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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Performance Analysis of Word Recognition System Using Tensor Flow 5th

Performance Analysis of Word Recognition System Using Tensor...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Kumar, Bittu Sri Ram Rahul, P. Karthikeya, G. Sai Nithin Vishnu, T.V. Srikanth, M. Shivani, Peta Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad500075 India

ISBN: (纸本)9783031770746

Word recognition stands as a pivotal element within natural language processing (NLP) and machine learning, crucial for diverse applications ranging from automatic speech recognition to optical character recognition and from text analysis to machine translation. this paper explores TensorFlow, a robust open-source machine learning framework, to tackle the complexities associated with word recognition. the research introduces innovative methods employing deep learning and neural networks to enhance the precision and efficiency of word recognition tasks. the evolution of word-to-text recognition technologies has witnessed transformative strides in recent years, impacting various industries and applications. this study scrutinizes the performance of a CNN-based word recognition system, evaluating accuracy and time variations with changes in hyperparameters, including the number of hidden layers, epoch size, activation function, and training-testing datasets. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Deep neural networks

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Lane Detection based on Generative Adversarial networks and Attention Mechanism 5

Lane Detection based on Generative Adversarial Networks and ...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Xu, Lu Guo, Xinyue Han, Xingyu Huang, Yuting Wang, Hui Fan, Zizhu East China Jiaotong University School of Science China

ISBN: (纸本)9798350325294

Lane detection plays a crucial role in the visual navigation system of intelligent vehicles. In lane detection, intelligent algorithms are capable of effectively processing images to accurately identify the desired objects. However, due to the complexity of real-world scenarios, objects to be detected in road images face challenges such as shape distortion, uneven illumination, shadow occlusion, and low resolution. Effectively extracting road features has become a research challenge. this study proposes a lane detection method based on generative adversarial networks (GANs) and attention mechanisms to address the severe imbalance between daytime and nighttime images in existing datasets. the method utilizes GANs for style transfer to augment the training dataset and designs a Coordinated Attention Detection Network (CADNet). By incorporating attention mechanisms into the residual modules of the backbone network, the model learns the correlation between feature channels, automatically calibrates attention in the channel dimension, and enhances the model's robustness and generalization ability. Experimental evaluations on mainstream datasets and comparisons with state-of-the-art algorithms demonstrate that the proposed method effectively mitigates the impact of lighting and shadows on image detection, enabling more accurate lane detection in complex scenarios involving occlusion and low light conditions. the method exhibits higher accuracy, lower false detection rate, and missing detection rate, while significantly improving feature extraction performance. © 2023 IEEE.

关键词： Feature extraction

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A novel Deep-Learning model for Human Activity Recognition based on Continuous Wavelet Transform 5

A novel Deep-Learning model for Human Activity Recognition b...

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5th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Pavliuk, Olena Mishchuk, Myroslav Silesian Tech Univ Ul Akad 2A PL-44100 Gliwice Poland Lviv Polytech Natl Univ Stepana Bandery St 12 UA-79000 Lvov Ukraine

Human Activity Recognition (HAR) has recently become in the spotlight of scientific research due to the development and proliferation of wearable sensors. HAR has found applications in such areas as digital health, mobile medicine, sports, abnormal activity detection and fall prevention. Neural networks have recently become a widespread method for dealing with HAR problems due to their ability automatically extract and select features from the raw sensor data. However, this approach requires extensive training datasets to perform sufficiently under diverse circumstances. this study proposes a novel Deep Learning - based model, pre-trained on the KU-HAR dataset. the raw, six-channel sensor data was preliminarily processed using the Continuous Wavelet Transform (CWT) for better performance. Nine popular Convolutional Neural Network (CNN) architectures, as well as different wavelets and scale values, were tested to choose the best-performing combination. the proposed model was tested on the whole UCI-HAPT dataset and its subset to assess how it performs on new activities and different amounts of training data. the results show that using the pre-trained model, especially with frozen layers, leads to improved performance, smoother gradient descent and faster training on small datasets. Additionally, the model performed on the KU-HAR dataset with a classification accuracy of 97.48% and F1-score of 97.52%, which is a competitive performance compared to other state-of-the-art HAR models.

关键词： Human activity recognition biomedical signal processing transfer learning continuous wavelet transform convolutional neural network

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Mathematical Modeling of an OFDM Communication System with Efficient Digital Signal processing 13

Mathematical Modeling of an OFDM Communication System with E...

