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Robust Wake Word Spotting With Frame-Level Cross-Modal Attention Based Audio-Visual Conformer

Robust Wake Word Spotting With Frame-Level Cross-Modal Atten...

引用

international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoxu Wang Ming Cheng Qiang Fu Ming Li Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China

In recent years, neural network-based Wake Word Spotting achieves good performance on clean audio samples but struggles in noisy environments. Audio-Visual Wake Word Spotting (AVWWS) receives lots of attention because visual lip movement information is not affected by complex acoustic scenes. Previous works usually use simple addition or concatenation for multi-modal fusion. The inter-modal correlation remains relatively under-explored. In this paper, we propose a novel module called Frame-Level Cross-Modal Attention (FLCMA) to improve the performance of AVWWS systems. This module can help model multi-modal information at the frame-level through synchronous lip movements and speech signals. We train the end-to-end FLCMA based Audio-Visual Conformer and further improve the performance by fine-tuning pre-trained uni-modal models for the AVWWS task. The proposed system achieves a new state-of-the-art result (4.57% WWS score) on the far-field MISP dataset.

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Simulation and Performance Evaluation of RIS Aided Beyond 5G systems 10

Simulation and Performance Evaluation of RIS Aided Beyond 5G...

引用

10th international conference on signal processing and Communication, ICSC 2025

作者： Iftekhar, Mariya Sharique, Mohd Zhcet Aligarh Muslim University Department of Electronics Engineering Aligarh India

ISBN: (纸本)9798331511890

In this work, we have considered the Re-configurable intelligent surface (RIS) aided beyong 5G communication systems. The system considered the two scenarios. First, we have taken the scenario where a RIS aided path as well as a direct path are present between the transmitter and the user equipment. In the second scenario, only the RIS aided path is considered as an obstruction present between the transmitter and receiver. The simulation is validated with the theoretical results of the Rayleigh channel model. The BER performance is thoroughly investigated. It has been observed from the results that more reflecting elements on RIS plane improves error performance. The Bit-Error Rate (BER) performances of Binary-Phase-Shift-Keying (B-PSK) and 4-QAM schemes are also evaluated for a comparative study. The free space path loss values are calculated by varying transmitter-receiver separation, and for different carrier-frequencies using urban 5G large scale model. © 2025 ieee.

关键词： Quadrature amplitude modulation

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Extreme-learning-machine-based architecture to assess the reliability of the integrated energy systems 8

Extreme-learning-machine-based architecture to assess the re...

引用

8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Huo, Xianxu Wang, Qingbiao Zhao, Chenyang Ding, Yi Zhang, Bingchen Electric Power Research Institute of State Grid Tianjin Electric Power Company Tianjin300384 China State Grid Tianjin Electric Power Company Baodi Power Supply Branch Tianjin301800 China State Grid Tianjin Electric Power Company Tianjin300010 China Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin300072 China

ISBN: (纸本)9798350302455

The global energy crisis has contributed to the development of integrated energy systems (IES). IES enables the coupling, conversion, and utilisation of multiple energy, such as electricity, heat and cooling. As renewable energy penetration increases, the precariousness of renewable energy in IES causes risks to the reliability of energy supply. Traditional optimal energy flow (OEF) models become complex and computationally intensive. To solve this problem, a machine-learning-based method is proposed instead of the traditional OEF model. The method obtains the model optimal coding parameters by extreme learning machine (ELM) model, establishes the nonlinear mapping relationship between the system operation state and the optimal load reduction, and realizes the high accuracy and fast solution of the reliability indices. In this article, simulation experiments are conducted on the integrated community energy system (ICES) to test the advantage and practicality of the approach in this paper. Compared with the model-driven approach, the method in this paper greatly improves the efficiency of reliability assessment and has an accuracy rate of over 95%, which is better than random forest, back propagation neural network and autoencoder. © 2023 ieee.

关键词： Deep learning

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Hardware Implementation of an efficient FIR filter for ECG signal Denoising Application 29

Hardware Implementation of an efficient FIR filter for ECG S...

