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检索条件"任意字段=Conference on Digital Signal Processing Technology"

共 23660 条记录，以下是51-60 订阅

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Design and Implementation of Delta Modulaton Transceiver in MATLAB, SIMULINK and DSP Processor 3

Design and Implementation of Delta Modulaton Transceiver in ...

引用

3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Anaghaa, R. Unnathi, G.D. Niharika, J. Bindu, S. Visvesvaraya Technological University Bnm Institute of Technology Karnataka Belgaum India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

With an emphasis on its ease of use and effectiveness in analog-to-digital conversion, this study investigates the design and implementation of a Delta Modulation (DM) system. Unlike traditional Pulse Code Modulation (PCM), DM encodes the difference between successive samples, reducing hardware complexity and bandwidth usage. The system was developed and tested through MATLAB simulations and real-time processing on a TMS320C6713 DSP board. Results highlight the capability of DM to effectively reconstruct analog signals, though minor errors were observed due to quantization noise. This study shows how DM can be used in real-time and low-bandwidth signal processing, where compact and effective design is crucial. © 2025 IEEE.

关键词： Delta modulation

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language processing

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Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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Quantum Neural Networks: A Path to Lower Emissions Through Fuel Consumption Prediction in Shipping

Quantum Neural Networks: A Path to Lower Emissions Through F...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Chien, S.F. Hermans, Julien J.M. Kana, Austin A. Zarakovitis, Charilaos C. Zavvos, Stathis Lim, H.S. Strategic ICT MIMOS Berhad Kuala Lumpur Malaysia Delft University of Technology Dept. of Maritime and Transport Technology Delft Netherlands Axon Logic Dept. of Applied Mathematics 7 Dekelias Athens14122 Greece VLTN BV De Keyserlei 58-60 Bus 19 Antwerp2018 Belgium Faculty of Engineering and Technology Multimedia University Melaka Malaysia

ISBN: (纸本)9798350368741

This paper proposes Quantum Neural Networks (QNNs) as a data-driven approach for predicting fuel consumption. We utilize various layer architecture designs available in the Torchquantum framework, including both entangled and non-entangled circuit designs. In general, QNNs can achieve comparable Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with significantly fewer trainable parameters. Neither pure QNNs nor hybrid QNN models exhibit the underfitting tendencies seen in classical neural networks (CNNs). Notably, one of the most significant findings of this work is that hybridizing or"dressing" the quantum circuit leads to substantial improvements in RMSE and MAPE for pure QNNs. These promising results suggest potential optimizations for reducing emissions in green shipping. © 2025 IEEE.

关键词： Classical neural network digital twin fuel consumption modeling NISQ devices parameterized quantum circuit quantum neural network

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Development of a Method for Assessing Indicators of Discrete Control Systems

Development of a Method for Assessing Indicators of Discrete...

引用

2025 Systems of signals Generating and processing in the Field of on Board Communications, SOSG 2025

作者： Artemyev, Viktor S. Mokrova, Nataliya V. Plekhanov Russian University of Economics Moscow Russia University of Science and Technology Misis Moscow Russia

ISBN: (纸本)9798331532635

In modern industry and transport systems, control to ensure the accuracy of technology processes is of crucial significance. The article provides examples of using the theory of dynamic systems to ensure the accuracy of control systems for elements of transport systems, allowing for monitoring, optimization and intensification of processes based on automated systems for analyzing control system indicators. Control systems encounter such disturbing factors as dynamic loads, equipment wear and tear and external factors leading to variations from specified parameters. The paper presents studies of dynamic characteristics to determine potential opportunities for adjusting process parameters, which in turn help in identifying the causes of deviations and determining optimal process parameters. The proposed methods for assessing the quality indicators of control systems are proposed for use in digital signal processing, control systems and other applications related to the analysis of dynamic systems. © 2025 IEEE.

关键词： Control system analysis

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Implanting Robust Watermarks in Latent Diffusion Models for Video Generation

Implanting Robust Watermarks in Latent Diffusion Models for ...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Liu, Xiaohang Chang, Heng Wei, Jinfu Zhu, Lei Liu, Li Li, Likun Zhou, Shiji Li, Chengyuan Xu, Di Gao, Wei School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China Huawei Technologies Co. Ltd. China Tsinghua University China

ISBN: (纸本)9798350368741

In the dynamic realm of digital media, latent diffusion models (LDM) have revolutionized the generation of videos, surpassing the capabilities of traditional generative models. This paper presents Stable Video Signature, a pioneering watermarking framework for LDM in video generation. Addressing the pressing need for copyright and model protection, our approach is the first to implant watermarks directly into the generation process of LDM based on video through a novel two-stage process. We first encode watermarks into the video's latent space embedding, ensuring a holistic temporal decoding mechanism of LDM. Then watermark is integrated into the LDM's decoder. In this process, our method can maintain frame consistency, preserving the quality and robustness of generated videos during watermark implantation. We further show that the framework embeds watermarks seamlessly into LDM, maintaining the original functionality of the models and exhibiting resilience against a spectrum of watermark attacks. Our comprehensive experiments on both text-to-video and image-to-video generation tasks substantiate the efficacy of Stable Video Signature. This work not only pioneers watermarking in video generation LDM, but also sets a precedent for safeguarding intellectual property in the age of advanced media synthesis. © 2025 IEEE.