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13th international conference on Advanced Computer Information Technologies, ACIT 2023

作者： Pyatin, Ilya Boiko, Juliy Eromenko, Oleksander Karpova, Lesya Polytechnic National University Khmelnytskyi Polytechnic Professional College by Lviv Department of Computer Engineering Khmelnytskyi Ukraine Khmelnytskyi National University Department of Telecommunications Media and Intelligent Technologies Khmelnytskyi Ukraine Khmelnytskyi National University Department of Physics and Electrical Engineering Khmelnytskyi Ukraine

the concept of Orthogonal Frequency-Division Multiplexing (OFDM) is a priority in wired and wireless information networks, mobile telecommunications of the 4th and 5th generations, modern digital television standards. Increasing the complexity of communication systems, expansion bandwidth requirements are important components of choosing the optimal characteristics signal converters for solving tasks and reducing the amount of Field-Programmable Gate Array (FPGA) hardware resources used. the article presents aspects mathematical modeling of a communication system with OFDM. the technique of modeling the main functional transformations in the Simulink environment and the study of the bit error rate is given. the researches of Hardware Description Language (HDL) implementation of Fast Fourier Transform (FFT) algorithms are presented. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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SDR Implementation of the Autocorrelation Based Spectrum Sensing Technique for Cognitive Radio networks 5th

SDR Implementation of the Autocorrelation Based Spectrum Sen...

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5th international conference on Electrical Engineering and Control Applications, ICEECA 2022

作者： Labsis, Lyes Teguig, Djamel Lassami, Nacerredine Signal Processing Laboratory Ecole Militaire Polytechnique Bordj El Bahri AlgiersBP17 Algeria Telecommunications Laboratory Ecole Militaire Polytechnique BP17 Algiers Bordj El Bahri Algeria

ISBN: (纸本)9789819747757

Spectrum sensing and dynamic spectrum access are the most important aspects of cognitive radios networks. the spectrum sensing techniques provide information about the utilization stat of frequency bands in a specific radio spectrum. According to this information, the dynamic spectrum access (DSA) orders the secondary users (unlicensed users) to use the frequency bands of primary/licensed users. the practice challenges of the spectrum sensing technique are the algorithm simplicity, the low sensing time, and the implementation possibility. this paper initially demonstrated the ability of correlation based spectrum sensing to detect the presence of the primary user's signal in poor reception conditions. the paper’s contribution shorts the study of the optimized detector parameters and the evaluation of its efficiency in simulation environments. then, an SDR implementation using RTL-SDR dangle and GNU Radio software platform has been realized to validate and present the autocorrelation based spectrum sensing effectiveness and limitations in a real scenario. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Autocorrelation function Cognitive radio networks Radio spectrum Spectrum sensing

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Estimating Acoustic Direction of Arrival Using a Single Structural sensor on a Resonant Surface 48

Estimating Acoustic Direction of Arrival Using a Single Stru...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Dipassio, Tre Heilemann, Michael C. thompson, Benjamin Bocko, Mark F. University of Rochester Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781728163277

the direction of arrival (DOA) of an acoustic source is a signal characteristic used by smart audio devices to enable signal enhancement algorithms. though DOA estimations are traditionally made using a multi-microphone array, we propose that the resonant modes of a surface excited by acoustic waves contain sufficient spatial information that DOA may be estimated using a singular structural vibration sensor. In this work, sensors are affixed to an acrylic panel and used to record acoustic noise signals at various angles of incidence. From these recordings, feature vectors containing the sums of the energies in the panel's isolated modal regions are extracted and used to train deep neural networks to estimate DOA. Experimental results show that when all 13 of the acrylic panel's isolated modal bands are utilized, the DOA of incident acoustic waves for a broadband noise signal may be estimated by a single structural sensor to within ±5° with a reliability of 98.4%. the size of the feature set may be reduced by eliminating the resonant modes that do not have strong spatial coupling to the incident acoustic wave. Reducing the feature set to the 7 modal bands that provide the most spatial information produces a reliability of 89.7% for DOA estimates within ±5° using a single sensor. © 2023 IEEE.