引用

29th ieee international conference on Electronics, Circuits and systems (ieee ICECS)

作者： Ajaydas, U. R. Reddy, B. Naresh Kumar James, Alex Digital Univ Kerala Sch Elect Syst & Automat Veiloor India

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

In this paper, an FIR filter using Vedic Multiplier and Carry Look Ahead adder (VMCLA) is proposed for Electrocardiogram (ECG) denoising. Vedic Multiplier based on Urdhva Tiryaghbhyam technique is designed because of its faster and better computational ability. Further, the delay in addition of partial products is also reduced by using Carry Look Ahead (CLA) Adder. ECG signal processing and validation of performance parameters are done in MATLAB. The proposed FIR filter design algorithm is synthesized and simulated using Vivado design suite 2021.2 and implemented on Kintex-7 FPGA board. The results revealed a high performance and area efficient compared to a well-known prior art filters.

关键词： Electrocardiogram (ECG) Vedic Multiplier (VM) Urdhva Tiryagbhyam Sutra Carry Look Ahead (CLA) Adder Performance

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An Analysis of the Brake Performance of Radar-based Adaptive Cruise Control During Ramp Merging on Simulation Software 7

An Analysis of the Brake Performance of Radar-based Adaptive...

引用

7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Zhao, Zhanle Wei, Chuheng University of Warwick Warwick Manufacturing Group Coventry United Kingdom

ISBN: (纸本)9781665478571

Adaptive Cruise Control System (ACC) is an essential ADAS system for the autonomous vehicle for preventing all kinds of collision in the complex traffic and road situations. Ramp merging has been considered as one of the special traffic situations that can be used for testing the performance of ACC and sensor fusion requirements. To realise the project outcomes, the parametres of Long-Range Radar and Short-Range Radar has been implemented onto the IPG CarMaker with several scenarios of ramp merging been generated for simulating the ACC performance under different sensors and sensor fusion strategies. According to the simulation result, the stability of ACC performance under ramp merging needs to be permitted through adding sensor fusion between Long and Short Range Radar in order to prevent all kinds of collision between different dynamic models of ego and front target vehicle. © 2022 ieee.

关键词： Adaptive control systems

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Smart Sports Video Tracking and Safety Solutions Integrating GPS and Satellite Communications 4

Smart Sports Video Tracking and Safety Solutions Integrating...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ying, Sui Tianjin Vocational College of Communications Tianjin300110 China

ISBN: (纸本)9798331523923

As the system of intelligent sensing and video surveillance continues to improve, its application in different fields has also emerged. In this study, the smart sports video tracking and safety solutions integrating the GPS and satellite communications is discussed. The core goal of this research is to address the needs of athlete tracking and safety in complex environments. The designed system consists of three core modules, namely, efficient video tracking module, high-precision GPS tracking module and stable satellite communication module. First, the proposed video tracking module uses a Laplace operator-based deblurring algorithm for video image processing. Subsequently, the human target in the moving video is accurately tracked through the two-dimensional flow field distribution. As a global optimization, the edge similarity feature segmentation is used to achieve dynamic marker tracking. The designed GPS tracking module uses the single difference observation method to improve positioning accuracy. This module combines the direct rounding fuzzy solution to effectively solve the coordinate conversion and measurement error problems. Finally, the satellite communication module used works through the coordination of the main control machine, main controller and signal switching module. This architecture realizes efficient data transmission and processing. Through experimental verification, the effectiveness of this tracking and assurance framework can be explored. © 2025 ieee.

关键词： Satellite communication systems

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Deep Learning Algorithm for the People Identification Using their ECG signals as a Biometric Parameter 46

Deep Learning Algorithm for the People Identification Using ...

