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Simulink Implementation of MFCC for Audio signal processing Applications 1

Simulink Implementation of MFCC for Audio Signal Processing ...

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1st international conference on intelligent Technologies for Sustainable Electric and Communications systems, iTech SECOM 2023

作者： Susithra, N. Akshara, R. Harshini, A. Aiswarya, P. Abinaya, M.R. Rajalakshmi, K. Psg Institute of Technology and Applied Research Department of Ece India Psg Institute of Technology and Applied Research Department of Ece Tamilnadu India Psg College of Technology Department of Ece India

ISBN: (纸本)9798350371185

Speech recognition is has been used in wide applications such as Voice-Activated systems, Virtual Assistants, and Interactive Voice Responses (IVR). Feature extraction is a crucial stage in speech and audio signal processing for various applications, including speech recognition, speaker identification, speaker authentication and audio categorization. The accuracy of any speech recognition technique depends on the type of feature extraction method used. The most commonly used techniques are Linear Predictive Coding (LPC), Mel Frequency Cepstral coefficients (MFCC), and Perceptual Linear Prediction. Among these, MFCC is the most effective and computationally efficient method. This paper focuses on the implementation of MFCC using Simulink for speech signal processing applications. The implementation of feature extraction using MFCC in Simulink involves several steps, including pre-emphasis, framing, windowing, Fast Fourier Transform, Mel-filter bank application, and extraction of cepstral coefficients. Finally, a recurrent neural network is used to detect the presence of the word 'yes' based on the extracted features. In summary, this paper highlights the significance of feature extraction in speech recognition. The Simulink implementation of MFCC feature extraction is explained in detail, and the use of a recurrent neural network for detecting the presence of a specific word is demonstrated. © 2023 ieee.

关键词： Simulink

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Prescribed Performance Control of Mobile Wheeled Inverted Pendulum systems under Arbitrary Initial Conditions 8

Prescribed Performance Control of Mobile Wheeled Inverted Pe...

引用

8th ieee international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Zhang, Mengshi Cao, Yu Yang, Bo Huang, Jian School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen518060 China

ISBN: (纸本)9798350300178

Mobile wheeled inverted pendulum (MWIP) is a typical naturally unstable underactuated system and it is very necessary to constrain its transient response to improve the overall safety. Prescribed performance control (PPC) is an effective method, however the system initial conditions are usually required to be known and constraint, otherwise it can lead to singularity problems. In practical systems, it is often difficult to obtain the exact initial state leading to limitations in the application of PPC. To overcome this issue, this paper presented a composite error transformation function to address the problem of initial condition dependence. A continuous bounded function was designed to compress the arbitrary initial error into the predefined bound to avoid singularities. After a set time, it was degraded to an original system error. Based on this composite transformation, a state feedback controller was proposed for the underactuated MWIP system to achieve the constraint outputs. Then, by employing the Lyapunov theorem, it was guaranteed that the closed-loop system would remain stable. Various numerical simulations under different initial conditions were carried out to prove the validity of the proposed controller. © 2023 ieee.

关键词： Closed loop systems

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Interconnection of LoRa Based IoT Nodes on 5G

Interconnection of LoRa Based IoT Nodes on 5G

引用

2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Malik, Mandeep Kothari, Ashwin Pandhare, Rashmi A. IIIT-Nagpur India VNIT Nagpur India

ISBN: (纸本)9798350379525

5G along-with loT are two of the most disruptive technologies shaping modern innovation, particularly in the realms of connectivity, sensing, and communication. Their convergence holds immense potential to unlock diverse opportunities, particularly for advanced applications in smart cities. 5G, as a next-generation network, is fully virtualized and designed for seamless integration with non-3GPP technologies, while loT operates as an expansive, low-power ecosystem optimized for sensing and actuation. However, loT's constrained transport capabilities and inherent security vulnerabilities, owing to its low-power architecture, present notable challenges. Before integrating Low Power Wide Area Networks (LPWAN) with 5G, it is crucial to address security concerns, especially those related to authentication and the seamless roaming of loT devices across 5G networks. The integration of these technologies is expected to drive significant advancements in smart city infrastructure and beyond, but robust solutions must be developed to overcome the inherent risks. This paper advocates for leveraging existing cellular network authentication frameworks, obfuscating device identities to enhance privacy, storing authentication credentials within home networks to mitigate roaming-related issues, and employing loT gateways to manage device address translation. By implementing these strategies, a secure and seamless integration of 5G and loT can be realized, laying the foundation for a more connected, resilient, and secure future. Additionally, this convergence will facilitate the deployment of Internet of Battlefield Things (loBT) for surveillance-centric applications, broadening the scope of both civilian and defense-related use cases. © 2024 ieee.

