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SSVEP EEG signal classification model based on EEMD-FastICA-TRCA 24

SSVEP EEG signal classification model based on EEMD-FastICA-...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Liu, Yunfeng Zhang, Xueru Wan, Sijia Zhan, Weijian Liu, Yongming Zhao, Zhuanzhe School of Mechanical Engineering Anhui Polytechnic University Anhui Wuhu China School of Artificial Intelligence Anhui Polytechnic University Anhui Wuhu China

ISBN: (纸本)9798400707308

In order to retain as many effective signals in EEG as possible and improve the accuracy of a signal classification and the performance of the brain-computer interface system, a steady-state visual evoked potential EEG classification model combining ensemble empirical mode decomposition, fast, independent component analysis and task-related component analysis was proposed to solve the problem that noise would reduce the accuracy of signal classification and recognition. Firstly, it is proved that the proposed EEMD-FastICA noise reduction model has certain advantages over the standard noise reduction methods, and the performance of the model is verified by using the Benchmark dataset of the brain-computer interface group of Tsinghua University. On this basis, the EEMD-FastICA noise reduction model is combined with the TRCA algorithm to classify EEG signals, and the classification results are compared with other classification models. The comparison results show that under different time Windows, the classification accuracy and information transmission rate of the proposed model are higher than those of other models. Within 1.5 S, the highest classification recognition accuracy was 85.83%, and the highest ITR was 238.34 bits/min. © 2024 Copyright held by the owner/author(s).

关键词： Empirical mode decomposition

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Generalized Innovative Approach to Detect Pest signal based on Frequency using STM32 3

Generalized Innovative Approach to Detect Pest Signal based ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Martin, Betty Sugumaran, G.S. Subhadra, M. Marshiana, D. Sastra Deemed University Thanjavur India Symbiosis Institute of Technology Pune Campus Pune India

ISBN: (纸本)9798331509675

Modern society requires innovative and creative advancements to arrive at practical solutions for real-time problems using microcontrollers. Hence, it is the responsibility of every researcher as well as an analyst to understand the basic working, processing flow, and programming of embedded systems. This paper aims to present a strategy for detecting the frequency of real-time signals followed by the manipulation of glowing patterns of various coloured LEDs (Light Emitting Diode) using a simple STM32 microcontroller. The detection of frequency of input signal is carried out using the timer module present in the STM32 microcontroller. Timer module is used to generate a ramp signal or triangular waveform of a desired frequency. The microcontroller uses embedded C programming (code loaded on the microcontroller) to identify the signal frequency and thereby controls the glowing pattern of coloured LEDs based on the detection of signal frequency. This includes designing, tuning, configuring the timer modules present in STM32 for generating signals of desired frequency and incorporating necessary conditions to control and manipulate the glowing pattern of LEDs according to the detected signal frequency. Moreover, experimental results also prove the efficiency and accuracy in detecting target frequency and in glowing the coloured LEDs with perfect synchronisation which shows the effectiveness of this approach. This paper aims to highlight the practical applications of microcontroller-based techniques and strategies for frequency detection and to provide solutions to the real time problems associated with embedded systems. This paper showcases a cost-effective method using STM32 microcontroller on chip timer module to solving the problem mentioned above. © 2025 IEEE.

关键词： Microcontrollers

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Integrating Attention Mechanisms with Bidirectional Long Short-term Memory Recurrent Neural Networks for Improved Speech Recognition 24

Integrating Attention Mechanisms with Bidirectional Long Sho...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Kimani, Mercy Wairimu Nderu, Lawrence Ndirangu, Dalton Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya United States International University Nairobi Kenya

ISBN: (纸本)9798400718120

Speech-to-text technology is indispensable for converting spoken words into text, facilitating easy storage and retrieval. This process involves several critical stages, progressing from a basic model through signal pre-processing, feature extraction, feature selection, and modeling. There has been extensive literature on improving speech recognition, however, challenges persist, particularly in addressing word error rates, accuracy, latency, and computational efficiency in continuous input streams in a noisy environment. This research systematically evaluates the effectiveness of recurrent neural networks (RNNs), long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), bi-directional long short-term memory (Bi-LSTM), and bi-directional long short-term memory (Bi-LSTM) with attention mechanism networks. It further proposes integrating attention mechanisms with a Bidirectional Long Short Memory Recurrent neural network to enable the model to focus on the crucial parts of the input sequence thus increasing computational efficiency. Experimental results, based on the Libri speech and TIMIT training dataset, demonstrate promising outcomes. The proposed model exhibits superior performance, achieving a word error rate of 1.6%, and an accuracy of 98.4% on the training dataset. As a future avenue of exploration, the study proposes evaluating the use of transformer models to reduce character error rates further and enhance accuracy in continuous input streams. © 2024 Copyright held by the owner/author(s).

关键词： Long short-term memory

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Object-level data Augmentation for Visual 3D Object Detection in Autonomous Driving

Object-level Data Augmentation for Visual 3D Object Detectio...

