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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Nripesh Kumar Nrip Susanta Kumar Nayak Indu Rani Yashwant Kumar Arvind Rehalia Aparna Marwah Dept of Computer Applications Bharati Vidyapeeth Institute of Management & Research New Delhi India Department of Computer Science & Engineering Centurion University of Technology and Management Paralakhemundi Odisha Dept of Management Studies Bharati Vidyapeeth Institute of Management & Research New Delhi India Dept. of Information Technology Bharati Vidyapeeth College of Engineering New Delhi India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The rapid evolution of technology in recent years has ushered in significant changes in the energy landscape. This comprehensive review explores the key components, benefits, challenges, and prospects of smart grid technology, with a specific focus on the Indian scenario. As India grapples with the challenges of rapid urbanization and increasing energy demand, smart grid technology holds immense potential to revolutionize the power sector. The paper discusses the components of smart grid technology, its benefits, and challenges faced in the Indian context, and anticipates future trends.

关键词： Renewable energy sources Technological innovation Urban areas Ecosystems Signal processing Market research Smart grids Reliability Security Sustainable development

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High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler

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Physical Review X 2023年第3期13卷 031035-031035页

作者： Leon Ding Max Hays Youngkyu Sung Bharath Kannan Junyoung An Agustin Di Paolo Amir H. Karamlou Thomas M. Hazard Kate Azar David K. Kim Bethany M. Niedzielski Alexander Melville Mollie E. Schwartz Jonilyn L. Yoder Terry P. Orlando Simon Gustavsson Jeffrey A. Grover Kyle Serniak William D. Oliver Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA

We propose and demonstrate an architecture for fluxonium-fluxonium two-qubit gates mediated by transmon couplers (FTF, for fluxonium-transmon-fluxonium). Relative to architectures that exclusively rely on a direct coupling between fluxonium qubits, FTF enables stronger couplings for gates using noncomputational states while simultaneously suppressing the static controlled-phase entangling rate (ZZ) down to kilohertz levels, all without requiring strict parameter matching. Here, we implement FTF with a flux-tunable transmon coupler and demonstrate a microwave-activated controlled-Z (CZ) gate whose operation frequency can be tuned over a 2-GHz range, adding frequency allocation freedom for FTFs in larger systems. Across this range, state-of-the-art CZ gate fidelities are observed over many bias points and reproduced across the two devices characterized in this work. After optimizing both the operation frequency and the gate duration, we achieve peak CZ fidelities in the 99.85%–99.9% range. Finally, we implement model-free reinforcement learning of the pulse parameters to boost the mean gate fidelity up to 99.922%±0.009%, averaged over roughly an hour between scheduled training runs. Beyond the microwave-activated CZ gate we present here, FTF can be applied to a variety of other fluxonium gate schemes to improve gate fidelities and passively reduce unwanted ZZ interactions.

关键词： Machine learning Quantum circuits Quantum coherence & coherence measures Quantum control Quantum engineering Quantum gates Quantum information architectures & platforms

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23 KHz linewidth 1064 nm SOA based fiber laser by using parallel type subring cavities

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Laser Physics 2024年第6期34卷 065101-065101页

作者： Liaw, Shien-Kuei Marlina, Lina Wang, Zi Chen, Shu-Miao Lee, Bo-Heng Tsai, Hsiou-Hsin Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Heterogeneously-integrated Silicon Photonic Integration Center National Taiwan University of Science and Technology Taipei10607 Taiwan Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Green Energy and Environment Research Laboratories Industrial Technology Research Institute Tainan71150 Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan

This study presents a high-quality fiber ring laser created by integrating a semiconductor optical amplifier to generate amplified spontaneous emission with a four-subring resonator and utilizing the nonlinear polarization rotator effect. When the driven current of 400 mA, the laser exhibited a maximum power deviation of 0.204 dB and a wavelength deviation of 0.012 nm during a one-hour testing period. Furthermore, utilizing a delayed self-heterodyne measurement system, we measured the linewidth of the self-made fiber laser to be 23 kHz. © 2024 Astro Ltd.