关键词： 5G mobile communication systems

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Artificial Intelligence Enhanced signal processing in Low-Profile Wearable Wideband Antenna for Future 5G Wireless communications 3

Artificial Intelligence Enhanced Signal Processing in Low-Pr...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Rentapalli, Vanitha Rani Roy, Bappadittya VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravati India

ISBN: (纸本)9798350320749

Artificial intelligence-based communication is the newtechnological tool, that is going to apply for signal processing techniques gives the main consociate to implement any type of applications. The antenna that needs to be designed for 5G wireless networks need to attain broad concepts over broadband artificial technology and its structural formations for all kinds of wearable antenna developments. Moreover, the artificial intelligent technology was recently admitted in communications to work with real modern technology concepts to resolve the unknown and hidden data related to innovative communication systems. AI could apparently handle all kinds of scientific signal processing problems in a data determined manner, to enhance the inherent characteristics to extract the real data. Hence the AI is particularly suitable for signal processing communications over advanced technical networks, hardware impairments and other dynamic transmissions. In this paper we are going to present over the key features of artificial intelligence sensing techniques, and how the intelligent processing features helps us to design the 5G wireless technology for advanced wearable antenna designs. The simulation results depict that how the 5G technology gives its performance characteristics over the data transmission. © 2023 ieee.

关键词： Artificial intelligence

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Weak Maneuvering Target Detection Using Cost-Reference Particle Filter Bank 5

Weak Maneuvering Target Detection Using Cost-Reference Parti...

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5th international conference on intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Lu, Jin Peng, Guojie Sun, Changming Zhang, Weichuang School of Electrical Information and Artificial Intelligence Shaanxi University of Science & Technology Xi'an China CSIRO Data61 NSW Australia Institute for Integrated and Intelligent Systems Griffith University QLD Australia

ISBN: (纸本)9798350336030

In this paper, a novel cost-reference particle filter bank (CRPFB) based method is proposed for detecting a low signal to noise ratio (SNR) nonlinear frequency modulated signal. The proposed method has two stages. First, the designed CRPFB is used for state estimation of a possible target. Second, a test metric (i.e., generalized likelihood ratio) based on the state estimation of the first stage is compared with an analyzable threshold for determining the presence of a target. In first stage, the CRPFB consists of many parallel cost-reference particle filters (CRPFs). The output of the CRPF with the minimum cumulated cost is considered as the estimation results of the CRPFB. In second stage, the threshold can be analytically obtained from several noisy-only measurements by using generalized extreme value distribution. Simulation results illustrate that, compared with the state-of-the-art methods, the proposed method has low computational complexity and its detection threshold can be analytically estimated. © 2023 ieee.

关键词： Filter banks

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Comparative Analysis of Federated Learning Techniques in Big Data-Driven intelligent Transportation systems 8

Comparative Analysis of Federated Learning Techniques in Big...

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8th international conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Kaleem, Sarah Babar, Muhammad Qureshi, Basit Ei-Affendi, Mohammed Koubaa, Anis Prince Sultan University Riyadh Saudi Arabia lqra University EIAS Lab Islamabad Pakistan CClS Prince Sultan University Robotics and Internet of Things Lab Riyadh Saudi Arabia CCIS Prince Sultan University KSA RIOTU Lab Saudi Arabia CCIS Prince Sultan University KSA EIAS Data Science Lab Saudi Arabia

ISBN: (纸本)9798331539696

The advancement of intelligent Transportation systems (ITS) represents a pivotal development in IoT technology, improving traffic management, reducing accidents, and optimizing transport networks. ITS generates vast data streams from interconnected devices, offering valuable insights into travel and road conditions. However, leveraging this extensive data poses significant performance, privacy, and scalability challenges. In this context, Federated Learning (FL) emerges as a crucial solution. This paper contributes a comprehensive comparative analysis of various FL algorithms in processing ITS Big Data, focusing on their effectiveness in managing and analyzing data while preserving user privacy and ensuring scalability. Using a dataset from Udacity, provided by Roboflow, consisting of 15,000 high-quality images across 11 categories formatted for YOLO, we conduct a comparative evaluation of FedSGD, FedAvg, and FedProx. Through this comparative evaluation, we demonstrate the potential of FL to manage and analyze ITS data while preserving user privacy and ensuring scalability. Our results highlight that FedAvg achieves the highest accuracy at 79.01%, outperforming FedSGD at 78.10% and FedProx at 78.61%. © 2025 ieee.

关键词： Federated learning

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A Real-time Framework of Multi-Camera Vehicle Tracking System

A Real-time Framework of Multi-Camera Vehicle Tracking Syste...

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2024 ieee international conference on Real-Time Computing and robotics, RCAR 2024

作者： Zhuang, Hanyang Chen, Xingyuan Lan, Jianglin Wang, Ying Lu, Zhaoxuan Wang, Chunxiang Yang, Ming University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200040 China Shanghai Intelligent and Connected Vehicle R and D Center Co. LTD Shanghai201400 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