关键词： Audio Feature Extraction Direction of arrival (DOA) Structural sensors Vibration Sensing

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2022 IEEE 5th international conference on Electronics and Communication Engineering, ICECE 2022

2022 IEEE 5th International Conference on Electronics and Co...

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5th IEEE international conference on Electronics and Communication Engineering, ICECE 2022

ISBN: (纸本)9781665487894

the proceedings contain 48 papers. the topics discussed include: dynamic resource allocation for all-optical multicast in space multiplexed elastic optical networks;research on packet pilot multiplexing and encryption strategy in 5G MIMO system;an empirical model for 1-D molecular drift-diffusion communication channels under reflecting and absorbing boundary conditions;design of intelligent frequency hopping anti continuous change jamming system based on migration learning;configuration mechanism of software defined chip applied in signal processing;research on key technologies of satellite mobile communication system;design of a digital twin for spacecraft division network system;applications of capacity enlargement and traffic management control in hybrid satellite-terrestrial 5g networks;a coarse-grained reconfigurable array based ethernet reliable transmission design;mobile networks classification based on time-series clustering;and studies documenting the components associated with Instagram influence: a review of the scientific literature.

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Design of the Structure and Software Architecture of a Mobile Research Robot with Variable Geometry 6th

Design of the Structure and Software Architecture of a Mobil...

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6th international conference Mechatronics - Ideas for Industrial Applications

作者： Taube, Robert Kuzmenko, Dmytro Bonar, Bartlomiej Janos, Daniel Packo, Pawel Ortyl, Lukasz Kuras, Przemyslaw AGH Univ Krakow Fac Mech Engn & Robot Dept Robot & Mechatron Al Mickiewicza 30 PL-30059 Krakow Poland AGH Univ Krakow Fac Geodata Sci Geodesy & Environm Engn Dept Engn Surveying & Construct Al Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9783031634437;9783031634444

Devices used in Ground Penetrating Radar (GPR) technology vary in size depending on the purpose of the measurement to be carried out. the variety of devices, especially measurement antennas, poses a design challenge for the hardware and software side of a robotic mobile platform. this article presents the design of the robotic mobile platform tailored for GPR surveys. the robot's construction, featuring a variable-geometry frame, is detailed to accommodate different antenna sizes. Key components, including the drivetrain and signal processing system, are discussed, highlighting the use of differential drive and multilayered control architecture for autonomy and reliability. the software architecture encompasses data analysis and motion control, implemented through concurrent threads for modularity and fault tolerance. Additionally, a database-based approach is adopted for data storage and exchange, improving reliability and processing efficiency. Field tests validate the system's performance, demonstrating accurate positioning and highlighting the need for sensor fusion in indoor environments. the article concludes with insights for future improvements, emphasizing enhanced maneuverability and fault tolerance in the robot's design.

关键词： mobile robot GPR concurrent processing

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Exploration Of Lossy Posture Classification Model Using In-Bed Flexible Pressure sensors 5

Exploration Of Lossy Posture Classification Model Using In-B...

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5th IEEE international conference on Flexible and Printable sensors and Systems, FLEPS 2023

作者： Moon, Aekyeung Son, Seung Woo Kim, Minjun Chang, Seyun Park, Hyeji Etri Korea Republic of UMass Lowell United States MiDAS H&T Inc. Korea Republic of

ISBN: (纸本)9781665457330

Advances in flexible and printable sensor technologies have made it possible to use posture classification for providing timely services in digital healthcare, especially for bedsores or decubitus ulcers. However, managing a large amount of sensor data and ensuring accurate predictions can be challenging. While lossy compressors can reduce data volume, it is still unclear whether this would lead to losing important information and affect downstream application performance. In this paper, we propose LCDNN (Lossy Compression using Deep Neural Network) to reduce the size of sensor data and evaluate the performance of posture classification models. Our sensors, placed under hospital beds, have a thickness of just 0.4mm and collect pressure data from 28 sensors (7 by 4) at an 8 Hz cycle, categorizing postures into 4 types from 5 patients. Our evaluation, which includes reduced datasets by LCDNN, demonstrates that the results are promising. © 2023 IEEE.

关键词： Deep neural networks

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Proceedings - 2022 5th international conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

Proceedings - 2022 5th International Conference on Advanced ...

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5th international conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

ISBN: (纸本)9781665484749

the proceedings contain 165 papers. the topics discussed include: design of temperature monitoring system for space environment simulation and research infrastructure based on EPICS;multi-sensor fusion localization and mapping of indoor mobile robot;fusion localization based on accuracy estimation;research on sway detection method of subway train based on MEMS gyroscope technology;principle and application of tunnel magnetoresistance current sensor against direct-current influence;research on design of railway supervision and obstacle removal special vehicle based on function analysis method;design and realization of conductor for spacecraft based on single-chip microcomputer;poles estimation based on backward shift method;parallel convolutional neural network based on multi-band brain networks for EEG classification;mechanical properties of interconnection interfaces in micro tin-silver-copper solder joints;and abnormal electricity detection method based on multi-dimensional unsupervised learning.

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