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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Park, Ki-Hong Alouini, Mohamed-Slim Chen, Yunfei King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia Durham University Department of Engineering Durham United Kingdom

ISBN: (纸本)9798350370218

In this paper, we consider the deployment problem of integrated sensing and communication (ISAC) network in sparsely populated area where the distributions of broadband users and Internet-of-things (IoT) devices are disjoint and therefore traditional network infrastructure can be under-utilized. Network-in-a-box which is lightweight, portable, and flexible to support different type of networks is the emerging technology for either one-off network or under-served network environments. We formulate the total network throughput maximization problem to optimally deploy multiple NIBs enabling an ISAC network supporting both conventional broadband data and sensed IoT data modeled by short packet communication (SPC) framework. Under the delay and minimum throughput requirements of sensing data for IoT devices, we will propose the suboptimal iterative solution to find location of NIBs, association between NIB and user, and coding rate in SPC framework and verify the feasibility by simulation. © 2023 IEEE.

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Carbon Quantum Dot Fluorescent Stickers for Biochip Authentication 33

Carbon Quantum Dot Fluorescent Stickers for Biochip Authenti...

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33rd IEEE Asian Test Symposium, ATS 2024

作者： Baban, Navajit Singh Abdelhameed, Mohammed Elbeh, Mahmoud Ramadi, Khalil Song, Yong-Ak Bhattacharjee, Sukanta Karri, Ramesh Chakrabarty, Krishnendu New York University Division of Engg. Abu Dhabi United Arab Emirates Indian Institute of Technology Department of Computer Science and Engineering Guwahati India New York University Department of Electrical and Computer Engineering New York United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ United States

ISBN: (纸本)9798331529161

Microfluidic biochips are widely used in biomedical research, clinical diagnostics, and point-of-care testing. However, their complex supply chains make them vulnerable to counterfeiting, overbuilding, and intellectual property (IP) piracy. We present fluorescent carbon quantum dot (CQD) stickers1 that can be integrated with the polydimethylsiloxane (PDMS) based biochips for authentication. The stickers can be plasma-bonded to biochips made of glass and silicon. A protective spin-coated PDMS layer makes them obscured and tamperproof. However, they are detectable under UV light and can be authenticated via spectral analysis. The scheme exhibits unique excitation-dependent responses associated with the variability of the CQD sizes. This makes it ideal for physical authentication. Reliability studies concerning mechanical, photonic, and thermal degradation have demonstrated highly stable results. The stability of CQDs within the PDMS, their robust excitation-based emission fluorescence response, and the use of waste polypropylene masks make this a sustainable and robust authenticator for biochips. © 2024 IEEE.

关键词： Polydimethylsiloxane

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Detection and Mitigation of Subtle Feature-map Attacks in Pseudo Parallel Collaborative CNN Models for Distributed Edge Intelligence 100

Detection and Mitigation of Subtle Feature-map Attacks in Ps...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Patel, Parth Hasan, Syed Rafay Rahman, Mohammad Ashiqur Homsi, Soamar Tennessee Tech University Department of Electrical and Computer Engineering United States Florida International University Department of Computer Science United States AirForce Research Laboratory Information Warfare Division United States

ISBN: (纸本)9798331517786

Although Collaborative Deep Neural Network (CDNN) promises to be an alternative mechanism to mitigate the effects of the untrusted cloud, this approach is susceptible to other kinds of adversarial attacks, which arise from one or more untrusted devices in CDNN acting maliciously. However, since each untrusted node in the CDNN contains only partial information of the complete DNN model, it is worth investigating whether the attacker can still muster a viable threat to CDNN or not. This led to the investigation of attack scenarios and their effects on convolutional neural networks (CNN) used for image classification in the CDNN environment. In this research, we are investigating the shortcomings of existing attacks on CDNN, that lead to non-subtle attack to the defender who is on look out against such attacks. Our research showed that sparse nature of feature maps (FMs) due to the ReLU function lead to many existing attacks more obvious to the attacker. Next, we investigated how one can detect the existing attacks if the defender has some previous knowledge of the complete CNN's FMs. Our results show minimal detection overhead of about 2%, with an accuracy of 95% and F1 score of above 0.97. © 2024 IEEE.