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

作者： Cheng, Haoran Xu, Junkai Peng, Liang Yang, Zheng He, Xiaofei Wu, Boxi Zhejiang University Hangzhou310027 China FABU Inc. Hangzhou310030 China

ISBN: (纸本)9798350368741

data augmentation plays an important role in visual-based 3D object detection. Existing detectors typically employ image/BEV-level data augmentation techniques, failing to utilize flexible object-level augmentations because of 2D-3D inconsistencies. This limitation hinders us from increasing the diversity of training data. To alleviate this issue, we propose an object-level data augmentation approach that incorporates scene reconstruction and neural scene rendering. Specifically, we reconstruct the scene and objects by extracting image features from sequences and aligning them with associated LiDAR point clouds. This approach is intended to conduct the editing process within a 3D space, allowing for flexible object manipulation. Additionally, we introduce a neural scene renderer to project the edited 3D scene onto a specified camera plane and render it onto a 2D image. Combined with scene reconstruction, it overcomes the challenges stemming from 2D/3D inconsistencies, enabling the generation of object-level augmented images with corresponding labels for model training. To validate the proposed method, we apply our method to various multi-camera 3D object detectors, consistently boosting the performance. © 2025 IEEE.

关键词： data augmentation Visual 3D object detection

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data Acquisition and Pre-processing of EEG signals Using Brainwave Sensor

Data Acquisition and Pre-processing of EEG Signals Using Bra...

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3rd international conference on signal and data processing, icsdp 2023

作者： Negi, Anju Apeksha, S. Bharath, C.H. Uzair, Mohamed PES University 100 ft Ring Road Banashankari Stage III Dwaraka Nagar Banashankari Karnataka Bengaluru 560085 India

ISBN: (纸本)9789819795772

Many brain disorders are very critical and need to be detected at an early stage. This demands the need to create a reliable low-cost, portable system that can acquire the signals and then process and analyze that data. Through this project, the authors aim to address this problem by creating a pre-processing system for an EEG data acquisition system which acquires EEG signals then processes the data to remove noise, and analyze the signal to provide valuable insights. The device is built with several high-pass, low-pass, and notch filters to condition the signal and instrumentation amplifiers for further amplification. An overall amplification of 90× in the first stage and 450× in the last stage is achieved. With the hardware in place, work has been done to integrate the data acquisition system with RLS adaptive filtering algorithms to further enhance the EEG signals by removing EOG artifacts. Later, the signal is analyzed using wavelet transformation and separated into different band frequencies. The proposed post processing system is set to analyze the acquired data for critical brain disorders like depression detection and autism severity indication by using novel approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brainwave computer interface Electroencephalography Feature extraction

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Joint Optimization of Computation Offloading, Dynamic Pricing and Admission Control in Mobile Edge Computing-Enable Blockchain 24

Joint Optimization of Computation Offloading, Dynamic Pricin...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Yi Zhang, Wenjie Zheng, Yifeng Chen, Liming Key Lab of Data Science and Intelligence Application School of Computer Sciences Minnan Normal University Fujian Zhangzhou China Zhangzhou Affiliated Hospital of Fujian Medical University Fujian Zhangzhou China

ISBN: (纸本)9798400710834

In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing (MEC) has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types of miners and a single Edge Service Provider (ESP). Different types of miners have different resource requirements and corresponding prices. Each miner makes a computation offloading decision based on the resource price set by ESP. Different miners possess varying resource requirements and correspond to distinct prices. Miners formulate task offloading decisions based on the prices set by ESP for different resource types. The ESP dynamically adjusts resource price based on miners’ offloading requests and makes admission control according to resource usage in each time slot. The problem is a joint optimization problem of computation offloading, price strategy, and admission control, we model the problem as a sequential decision-making problem and use a dynamic programming algorithm to obtain the optimal solution. Through simulation experiments, our results demonstrate that the proposed algorithm can effectively improve the overall social utility. © 2024 Copyright held by the owner/author(s).

关键词： Computation offloading

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Deep Learning for Automated Image Captioning: A CNN and Transformer Model Analysis 3

Deep Learning for Automated Image Captioning: A CNN and Tran...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saarika, Kathula Varsha, Vasaraju Sri Harsha, P. Sheikh, Anjum Nabi Dept. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology India

ISBN: (纸本)9798331527549

Automatic image captioning is a multidisciplinary effort that combines machine learning, natural language processing, and computer vision in creating meaningful and context-rich captions for images. Within a short time, this research problem has garnered a lot of attention and new techniques are continuously being proposed. However, there are many dismaying factors as well. For instance, the current models still have a long way to go in attaining human accuracy. This paper seeks to conduct a systematic review of the recent trends and developments in the field of image captioning techniques and models with a focus on 2023 and 2024 by systematically analyzing the 13 publications located in Springer and IEEEXplore libraries. According to our research, Convolutional Neural Networks allow the identification of image content at an early stage, and Recurrent Neural Networks or Long Short-Term Memory networks usually appear at the text generation stage. The most common image datasets are MS COCO and Flickr8k. Almost all studies utilized the BLEU scores (1, 2, 3, and 4) mechanism. It is alleviated that CNN with LSTM models tends to perform better when compared to RNN-modeled networks. Attention mechanisms and Encoder-Decoder architecture are aggressively becoming the two major solutions to strengthening image captioning. This review will help and provide direction to those who are interested in practicing research that aims to promote the techniques and processes of automatic image captioning. © 2025 IEEE.