关键词： Ring lasers

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When Should you Offer an Upgrade: Online Upgrading Mechanisms for Resource Allocation

arXiv

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arXiv 2024年

作者： Jaillet, Patrick Podimata, Chara Vakhutinsky, Andrew Zhou, Zijie Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Sloan School of Management Massachusetts Institute of Technology United States Oracle Lab United States Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States

In this work, we study an upgrading scheme for online resource allocation problems. We work in a sequential setting, where at each round a request for a resource arrives and the decision-maker has to decide whether to accept it (and thus, offer the resource) or reject it. The resources are ordered in terms of their value. If the decision-maker decides to accept the request, they can offer an upgrade-for-a-fee to the next more valuable resource. This fee is dynamically decided based on the currently available resources. After the upgrade-fora-fee option is presented to the requester, they can either accept it, get upgraded, and pay the additional fee, or reject it and maintain their originally allocated resource. We take the perspective of the decision-maker and wish to design upgrading mechanisms in a way that simultaneously maximizes revenue and minimizes underutilization of resources. Both of these desiderata are encapsulated in a notion of regret that we define, and according to which we measure our algorithms’ performance. We present a fast algorithm that achieves O(log T) regret. Finally, we implemented our algorithm utilizing data akin to those observed in the hospitality industry and estimated our upgrading mechanism would increase the annual revenue by over 17%. © 2024, CC BY.

关键词： Resource allocation

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Non-Asymptotic Neural Network-based State and Disturbance Estimation for a Class of Nonlinear Systems using Modulating Functions

Non-Asymptotic Neural Network-based State and Disturbance Es...

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American Control Conference (ACC)

作者： Yasmine Marani Ibrahima N’Doye Taous Meriem Laleg Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia National Institute for Research in Digital Science and Technology Paris-Saclay France

Model disturbances result from model uncertainties or external factors acting on the system. They usually affect the closed-loop performance in a control loop system. However, they are often unknown and cannot be then compensated. Therefore, it is crucial to develop estimation methods for the effective estimation of the disturbances which can be then considered appropriately in the control design. This paper proposes a hybrid method for the joint estimation of the state and the disturbance for a class of nonlinear systems in two steps. The approach consists in a neural network with time-varying weights used to approximate the disturbance term and a modulating function method for the finite-time estimation of the state and the weights. The modulating functions approach simplifies the estimation problem into solving an algebraic systems of equations. Both offline and online frameworks are presented and discussed. An example is presented to demonstrate the performance of the proposed algorithm.

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Cross Contrasting Feature Perturbation for Domain Generalization

Cross Contrasting Feature Perturbation for Domain Generaliza...

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International Conference on computer Vision (ICCV)

作者： Chenming Li Daoan Zhang Wenjian Huang Jianguo Zhang Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples or features to diversify distributions complementary to source domains. Yet, these approaches can hardly deal with the restriction that the samples synthesized from various domains can cause semantic distortion. In this paper, we propose an online one-stage Cross Contrasting Feature Perturbation (CCFP) framework to simulate domain shift by generating perturbed features in the latent space while regularizing the model prediction against domain shift. Different from the previous fixed synthesizing strategy, we design modules with learnable feature perturbations and semantic consistency constraints. In contrast to prior work, our method does not use any generative-based models or domain labels. We conduct extensive experiments on a standard DomainBed benchmark with a strict evaluation protocol for a fair comparison. Comprehensive experiments show that our method outperforms the previous state-of-the-art, and quantitative analyses illustrate that our approach can alleviate the domain shift problem in out-of-distribution (OOD) scenarios. https://***/hackmebroo/CCFP

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A Survey of Automatic Prompt engineering: An Optimization Perspective

arXiv

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arXiv 2025年

作者： Li, Wenwu Wang, Xiangfeng Li, Wenhao Jin, Bo School of Computer Science and Technology Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China School of Computer Science and Technology East China Normal University China Key Laboratory of Mathematics and Engineering Applications MoE East China Normal University China

