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Performance Analysis of Discrete Wavelet Transforms for Acoustic Scene Classification with DCASE Dataset 1

2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024

作者： Singh, Vikash Kumar Sharma, Kalpana Sur, Samarendra Nath Department of Computer Science and Engineering Sikkim Manipal Institute of Technology SMU Sikkim Majitar India Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology SMU Sikkim Majitar India

ISBN: (数字)9783031691461

ISBN: (纸本)9783031691454

To control the chaos during an acoustic scene, it is crucial to recognize each particular sound class in the surrounding. This paper discusses the development of a model for acoustic scene classification, incorporating Fast Dynamic Time Warping (DTW) and reference audio. The main goal of the model is to correctly classify various sounds that are produced in environments, including airport, metro stations, parks, public square, bus etc. The model successfully identifies similarities across the extensive dataset of 23035 audio samples gathered from the Detection and Classification of Acoustic Scenes and Events (DCASE) 2021 Challenge Task 1 by employing the Fast DTW approach. In light of the significance of handling extensive audio data, the integration of reference audio diminishes processing complexity. Additionally, the suggested model investigates the effectiveness of features from several Discrete Wavelet Transforms, including Meyer, Symlets, and Daubechies, to improve classification accuracy. The acquired results, which demonstrate the model’s efficacy and breakthroughs in acoustic scene classification, are compared with the DCASE 2021 baseline model and other technical papers presented in the same year. With the Meyer wavelet characteristic of audio data, the suggested model attains an accuracy of 79.92%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Subway stations

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Recognition of De Novo Drug Design for Targeted Chemical Libraries through Optimization Techniques 5

Recognition of De Novo Drug Design for Targeted Chemical Lib...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Balamurugan, K.S. Navaneethakrishnan, Sundara Rajulu Srinivasulu, Senduru Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli Trichy India Sathyabama Institute of science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Jeppiaar Institute of Technology Department of Electronics and Communication Engg. Tamilnadu Sriperumbudur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331504403

The process of De Novo drug design involves the integration of artificial intelligence with computational intelligence techniques. The proposed system aims to provide precision therapeutics through the development of targeted chemical libraries designed for individual patient's needs. They also highlight the potential impact of precision medicine. The traditional approach involves trial and error methods in the combination of various chemicals for desired drug discovery that leads to the waste of time and energy. These challenges are overcome through the aid of artificial intelligence techniques. The interaction of molecules, physiochemical properties, and biological interaction of potential drugs are obtained through the aid of optimization algorithms. With the integration of computational intelligence, the design structure becomes more adaptable. This helps in the formation of chemically diverse compound libraries. The important aspect of the proposed system involves the ability of advanced precision therapeutics. The clinical information is necessary for the identification of molecular targets achieved through the aid of optimization algorithms. These obtained targets are necessary for the development of new drugs with high affinity and specificity. This helps in minimizing the off-target effects which improves the therapeutic efficacy. The novel molecular structure is generated using the Generative Adversarial Networks (GAN). The compound properties are optimized using reinforcement and an iterative learning process. The simpler structure of drug-like molecules is obtained through an evolutionary algorithm. Thus the proposed system provides rapid exploration of chemical space that leads to the formation of chemical compounds with personalized pharmacological profiles. © 2024 IEEE.

关键词： Drug discovery

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Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

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computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud Al Naimi Syed Alwee Aljunid Syed Junid Muhammad lmran Ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

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OMN-IBUS Type of Networking System for the communication System in 6G 4

OMN-IBUS Type of Networking System for the Communication Sys...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Devi, A. Sangeetha Bhatt, Deepanshu Bansal, Saloni Keerthika, A. Mohanraj, T. Santhosh, S. Tamil Nadu T.M.Palayam India Ies Institute of Technology and Management Ies University Department of Mechanical Engineering M.P Bhopal India Gla University Department of Computer Engineering and Applications Mathura India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India TN India Einstein College of Engineering Department of Electronic and Communication Engineering Tamil nadu India

