate-Compare operations, is well-suited for Computing-in-Memory (CIM) architectures. However, exploiting high parallel processing and spike sparsity in CIM-based SNN accelerators is challenging due to the irregularity and time dependency of spikes. To address these limitations, the paper proposes COMPASS, a SRAM-based CIM architecture for efficient SNNs. We first introduce an efficient method to exploit irregular sparsity for both input spikes (explicit) and output spikes (implicit). This is achieved through a speculation mechanism that exploit dynamic spike patterns, enabling lean hardware for sparsity utilization. Additionally, the CIM architecture is carefully modified to facilitate dynamic spike pattern generation and exploitation with minimal overhead. Moreover, we design an adaptive dataflow with temporal spike representation tailored for input/output spikes, reducing memory footprint and enabling parallel execution. Comprehensive evaluation results demonstrate that COMPASS can achieve 26.7x end-to-end speedup over recent SNN accelerators hardware implementation with up to 386.7x less energy per inference. © 2024 IEEE.

关键词： Compasses (magnetic)

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Vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Akbari, Akbar Sheikh Kumar, Arvind Reddy, B Ramachandra Singh, Koushlendra Kumar Takei, Masahiro Leeds Beckett University United Kingdom National Institute of Technology Machine Vision and Intelligence Lab Jharkhand Jamshedpur India Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur India Chiba University Department of Mechanical Engineering Chiba Japan

ISBN: (纸本)9798350378214

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets. © 2024 IEEE.

关键词： Lung cancer

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New In-Air Signature Datasets

New In-Air Signature Datasets

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Alobaidi, Abdulazeez R. Dhieb, Thameur Nuimi, Zeina N. Hamdani, Tarek M. Wali, Ali Alimi, Adel M. University of Sfax REGIM-Lab.: REsearch Groups in Intelligent Machines Enis Tunisia Imam ja'Afar Al-Sadiq University Faculty of Information Technology Department of Computer Techniques Engineering Iraq Higher Institute of Business Administration University of Gafsa Tunisia College of Engineering Department of Electrical Engineering University of Misan Iraq Higher Institute of Computer Science Mahdia University of Monastir Tunisia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science South Africa

ISBN: (纸本)9798350335590

Compared to traditional biometric systems, in-air signatures are considered more robust and secure than classical pen paper. A few datasets capturing in-air signatures have been introduced, utilizing various devices such as the Leap Motion and the Microsoft Kinect sensor camera. However, these devices are not exempt from shortcomings and exhibit certain limitations. The expenses associated with their implementation and the requirement for technical proficiency in operating them present notable challenges for in-air signature analysis. Additionally, users may encounter difficulties in adapting their finger movements to fit within the device's limited field of view, particularly if they lack familiarity with these devices. To address these concerns, this paper proposes the creation of three in-air signature datasets using solely the camera of a laptop or a smartphone, eliminating the need for any additional specialized equipment. Our datasets were collected in three ways. The first is the In-Air Signature dataset (IAS dataset) and the second is the In-Air Signature dataset using a transparent Glass Plate (IASGP dataset) while the third is the In-Air Signature dataset using Smart Phone (IASSP dataset). Forty volunteers participated in the construction of these datasets. Their ages ranged from 21 to 40 years. Each volunteer signs in the air five signatures and imitates five signatures of five other volunteers. Our in-air signatures datasets are publicly available and can be used for various research tasks like in-air signature verification and identification. © 2023 IEEE.

关键词： Cameras

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SAM struggles in concealed scenes—empirical study on “Segment Anything”

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science China(Information sciences) 2023年第12期66卷 278-280页

作者： Ge-Peng JI Deng-Ping FAN Peng XU Bowen ZHOU Ming-Ming CHENG Luc VAN GOOL School of Computing Australian National University Computer Vision Lab(CVL) ETH Zurich Department of Electronic Engineering Tsinghua University College of Computer Science Nankai University

Large models open up new opportunities for artificial intelligence. In the past few months, there has been a boom in training foundation models on the vast linguistic corpus to produce amazing applications, e.g., Chat GPT, *** natural language processing and multimodal learning communities have been revolutionized. Large models' capacity for generalization and emergent makes it easy for users to believe that large models can solve anything.

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