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46th international conference on Telecommunications and signal processing, TSP 2023

作者： Rincon-Melchor, Victor Nakano-Miyatake, Mariko Juarez-Sandoval, Oswaldo Olivares-Mercado, Jesus Moreno-Saenz, Jairo Benitez-Garcia, Gibran Instituto Politecnico Nacional Sepi Esime Culhuacan Mexico City04440 Mexico The University of Electro-Communications Department of Mechanical and Intelligent Systems Engineering Tokyo Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350303964

Reliable identification and authentication methods are a growing concern in various domains. Despite facial recognition, fingerprint and iris recognition being common options in the field of biometrics, their inherent vulnerabilities have led to exploration of new options. In particular, electrocardiogram (ECG) signals are gaining interest as a promising biometric feature due to its unique and difficult-to-falsify characteristic. In addition, the ECG is a noninvasive signal that can be collected quickly and easily. Therefore, in this paper, we propose a deep learning algorithm capable of performing biometric identification using ECG signals, highlighting the potential of these signals as a unique biometric feature. Our work focuses on utilizing a Transformer neural network for biometric identification based on pattern recognition of ECG signals. This type of network includes attention layers to highlight the most relevant parts of the signal, and we describe the process of training and evaluating the model. © 2023 ieee.

关键词： Learning algorithms

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Deep Learning Approaches for Epileptic Seizures Recognition based on EEG signal 46

Deep Learning Approaches for Epileptic Seizures Recognition ...

引用

46th international conference on Telecommunications and signal processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Recognition system of human actions is a popular research field in healthcare which is essential in identifying abnormal patient activities to estimate their psychological state. Epileptic seizures are a common neurological disorder affecting millions of individuals globally. Early and accurate diagnosis of epilepsy can lead to up to 70% of seizure-free patients. There is a requirement for smart and automated HAR systems to aid clinicians in accurately diagnosing neurological disorders. This study examines the latest deep learning networks for time-series classification to detect epileptic seizures using EEG signal data. To enhance recognition performance, a hybrid residual network called Inception-ResNet is presented for the binary classification of epileptic activities. Through thorough analysis, the Inception-ResNet model was observed to provide the best results, with an average accuracy and F1-score of 99.61%. © 2023 ieee.

关键词： Electroencephalography

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Discussion on intelligent Control Route of Thermal Power Plant 4

Discussion on Intelligent Control Route of Thermal Power Pla...

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4th international conference on intelligent Control, Measurement and signal processing, ICMSP 2022

作者： Gu, Shan Liang, Xiaoye Zhang, Zhifei Yan, Shubin Zhejiang-Belarus Jt. Lab. of Intelligent Equip. and Syst. for Water Conserv. and Hydropower Safety Monitoring Zhejiang University of Water Resources and Electric Power Hangzhou China China Energy Engineering Group Zhejiang Electric Power Design Institute CD. LTD. Hangzhou China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

intelligent power plant is the future development direction of power generation enterprises, and it is the only way for power generation enterprises to realize the deep integration of information and industrialization. Aiming at the deficiency of the control system of the current thermal power plant, this paper focuses on the design of intelligent control system of the intelligent power plant and puts forward a variety of intelligent control strategies. © 2022 ieee.

关键词： Control systems

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DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions 39

DEVO: Depth-Event Camera Visual Odometry in Challenging Cond...

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ieee international conference on robotics and Automation (ICRA)

作者： Zuo, Yi-Fan Yang, Jiaqi Chen, Jiaben Wang, Xia Wang, Yifu Kneip, Laurent Beijing Inst Technol Sch Opt & Photon Key Lab Optoelect Imaging Technol & Syst Minist Educ Beijing 100081 Peoples R China ShanghaiTech Univ Mobile Percept Lab Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781728196817

We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera. Our framework balances accuracy and robustness against computational efficiency towards strong performance in challenging scenarios. We extend conventional edge-based semi-dense visual odometry towards time-surface maps obtained from event streams. Semi-dense depth maps are generated by warping the corresponding depth values of the extrinsically calibrated depth camera. The tracking module updates the camera pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach is validated on both public and self-collected datasets captured under various conditions. We show that the proposed method performs comparable to state-of-the-art RGB-D camera-based alternatives in regular conditions, and eventually outperforms in challenging conditions such as high dynamics or low illumination.

关键词： Location awareness Image processing Robot vision systems Dynamics Lighting Cameras Robustness

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