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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Ichimura, Yuichiro Goto, Kenta Johguchi, Koh Graduate School of Science and Technology Shinshu University Electrical and Computer Engineering Division Department of Engineering Nagano Japan Shinshu University Faculty of Engineering Department of Electrical and Computer Engineering Nagano Japan

ISBN: (纸本)9798350315042

In this work, a prototype system has been designed with a 0.18-μm CMOS technology to capture perspiration rate in daily life. To calculate an amount of perspiration, a temperature sensor is necessary concurrently with a relative humidity sensing. The designed temperature sensor consists of 8-bit binary digital-to-analog converter and an analog front-end circuit for thermistors. The designed circuits for temperature and humidity sensors have successfully verified. © 2023 IEEE.

关键词： Humidity sensors

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Design and Analysis of SiN Optical Waveguide for 2D Beam Steering of small angle

Design and Analysis of SiN Optical Waveguide for 2D Beam Ste...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Zhou, Zhiwei Shimeno, Yoshikazu Kawai, Shotaro Maruyama, Takeo Lu, Sicheng Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan

ISBN: (纸本)9781665462136

The purpose of this research is to address the issue of optical deflection angle in small magnetic devices, which can limit their effectiveness in various applications. To overcome this challenge, we propose using grating couplers to control the direction of light propagation. Specifically, we have developed a system that consists of 16 phase array grating couplers and 8 multimode interferometers (MMIs) that work together to achieve 2D beam steering. The researchers found that changing the wavelength by 50nm was enough to achieve 6.06° of 1D beam steering. By carefully manipulating the phase of the grating couplers, we were able to deflect the light from 0° to 1.75° of 2D beam steering, which could greatly enhance the performance of small magnetic devices. © 2023 IEEE.

关键词： Silicon nitride

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Predicate encryption with selective-opening security for receivers: formal definition, generic construction, and concrete instantiations for several primitives

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Designs, Codes, and Cryptography 2024年第6期92卷 1477-1500页

作者： Tseng, Yi-Fan Liu, Zi-Yuan Tso, Raylin Department of Computer Science National Chengchi University Zhinan Rd. Taipei116011 Taiwan Division of Electrical Engineering and Computer Science Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

With the rise of cloud computing, multi-user scenarios have become a common setting for data sharing nowadays. The conservative security notion might not be sufficient for such a data sharing model. As a response to this challenge, there has been significant research targeting security against receiver selective-opening (RSO) attacks. However, we found that none of these studies discuss RSO security specifically for predicate encryption (PE)—an encryption mechanism naturally designed for multi-user data sharing. This manuscript first formalizes the RSO security for PE. We then present a generic PE construction that achieves RSO security based on the simulation-based definition. Our work also features several instantiations for various predicate families, including attribute-based encryption for the monotone span program, which is known as one of the most expressive PE. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Access control

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Reflect-and-Amplify: a Novel SWIPT Technique through Hybrid Active/Passive RIS

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IEEE Internet of Things Journal 2025年第12期12卷 22073-22089页

作者： Ata, Yalcin Zedini, Emna Vegni, Anna Maria Alouini, Mohamed-Slim Ostim Technical University Ostim Department of Electrical and Electronics Engineering Yenimahalle Ankara06374 Turkey University of Michigan-Flint College of Innovation and Technology FlintMI United States Roma Tre University Department of Industrial Electronics and Mechanical Engineering Rome Italy Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

This paper presents a novel technique for the simultaneous wireless information and power transfer (SWIPT) toward low-power devices, by means of Reconfigurable Intelligent Surfaces (RISs). Our approach is based on a Reflect-and-Amplify protocol meaning that, based on the RIS beam-steering functionality and assuming different reflection coefficients for the RIS elements, it allows to reflect and amplify the impinging wireless signal from the transmitter toward the receiver, which will be able to both collect energy and download data. Specifically, by considering a hybrid RIS comprised of both passive and active elements, the proposed SWIPT approach is defined Active/Passive Switching (APS). The reflection of the wireless beam toward the receiver is accomplished by means of passive RIS elements, while an amplification of the wireless signal is provided through active RIS elements. Such amplification allows the receiver to convert the incoming wave power into a DC voltage, but also causes an increase of power consumption due to active elements. To assess the performance of the proposed SWIPT approach, we derive the closed-form expression of the joint data transmission and energy harvesting outage probability (OP) in case of both a single and multiple users. We compare the proposed technique to other SWIPT approaches from the literature, such as the power splitting and the time switching, under power fairness conditions. Our results validate the benefits of using hybrid RISs for SWIPT systems, as compared to other existing approaches showing reduced performance, also in case of asymptotic analysis for high power and high number of RIS elements. © 2014 IEEE.