关键词： Convolutional neural networks

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Near-sensor computing-assisted simultaneous viral antigen and antibody detection via integrated label-free biosensors with microfluidics

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InfoMat 2023年第10期5卷 80-95页

作者： Byungjoon Bae Yongmin Baek Jeongyong Yang Heesung Lee Charana S.S.Sonnadara Sangeun Jung Minseong Park Doeon Lee Sihwan Kim Gaurav Giri Sahil Shah Geonwook Yoo William APetri Kyusang Lee Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVirginiaUSA School of Electronic Engineering Soongsil UniversitySeoulRepublic of Korea Department of Electrical and Computer Engineering University of MarylandCollege ParkMarylandUSA Department of Chemical Engineering University of VirginiaCharlottesvilleVirginiaUSA Division of Infectious Diseases and International Health Department of MedicineSchool of MedicineUniversity of VirginiaCharlottesvilleVirginiaUSA Department of Material Science and Engineering University of VirginiaCharlottesvilleVirginiaUSA Department of Intelligent Semiconductors Soongsil UniversitySeoulRepublic of Korea

Precise diagnosis and immunity to viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and Middle East respiratory syndrome coronavirus(MERS-CoV)is achieved by the detection of the viral antigens and/or corresponding antibodies,***,a widely used antigen detection methods,such as polymerase chain reaction(PCR),are complex,expensive,and time-consuming Furthermore,the antibody test that detects an asymptomatic infection and immunity is usually performed separately and exhibits relatively low *** achieve a simplified,rapid,and accurate diagnosis,we have demonstrated an indium gallium zinc oxide(IGZO)-based biosensor field-effect transistor(bio-FET)that can simultaneously detect spike proteins and antibodies with a limit of detection(LOD)of 1 pg mL–1 and 200 ng mL–1,respectively using a single assay in less than 20 min by integrat-ing microfluidic channels and artificial neural networks(ANNs).The near-sensor ANN-aided classification provides high diagnosis accuracy(>93%)with significantly reduced processing time(0.62%)and energy consumption(5.64%)compared to the software-based *** believe that the development of rapid and accurate diagnosis system for the viral antigens and antibodies detec-tion will play a crucial role in preventing global viral outbreaks.

关键词： artificial intelligence biosensor edge-computing integration microfluidic

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Red VCSEL Array for Optical Parallel-Interconnected Links with Ultra-Low Energy Consumption

Red VCSEL Array for Optical Parallel-Interconnected Links wi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nawal Almaymoni Omar Alkhazragi Fabian Finkbeiner Tien Khee Ng Boon S. Ooi Photonics Laboratory Electrical and Computer Engineering Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Materials Science and Engineering Division of Physical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

We demonstrated a high data rate of 4.7 Gb/s based on a single 650-nm vertical-cavity surface-emitting laser with 2-pJ/bit energy consumption, potentially enabling Tb/s parallel interconnects based on a $14\times 16$... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical fibers Energy consumption Optical interconnections Surface waves Electro-optic effects Light emitting diodes Arrays Vertical cavity surface emitting lasers Optical arrays Optical surface waves

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Tangentially Aligned Integrated Gradients for User-Friendly Explanations 32

Tangentially Aligned Integrated Gradients for User-Friendly ...