关键词： diffusion model digital watermark video generation

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L2 · M = C2 Large Language Models Are Covert Channels

L2 · M = C2 Large Language Models Are Covert Channels

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Gaure, Simen Koffas, Stefanos Picek, Stjepan Rønjom, Sondre Norwegian National Security Authority Norway Cybersecurity Group Delft University of Technology Netherlands Digital Security Group Radboud University Netherlands University of Bergen Norway

ISBN: (纸本)9798350368741

Large Language Models (LLMs) are susceptible to various attacks but can also improve the security of diverse systems. However, how well do open source LLMs behave as covertext distributions to, e.g., facilitate censorship-resistant communication? In this paper, we explore open-source LLM-based covert channels. We empirically measure the security vs. capacity of two open-source LLM models (Llama-7B and GPT-2) to assess their performance as covert channels. Although our results indicate that such channels are not likely to achieve high practical bitrates, we also show that the chance for an adversary to detect covert communication is low. To ensure our results can be used with the least effort as a general reference, we employ a conceptually simple and concise scheme and only assume public models. © 2025 IEEE.

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Forensics Analysis of Residual Noise Texture in digital Images for Detection of Deepfake

Forensics Analysis of Residual Noise Texture in digital Imag...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Méreur, Arthur Mallet, Antoine Cogranne, Rémi Kuribayashi, Minoru Troyes University of Technology France Center for Data-driven Science and Artificial Intelligence Tohoku University Japan

ISBN: (纸本)9798350368741

This paper proposes an original approach for the automatic detection of AI-generated images, using features derived from noise residuals artefacts. Contrary to most current research that leverages sophisticated deep learning models to further improve performance, this study highlights the distinct noise residual characteristics in deepfakes, facilitating the identification of AI-generative images. Our findings highlight some limitations of image models, which can be used for forensic analysis and for future AI-based text-to-image generative models. Broad numerical results on a large and diverse dataset show the interest of the identified features as well as the relevance of the present method. © 2025 IEEE.

关键词： DeepFakes Explainable method Machine learning Noise residual Statistical detection

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From ChatGPT to SORA-AIGC Empowering the digital Transformation in Higher Education 24

From ChatGPT to SORA-AIGC Empowering the Digital Transformat...

引用

7th International conference on Educational technology Management, ICETM 2024

作者： Liu, Shuguang Wu, Chengwei Zhou, Yunyan Wang, Hongsheng Sun, Hao School of Mechanical and Electrical Eng. Huaibei Institute of Technology Anhui China School of Mechanical and Electrical Eng. Huangshan University Anhui China

ISBN: (纸本)9798400717468

The emergence of ChatGPT and Sora is two sensational events in the history of artificial intelligence development, jointly demonstrating the powerful functions of generative and universal artificial intelligence based on large models. As a technology system that integrates multiple functions such as text, image, and video processing, AIGC (Artificial Intelligence Generated Content) not only brings technological breakthroughs to the education field due to its emergence of multiple platforms, terminals, and modalities, but also brings challenges and opportunities to the current teaching and research system. The endogenous value of AIGC is reflected in its nonlinearity, multiscale, self-organization, adaptability, difficulty in regulation, signal transmission, feedback loop, and historical dependence, which is of great benefit to the digital transformation of higher education today. Empowering teaching, AIGC has promoted the shift of teaching mode from the binary structure of"teacher - student" to the ternary structure of"teacher - machine - student", promoted the shift of teaching content from manual production to intelligent production, and catalyzed the evaluation mode of"knowledge+literacy";Empowering learning, AIGC has promoted the ubiquity of learning space, met the personalized needs of full coverage of the learning process, and formed a human-machine collaborative learning mode;Empowering education, AIGC has promoted the shift of educational philosophy towards high-level ability cultivation and comprehensive literacy cultivation, and innovated the education model of interdisciplinary integration. © 2024 Copyright held by the owner/author(s).

关键词： Students

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OFDM signal Modulation Classification using Dilated CNN Model 17

OFDM Signal Modulation Classification using Dilated CNN Mode...

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17th International conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Shah, Riddhi Parmar, Ashok Chouhan, Ankit Captain, Kamal Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (纸本)9798331531195

Modern wireless communication systems, including Wi-Fi, LTE, and 5G, use the highly efficient digital transmission technique known as orthogonal frequency division multiplexing (OFDM). Automatic modulation classification (AMC) is a critical signal-processing technique employed in the physical layer of wireless communication systems. Its primary objective is to identify an incoming signal's modulation type without prior knowledge at the receiver end. This letter outlines our proposed deep learning model for the modulation classification of OFDM using dilated convolution and modified residual network block which uses skip connections for better performance. This model is referred to as a modified residual dilated convolutional neural network (MRDCNN). The dataset containing 6 different modulation techniques which contain different modulation techniques for header and payload of OFDM signals with the signal-to-Noise Ratio (SNR) range of -10 dB to 20 dB is used here. The implementation results show that the model gives an accuracy of 93.01% for overall SNRs ranging from -10 dB to 20 dB. It can achieve 98% accuracy for 0 dB SNR and can achieve 100% accuracy for SNRs higher than 4 dB. In addition, the proposed model outperforms the state-of-the-art models in modulation classification in OFDM signals. © 2025 IEEE.

关键词： signal to noise ratio

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