Multi-camera vehicle tracking (MCVT) system is a key technology to build an intelligent city and intelligent transportation system. The MCVT system utilizes roadside monitoring devices and computing platforms to achieve city-level road traffic perception at a finer scale by tracking each vehicle on the road to obtain the trajectory. The existing MCVT system mainly focuses on dealing with the algorithm modules separately, without studying the real-time performance and system architecture of the entire pipeline. However, in real-world applications, many of the tricks in improving evaluation metrics are not applicable since there is endless traffic flow running all the time. Therefore, this paper proposes a real-time framework of the MCVT system to streamline all the algorithm modules with proper data flow. Firstly, a multi-threaded multi-vehicle tracking (MVT) module is developed to detect vehicles in each camera. Re-identification is implemented during MVT to provide the feature for tracking in the same camera and across different cameras. Then, after the vehicle leaves the camera field of view, the feature of vehicle trajectory is calculated by averaging the feature at each frame. Meanwhile, a background thread runs continuously to match the incoming feature of vehicle trajectory with past features by conducting a topology searching method. In the end, the vehicle trajectories with well-matched features are considered to be the same vehicle. The entire pipeline has been validated on two different datasets, the precision is 0.64 in the CityFlow dataset which uses 6 cameras simultaneously. In the customized dataset, the same method can still reach real-time when using 16 cameras at the same time with smaller network modules. © 2024 ieee.

关键词： Vehicle locating systems

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Noncontact intelligent Vibrometer Based on Laser Triangulation 5

Noncontact Intelligent Vibrometer Based on Laser Triangulati...

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5th international conference on intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Zhang, Wenhao Yu, Jingwei Ju, Bingfeng Wu, Jiajun Zhejiang University State Key Laboratory of Fluid Power & Mechatronic Systems Hangzhou China

ISBN: (纸本)9798350336030

Effective vibration measurement is crucial for monitoring the health of mechanical systems and preventing failure. Current methodologies face limitations such as mass loading, signal attenuation, and environmental sensitivity. This paper presents a novel noncontact laser triangulation vibrometer (LTV) based on the laser triangulation principle for vibration measurements. The LTV offers rapid sampling, high precision, compact design, and easy integration, addressing the challenges faced by existing techniques. Validation experiments, including low-frequency tests on a piezoelectric actuator and high-frequency tests on a speaker diaphragm, demonstrate the LTV's high accuracy, repeatability, and adaptability in vibration measurement applications. Results show that the LTV can measure vibrations up to 45 kHz, and can achieve a low residual nonlinearity of ± 0.0121% F.S. and a low noise RMS of 0.549 μm. The proposed LTV provides a high-performance alternative to traditional vibration measurement methods for monitoring machinery health, diagnosing faults, and reducing maintenance costs. © 2023 ieee.

关键词： Vibration measurement

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A study on Image processing Method for Noise Reduction in Digital Holographic Microscopy 9

A study on Image Processing Method for Noise Reduction in Di...

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9th international conference on intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Nagashima, Masaharu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Iitc Hankyong National University School of Ict Robotics and Mechanical Engineering Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Digital Holographic Microscopy (DHM) can obtain three-dimensional (3D) information about the fine structure of an object by utilizing the phase information of coherent light. In DHM, during the process of reconstructing the shape of an object, phase information is extracted by windowing the sideband containing the phase information. The 3D information of the object is then obtained from the acquired phase information. When windowing the sideband in a narrow range, it can remove noise caused by the DC spectrum, but detailed shape information of the object is lost. On the other hand, when windowing in a wide range, detailed shape information of the object can be obtained, but noise from the DC spectrum increases. To solve this trade-off relationship, we propose a method of determining the range for windowing the sideband using a function and then averaging the windowed sidebands. This proposed method can reduce the impact of noise from the DC spectrum and enables the acquisition of the detailed shape information. In this paper, we explain the reconstruction algorithm of this method and compare it with conventional methods and the previous study, phase differences averaging (PDA). © 2024 ieee.

关键词： Noise abatement

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Detection of TEMPEST audio compromising signal using artificial intelligence 15

Detection of TEMPEST audio compromising signal using artific...

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15th international conference on Communications, COMM 2024

作者： Vizitiu, Alexandru-Madalin Dobrescu, Lidia Molder, Cristian Sebacher, Bogdan Trip, Bogdan Catalin Butnariu, Vlad Florian National University of Sciences and Technologies Politehnica Bucharest Faculty of Electronics Telecommunications and Information Technology Bucharest Romania "Ferdinand I"Military Technical Academy Center of Excellence in Robotics and Autonomous Systems - CERAS Bucharest Romania Military Technical Academy "Ferdinand I" Department of Applied Informatics Bucharest Romania

ISBN: (纸本)9798350355956

In the contemporary landscape of cybersecurity, electronic devices are increasingly susceptible to various forms of exploitation, with TEMPEST (Transient Electromagnetic Pulse Emanation Standard) attacks posing a significant threat. This article delves into the application of artificial intelligence (AI) for detecting vulnerabilities in electronic devices through audio signal analysis within the TEMPEST domain. By leveraging advanced AI techniques, particularly machine learning algorithms, this study identifies a novel approach regarding the TEMPEST evaluation of electronic devices which process audio signal using artificial intelligence. The process involves the collection and processing of audio signals emanating from electronic devices, followed by the application of AI models to detect the presence of audio information in the received signal. The findings underscore the efficacy of AI involved in TEMPEST evaluation of electronic devices. © 2024 ieee.

关键词： artificial intelligence audio emission security TEMPEST

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