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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn School of Electrical and Mechanical Engineering The University of Adelaide Australia School of Computer and Mathematical Sciences The University of Adelaide Australia Information Sciences Division Defence Science and Technology Group Australia

Integrated gradients is prevalent within machine learning to address the black-box problem of neural networks. The explanations given by integrated gradients depend on a choice of base-point. The choice of base-point is not a priori obvious and can lead to drastically different explanations. There is a longstanding hypothesis that data lies on a low dimensional Riemannian manifold. The quality of explanations on a manifold can be measured by the extent to which an explanation for a point lies in its tangent space. In this work, we propose that the base-point should be chosen such that it maximises the tangential alignment of the explanation. We formalise the notion of tangential alignment and provide theoretical conditions under which a base-point choice will provide explanations lying in the tangent space. We demonstrate how to approximate the optimal base-point on several well-known image classification datasets. Furthermore, we compare the optimal base-point choice with common base-points and three gradient explainability models. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging

Optimal Control of Connected Automated Vehicles with Event-T...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Sabouni, Ehsan Ahmad, H.M. Sabbir Xiao, Wei Cassandras, Christos G. Li, Wenchao Boston University Division of Systems Engineering Department of Electrical & Computer Engineering BostonMA United States Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA United States

ISBN: (纸本)9798350335446

We address the problem of safely coordinating a network of Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network. Such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved online over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may limit the controller's application to real CAVs. We tackle this limitation by adopting an event-triggered control approach for CAVs such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed developed to emulate merging roadways using mobile robots as CAVs. We present results to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety. © 2023 IEEE.

关键词： Merging

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DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying 31

DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Guanghui Liu, Dexi Nie, Jian-Yun Wan, Qizhi Hu, Rong Liu, Xiping Liu, Wanlong Liu, Jiaming School of Information Management Jiangxi University of Finance and Economics China Canada School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Statistics Division of the Physical Sciences The University of Chicago United States

ISBN: (纸本)9798891761964

Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference, such as retrieved instances and event templates. These methods face two challenges: (1) the retrieval results may be irrelevant and (2) templates are developed independently for each event without considering their possible relationship. In this work, we propose DEGAP to address these challenges through a simple yet effective components: dual prefixes, i.e. learnable prompt vectors, where the instance-oriented prefix and template-oriented prefix are trained to learn information from different event instances and templates. Additionally, we propose an event-guided adaptive gating mechanism, which can adaptively leverage possible connections between different events and thus capture relevant information from the prefix. Finally, these event-guided prefixes provide relevant information as cues to EAE model without retrieval. Extensive experiments demonstrate that our method achieves new state-of-the-art performance on four datasets (ACE05, RAMS, WIKIEVENTS, and MLEE). Further analysis shows the impact of different components. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Robust STL Control Synthesis under Maximal Disturbance Sets 63

Robust STL Control Synthesis under Maximal Disturbance Sets

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Verhagen, Joris Lindemann, Lars Tumova, Jana Kth Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm Sweden University of Southern California Thomas Lord Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350316339

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors' best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV). © 2024 IEEE.

关键词： Control system synthesis

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Unknown Input Observers Breaking Confidentiality of Controller States 63

Unknown Input Observers Breaking Confidentiality of Controll...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Breukelman, Enno Sandberg, Henrik Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

Driven by ubiquitous digitalization and cyberattacks on critical infrastructure, there is a high interest in research on the security of cyber-physical systems. If an attacker gains access to protected and sensitive information, such as the internal states of a control system, this is considered a breach of confidentiality. Access to sensitive information can be the first step in a larger cyber-attack scheme, such as a stealthy false data injection attack. Considering process and measurement noise in the plant, existing research investigated when an attacker equipped with a Kalman filter can perfectly estimate the internal controller states if the attacker has access to plant measurements and all model parameters. For this estimate to converge, the controller is required to have stable poles. In this paper, we show that if the attacker has access to the control inputs instead of the plant measurements, the controller needs to have stable zeros. Additionally, we demonstrate that an attacker equipped with an Unknown Input Observer, using tools from delayed system inversion, can get a delayed yet perfect estimate of the controller states from the control inputs without knowledge of the plant's parameters and noise characteristics. Lastly, we present simulation results from a three-tank system to showcase the differences in controller state estimation. © 2024 IEEE.

关键词： Delay control systems

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