关键词： Inductive power transmission

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Demonstration of synaptic and resistive switching characteristics in W/TiO_(2)/HfO_(2)/TaN memristor crossbar array for bioinspired neuromorphic computing

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Journal of Materials Science & Technology 2022年第1期96卷 94-102页

作者： Muhammad Ismail Umesh Chand Chandreswar Mahata Jamel Nebhen Sungjun Kim Division of Electronics and Electrical Engineering Dongguk UniversitySeoul 04620South Korea Department of Electrical and Computer Engineering National University SingaporeSingapore 117583Singapore College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia

In this study,resistive random-access memory(RRAM)-based crossbar arrays with a memristor W/TiO_(2)/HfO_(2)/TaN structure were fabricated through atomic layer deposition(ALD)to investigate synaptic plasticity and resistive switching(RS)characteristics for bioinspired neuromorphic computing.X-ray photoelectron spectroscopy(XPS)was employed to explore oxygen vacancy concentrations in bilayer TiO_(2)/HfO_(2)*** fitting for O1s peaks confirmed that the HfO_(2)layer contained a larger number of oxygen vacancies than the TiO_(2)*** addition,HfO_(2)had lower Gibbs free energy(ΔG°=-1010.8 kJ/mol)than the TiO_(2)layer(ΔG°=-924.0 kJ/mol),resulting in more oxygen vacancies in the HfO_(2)*** results andΔG°magnitudes confirmed that formation/disruption of oxygen-based conductive filaments took place in the TiO_(2)*** W/TiO_(2)/HfO_(2)/TaN memristive device exhibited excellent and repeatable RS characteristics,including superb 10^(3) dc switching cycles,outstanding 107 pulse endurance,and high-thermal stability(10^(4) s at 125℃)important for digital computing ***,some essential biological synaptic characteristics such as potentiation-depression plasticity,paired-pulse facilitation(PPF),and spike-timing-dependent plasticity(STDP,asymmetric Hebbian and asymmetric anti-Hebbian)were successfully mimicked herein using the crossbar-array memristive *** on experimental results,a migration and diffusion of oxygen vacancy based physical model is proposed to describe the synaptic plasticity and RS *** study demonstrates that the proposed W/TiO_(2)/HfO_(2)/TaN memristor crossbar-array has a significant potential for applications in non-volatile memory(NVM)and bioinspired neuromorphic systems.

关键词： Resistive switching Crossbar-array memristive device Synaptic plasticity TiO_(2)/HfO_(2)film Oxygen vacancy

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Compact deep-groove-assisted bends on SiN platform for monolithic integrated laser

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Optics Express 2025年第3期33卷 4383-4395页

作者： Zhang, Ziyao Skandalos, Ilias Ma, Rui Chen, Minjia Sun, Bohao Zhong, Chuan Gardes, Frederic Cheng, Qixiang Penty, Richard Centre for Photonic Systems Electrical Engineering Division Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

To build a photonic integration platform that incorporates monolithically integrated light sources which require minimized loss and reflection, a micro-meter scale passive layer is needed in addition to the III-V layer. Low temperature plasma-enhanced chemical vapor deposition (PECVD) silicon nitride (SiN) is a promising candidate, owing to its back-end-of-line (BEOL) integration capabilities, along with its amorphous structure that allows the growth of defect-free thick layers. In view of the III-V layer stack, the optimized SiN waveguides are thick and in a rib formation. However, bends on this thick SiN platform suffer from high radiation losses, resulting in the need for a bending radius as large as 800 µm that poses challenges for compact photonic integrated circuits (PICs). This paper describes and demonstrates a novel SiN bending structure with a deep etched groove along the outer side. This structure significantly reduces the bending radii to 37 µm with a bending loss of 0.1 dB/90°. A compact micro-ring resonator (MRR) with deep etched grooves is also investigated, exhibiting a substantial enhancement in the free spectral range (FSR) compared with the rib waveguide MRRs, while offering a passband of 39.1 GHz. These notable performance improvements pave the way for compact integrated emitters on a single substrate. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Silicon nitride

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A Subspace Projective Clustering Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3497-3509页

作者： Wang, Yue Li, Wenqing Sarkar, Esha Shafique, Muhammad Maniatakos, Michail Jabari, Saif Eddin New York University Abu Dhabi Division of Engineering Abu Dhabi129188 United Arab Emirates New York University Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States New York University Department of Civil and Urban Engineering Tandon School of Engineering BrooklynNY11201 United States

Backdoor attacks in deep neural networks (DNNs) involve an attacker inserting a backdoor into the network by manipulating the training dataset, which causes misclassification of inputs that contain a specific trigger. Detecting and mitigating such attacks are challenging, as only the attacker knows the trigger and target class. Our study demonstrates that the representations, i.e., the neuron activations for a given DNN, of poisoned and genuine data lie in different subspaces, which implies that there exists a certain subspace where the difference of projections from different data can be manifested. To this end, we propose a method based on subspace projective clustering (SPC), which learns a subspace as well as a projection-based weight vector by solving a projection maximization program, and the optimized weight vector can be utilized in a clustering framework to infer the group of data. Based on our theoretical analysis and experimental results, we demonstrate the effectiveness of our method in defending against backdoor attacks that use different settings of poisoned samples on GTSRB, Imagenet, VGGFace2, and PubFig datasets in comparison with the state-of-the-art methods. Our algorithm can detect more than 90% of the infected classes and identify 95% of the poisoned samples. © 2020 IEEE.