关键词： Convolutional neural networks

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A Spatio-Temporal Multi-Subgraph Convolutional Network for Parkinson’s Disease Detection Using Gait data 24

A Spatio-Temporal Multi-Subgraph Convolutional Network for P...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wu, Huimin Liu, Yongcan Wang, Xianquan Yu, Xinglin Zhao, Aite College of Computer Science and Technology Qingdao University Shandong Qingdao China Institute of Advanced Technology University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400707308

Gait disturbance is a key motor symptom of Parkinson’s disease (PD) and has been widely studied for diagnosis and severity assessment using non-invasive sensor data. However, most existing methods for PD detection struggle to adaptively model sensor positions and inter-signal correlations within a multi-sensor system. Additionally, these sensors form a complex system with multiple subsystems monitoring local regions, and their interrelations are often overlooked. In this work, we propose a spatio-temporal multi-subgraph convolutional network (ST-MSGCN), a hybrid framework using graph neural networks to capture gait differences between healthy individuals and PD patients from multi-sensor data. We first decouple the overall sensor system and encode inter-subsystem correlations into multiple subgraphs. The ST-MSGCN then employs a spatio-temporal graph convolutional network to analyze 3D-graph representations of foot force signals, exploring spatial and temporal characteristics within each subgraph. We also introduce channel-wise topology and temporal attention mechanisms into the graph convolutional layers to dynamically learn discriminative features. Finally, a decision-making layer using a voting strategy fuses observations and state estimations from multiple graphs. The proposed method is validated on the PDgait dataset and compared with several existing models. Additionally, it is evaluated on a normal skeleton-based dataset to explore the relationships among body parts. © 2024 Copyright held by the owner/author(s).

关键词： Neurodegenerative diseases

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Research on the Application of Cloud Computing-based Dual-Machine Hot Standby System in Railway signal Safety Management 3

Research on the Application of Cloud Computing-based Dual-Ma...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ji, Zhe Wang, Ding Liu, Bing Liu, Yiming Research and Design Institute Co. Ltd Shenzhen China China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (纸本)9798331508456

This paper investigates the application of cloud computing-based dual-computer hot standby computer security platform in railway signalling security management, aiming to enhance the reliability and security of railway signalling system. With the increasing importance of railway transport to modern society, the safety of railway signal system has become a key factor for public safety and social stability. This paper first analyses the security challenges faced by railway signalling systems and explores the potential of dual-machine hot-standby systems in improving system availability and security. Then, this paper details the design and implementation of a cloud-based dual-machine hot standby architecture, including system architecture, signal monitoring module, data encryption module, security audit module, and event response module. Through experimental validation, this paper compares the performance of the cloud-based dual-machine hot-standby architecture with that of the traditional single-machine system in terms of failure recovery time, resource utilisation and system response time, and the results show that the dual-machine hot-standby architecture has significant advantages in these key performance indicators. Finally, the paper discusses the limitations of the research and proposes directions for future research, including cost-benefit analysis, resource optimisation, technology adaptation research and intelligent development, with a view to providing a more efficient and intelligent solution for railway signal safety management. © 2025 IEEE.

关键词： Subway signal systems

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High-Speed Area Efficient Approximate Kogge–Stone Adder 3rd

High-Speed Area Efficient Approximate Kogge–Stone Adder

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Dantla, Sudhakar Reddy Tummala, Prudhvi Musala, Sarada Kanapala, Satish Department of Electronics and Communication Engineering Vignan’s Foundation for Science Technology & Research Andhra Pradesh Vadlamudi India

ISBN: (纸本)9789819719426

Approximate parallel prefix adders are a type of circuit that can perform addition operations on binary numbers with high speed, low area, and less power. These circuits are designed to provide approximate results, which means that they sacrifice accuracy for efficiency. This trade-off makes them ideal for use in applications where speed is more important than precision. In recent years, there has been a growing interest in the improvement of these circuits, as they have the potential to revolutionize the field of digital signal processing. Kogge–Stone (KS) adder is a kind of parallel prefix adder that has the strong point of quickest addition primarily based on design time. Approximation Kogge–Stone (AxKS) adder is a variant of basic Kogge–Stone Adder that introduces a level of approximation in its operation. In this paper, a new approximate KS Adder is proposed with high-speed performance and less area. The KS Adder and its AxKS Adder are compared for bits 8, 16, 32 using software tool Xilinx Vivado. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital signal processing

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