The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engineering faces limitations in scalability, adaptability, and cross-modal alignment, automated methods, spanning foundation model (FM) based optimization, evolutionary methods, gradient-based optimization, and reinforcement learning, offer promising solutions. Existing surveys, however, remain fragmented across modalities and methodologies. This paper presents the first comprehensive survey on automated prompt engineering through a unified optimization-theoretic lens. We formalize prompt optimization as a maximization problem over discrete, continuous, and hybrid prompt spaces, systematically organizing methods by their optimization variables (instructions, soft prompts, exemplars), task-specific objectives, and computational frameworks. By bridging theoretical formulation with practical implementations across text, vision, and multimodal domains, this survey establishes a foundational framework for both researchers and practitioners, while highlighting underexplored frontiers in constrained optimization and agent-oriented prompt design. © 2025, CC BY.

关键词： Constrained optimization

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Audio-Visual Target Speaker Extraction With Selective Auditory Attention

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 797-811页

作者： Ruijie Tao Xinyuan Qian Yidi Jiang Junjie Li Jiadong Wang Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing China Chinese University of Hong Kong Shenzhen China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China University of Bremen Bremen Germany

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person's speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuseson the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets.

关键词： Noise measurement Lips Visualization Synchronization Noise Correlation Data mining Speech processing Interference Speech recognition

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Weather Prediction on Different LSTM Techniques for Time Series Data in Chennai, India

Weather Prediction on Different LSTM Techniques for Time Ser...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Harikishan Tk S Sasikiran G Manoj Kumar Ferdin Joe John Joseph Mithileysh Sathiyanarayanan Student of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Thai-Nichi International College Thai-Nichi Institute of Technology Bangkok Research & Innovation MIT Square London

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The prediction of weather has always been an integral factor in our lives as it determines if the day of activity leads to favorable or unfavorable conditions. The existence of different deep learning models has facilitated several major problems which without such techniques would have been a very time-consuming and exhaustive process. In this following paper, we explore various types of LSTM models and analyze which among them provides the best fit for the Time Series data that is present in the Weather data. Upon gathering the datasets, a strategy called Long Short-Term Memory (LSTM) is carried out. LSTM is a deep learning model which is primarily focused on time-series data. By employing such technical improvisations on the same data with multiple models each with their own unique characteristics, we can ultimately determine the best model which has an overall better accuracy. Based on the metrics produced by employing each technique, it is found that our LSTM model which proposed here has the best performance in terms of predicting the data with an overall low error rate and loss during the training phase.

关键词： Deep learning Training Measurement Error analysis Time series analysis Weather forecasting Predictive models

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Real-Time Sidewalk Crack Identification and Classification based on Convolutional Neural Network using Thermal Images 1

Real-Time Sidewalk Crack Identification and Classification b...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Karthikeyan, G. Dhaarani, T.G. Anusuya, R. Kalyan Kumar, G. Joel, T. Prabu, R. Thandaiah Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India Department of Computer and Communication Engineering Rajalakshmi Instittue of Technology Chennai India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781665460842

The most common causes of pavement degradation include incorrect road construction and maintenance, overburden, inadequate road surface drainage, seepage, especially difficult environmental conditions like frost. Highway distresses raise fuel expenses, raise travel times, and have a negative impact on road safety. They also slow down traffic flow. The implementation of a lightweight classification algorithm for the identification of complex paving circumstances using RGB-thermal input and also an attention component embedded to modify the spatial as well as characteristics of the data of the images is recommended using a convolutional neural network based on a revised residual framework. With an attention mechanism as well as the RGB-thermal as input, the suggested model's better predictive accuracy is 98.88 percent. The proposed technique improves the picture's level of clarity and controls how picture streams are used, which improves the model's actual quality. Additionally, it is contrasted to cutting-edge deep learning techniques, showing that our approach outperforms other picture categorization models in terms of character recognition, training time, and parameter count. In order to compare the information, the system learns from of the photos under various input information, a visualization approach combining GWM is provided. © 2022 IEEE

关键词： Convolutional neural networks

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