关键词： Deep neural networks

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Effect of Signal Conditioning and Evoked-Potential Based Representation on Stability and Distinctiveness of EEG Brain Signatures 12

Effect of Signal Conditioning and Evoked-Potential Based Rep...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Oztemel, Muhammed Esad Soysal, Omer Muhammet Louisiana State University Division of Electrical and Computer Engineering United States Southeastern Louisiana University Computer Science United States

ISBN: (纸本)9798350330366

Biometric patterns have been used for authentication purposes for more than decades. Although traditional biometric measurements including fingerprints, facial recognition, and sound recognition are often used for authentication purposes, there is ongoing concern about the risks associated with their repeatability and susceptibility to theft. The utilization of electroencephalography (EEG) has promising potential thanks to the distinctive patterns extracted from brain signals. The main goal of this study is to explore the effectiveness of signal conditioning and evoked potentials on uniqueness and permanence of individuals' EEG signals for authentication purpose. We utilized the Laplacian of Gaussian as a signal conditioning operator. In addition, three signifying functions were explored in obtaining evoked potential time-domain representation from an individual EEG signal. Different stimuli protocols were employed for comparison. A time-series and a statistical technique were utilized for uniqueness and permanence analysis. © 2024 IEEE.

关键词： Authentication

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Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games 63

Conjectural Online Learning with First-order Beliefs in Asym...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Li, Tao Hammar, Kim Stadler, Rolf Zhu, Quanyan New York University Department of Electrical and Computer Engineering United States Kth Royal Institute of Technology Division of Network and Systems Engineering Sweden

ISBN: (纸本)9798350316339

Asymmetric information stochastic games (AISGs) arise in many complex socio-technical systems, such as cyberphysical systems and IT infrastructures. Existing computational methods for AISGs are primarily offline and can not adapt to equilibrium deviations. Further, current methods are limited to particular information structures to avoid belief hierarchies. Considering these limitations, we propose conjectural online learning (COL), an online learning method under generic information structures in AISGs. COL uses a forecaster-actorcritic (FAC) architecture, where subjective forecasts are used to conjecture the opponents' strategies within a lookahead horizon, and Bayesian learning is used to calibrate the conjectures. To adapt strategies to nonstationary environments based on information feedback, COL uses online rollout with cost function approximation (actor-critic). We prove that the conjectures produced by COL are asymptotically consistent with the information feedback in the sense of a relaxed Bayesian consistency. We also prove that the empirical strategy profile induced by COL converges to the Berk-Nash equilibrium, a solution concept characterizing rationality under subjectivity. Experimental results from an intrusion response use case demonstrate COL's faster convergence over state-of-the-art reinforcement learning methods against nonstationary attacks. © 2024 IEEE.

关键词： Cost functions

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Physics-Constrained Adversarial Training for Neural Networks in Stochastic Power Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1121-1131页

作者： Li, Wenting Deka, Deepjyoti Wang, Ren Paternina, Mario R. Arrieta LANL Applied Mathematics and Plasmas Physics Division Los AlamosNM87544 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States National Autonomous University of Mexico Department of Electrical Engineering Mexico City04510 Mexico

The growth in distributed energy resources (DERs) is an important step toward solving the global climate crisis. However, many DERs, such as wind and solar power, are random and intermittent, causing the data in power grids to be perturbed with high uncertainty. Such perturbations degrade the performance of data-driven algorithms introduced in power grids for sensing and control. Existing approaches can strengthen machine learning models against well-designed malicious attacks. However, such physics-agnostic efforts fail to ensure the robustness of natural perturbations in power grids, that occur due to varying loads and changes in control inputs. This article proposes a novel physics-constrained adversarial training method for robustifying neural networks for the crucial problem of locating edge-faults in power grids. Our approach relies on first deriving analytical physical laws that are satisfied by state perturbations in realistic grids and then using the descriptive sets during adversarial training. Compared to state-of-the-art methods, our perturbation-robust neural networks have higher robust accuracy as well as training efficiency. Also, we demonstrate that the proposed approach achieves a tighter upper bound of robust risks than traditional efforts. The numerical experimental results in the IEEE 68-bus benchmark system validate our adversarial training in two scenarios when loads and control inputs vary randomly. The proposed method shows superior performance in accuracy and efficiency for different perturbed datasets. In addition, we testify the influence of different inputs on the robustness. © 2023 IEEE.

关键词： Neural networks

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