ISBN: (纸本)9798350360165

The scientific community and industry have been working together in recent years to shift computational resources off centralized cloud systems to decentralized computing, or 'edge' computing. This change is a result of the cloud system's inability to keep up with the growing demands of emerging relationships, which are marked by the spread of time-sensitive applications like sophisticated robotics, augmented reality/virtual the truth technologies, self-driving cars, and vital remote control of Internet of Things applications. Decentralized computing at the border is still in its infancy and lacks a full platform, despite the critical role that it will play in future distributed systems. This article presents OMNIBUS, a novel decentralized edge architecture. This approach uses moving vehicles as resources for processing and storage, enabling the continuous delivery of computational capability to end-devices across many locations. Scalability and flexibility are two characteristics that set OMNIBUS distinct from other edge computing solutions. The suggested method, which is based on mathematical models of prediction, determines computational capacity for particular locations by learning the direction and time of vehicle movements. It also presents a theoretical foundation for learning, improvement, and caching amid dynamic conditions caused by moving vehicles, specifically for sequential concurrent conversion. OMNIBUS's scalability allows for almost infinite growth, which might result in a significant increase in network speed. © 2024 IEEE.

关键词： Scalability

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Signal Detection and Channel Estimation in Multiple Input Multiple Output communication Systems using Distributed Approximate Message Passing 4

Signal Detection and Channel Estimation in Multiple Input Mu...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Hussein, Layth Saranya, R. Yajaman, Gummagatta Vybhavi Malathy, V. Kumar, S. Senthil College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Bharathiyar Institute of Engineering for Women Department of Electronics and Communication Engineering Salem India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Sr University Department of Electronics and Communication Engineering Warangal India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent years, Multiple-Input Multiple-Output (MIMO) have revolutionized reliability and spectral efficiency of wireless communication systems. These systems exploit multiplexing and spatial diversity which enabling the essential for meeting the growing demands of modern wireless networks but it had challenges in detection of signals and estimation of channels. The traditional approaches for signal detection had faced challenges which include computational resources and network architecture. Therefore, this research proposes Distributed Approximate Message Passing (DAMP) for Signal Detection and Channel Estimation in MIMO communication systems. The collected data contains various digital and communication system which are pre-processed by using Deceived Non-Local Means (DNLM) and reduce the noise. These pre-processed data optimal features are extracted using Independent Component Analysis (ICA) with wavelet transform that separates mixed signals into independent components for eliminating the irrelevant attributes. Finally, Distributed Approximate Message Passing (DAMP) is employed that enables message passing framework to approximate the posterior distribution for detecting the signal and channel estimation in MIMO communication systems. The proposed DAMP succeeded with high results including accuracy (98.92%), precision (94.85%), recall (96.86%), F1-score (94.35%) when compared with existing Generative Adversarial Network (GAN). © 2024 IEEE.

关键词： Channel estimation

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Deep Learning based Recognition of Auto Covariance Signatures through Protein Interaction Model for Amino Acid Sequence 8

Deep Learning based Recognition of Auto Covariance Signature...

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8th International Conference on electronics, communication and Aerospace Technology, ICECA 2024

作者： Balamurugan, K.S. Geetha Rani, K. Sadish Sendil, M. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Bangalore560069 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Vel Nagar Avadi Chennai India Jeppiaar Institute of Technology Sriperumudur Department of Electronics and Communication Engg Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350367904

The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. The model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. The approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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Study on Robot Grasping System of SSVEP-BCI Based on Augmented Reality Stimulus

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Tsinghua Science and Technology 2023年第2期28卷 322-329页

作者： Shangen Zhang Yuanfang Chen Lijian Zhang Xiaorong Gao Xiaogang Chen School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Institute of Mechanical Equipment Beijing 100854China Department of Biomedical Engineering School of MedicineTsinghua UniversityBeijing100084China Institute of Biomedical Engineering Chinese Academy of Medical Sciences and Peking Union Medical CollegeTianjin 300192China

Although notable progress has been made in the study of Steady-State Visual Evoked Potential(SSVEP)-based Brain-computer Interface(BCI),several factors that limit the practical applications of BCIs still *** of these factors is the importability of the *** this study,Augmented Reality(AR)technology was introduced to present the visual stimuli of SSVEP-BCI,while the robot grasping experiment was designed to verify the applicability of the AR-BCI *** offline experiment was designed to determine the best stimulus time,while the online experiment was used to complete the robot grasping *** offline experiment revealed that better information transfer rate performance could be achieved when the stimulation time is 2 *** of the online experiment indicate that all 12 subjects could control the robot to complete the robot grasping task,which indicates the applicability of the AR-SSVEP-humanoid robot(NAO)*** study verified the reliability of the AR-BCI system and indicated the applicability of the AR-SSVEP-NAO system in robot grasping tasks.

关键词： Steady-State Visual Evoked Potential(SSVEP) Brain-computer Interface(BCI) Augmented Reality(AR) robot grasping system

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Optimizing sum rates in IoT networks: A novel IRS-NOMA cooperative system

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ICT Express 2025年第3期11卷 448-453页

作者： Kumar, Mukkara Prasanna Summaq, Ammar Chinnadurai, Sunil Selvaprabhu, Poongundran Kumaravelu, Vinoth Babu Sarker, Md. Abdul Latif Han, Dong Seog Department of Electronics and Communication Engineering School of Engineering and Sciences SRM University-AP 522502 India Department of Communication Engineering School of Electronics and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Centre for ICT & Automotive Convergence Kyungpook National University Daegu 41566 South Korea School of Electronic and Electrical Engineering Kyungpook National University Daegu 41566 South Korea

Intelligent Reflecting Surfaces (IRS) offer a promising solution for enhancing sum rates in wireless networks by dynamically adjusting signal reflections to optimize propagation paths. When combined with Non-Orthogonal Multiple Access (NOMA), which enables multiple users to share the same frequency band, significant improvements in spectral efficiency can be achieved. However, as the number of users increases in IRS-NOMA systems, ensuring consistently high data rates for all users becomes challenging due to coverage limitations and inefficient power allocation in static network configurations, leading to performance degradation in multi-user scenarios. To address these limitations, we propose a novel IRS-NOMA cooperative system designed to optimize sum rates through an intelligent power allocation algorithm, nearby users, and IRS to assist the base station in delivering signals and expanding network coverage. The proposed system operates in two phases: during the first phase, the base station transmits signals directly to users and indirectly through the IRS. In the second phase, nearby users assist in relaying signals to enhance coverage and reliability. The proposed system adopts a cascaded channel model to accurately capture the interactions between the base station, IRS, and users. By leveraging our optimization algorithm, the proposed system ensures efficient resource allocation, achieving superior spectral efficiency and fairness among users compared to traditional models. Numerical results validate the effectiveness of the proposed system, demonstrating its potential for next-generation IoT networks. © 2025 The Authors

关键词： Intelligent Reflecting Surface IoT Non-orthogonal multiple access Orthogonal multiple access

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On the Mates of Cross Z-Complementary Pairs for Training Sequence Design in Generalized Spatial Modulation

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IEEE Access 2023年 11卷 145231-145237页

作者： Huang, Zhen-Ming Hu, Chen Liu, Zilong Chen, Chao-Yu University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom Institute of Computer and Communication Engineering National Cheng Kung University Tainan701 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan National Cheng Kung University Department of Engineering Science Tainan701 Taiwan

Generalized spatial modulation (GSM) as a novel multiple-antenna transmission technique is an extension of spatial modulation (SM). The GSM system provides flexibility in terms of spectral efficiency, energy efficiency, and the cost of radio-frequency (RF) chains. In the literature, cross Z-complementary pair (CZCP) has been proposed for optimal training sequence design in SM, but not for GSM. Due to its specific zero correlation zone (ZCZ) properties for autocorrelation sums and cross-correlation sums, optimal channel estimation performance in SM over frequency-selective channels can be achieved. In this paper, we propose an efficient construction for mutually orthogonal mate of a CZCP and provide a new construction of CZCPs. Based on the proposed CZCP and its mate, a novel training framework of GSM system is presented to achieve the minimum mean square error (MSE) channel estimation performance in frequency-selective channels. Numerical results demonstrate that the proposed CZCP-mate-based training for the GSM system can eliminate inter-symbol interference (ISI) and inter-antenna interference (IAI). © 2013 IEEE.

关键词： Autocorrelation

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