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Assessment of Developmental Dysgraphia Utilising a Display Tablet 21st

Assessment of Developmental Dysgraphia Utilising a Display...

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The 21st international Graphonomics Society conference, IGS 2023

作者： Mekyska, Jiri Galaz, Zoltan Safarova, Katarina Zvoncak, Vojtech Cunek, Lukas Urbanek, Tomas Havigerova, Jana Marie Bednarova, Jirina Mucha, Ján Gavenciak, Michal Smekal, Zdenek Faundez-Zanuy, Marcos Department of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Research Methodology Institute of Psychology The Czech Academy of Sciences Brno Czech Republic Tecnocampus Universitat Pompeu Fabra Barcelona Mataro Spain

ISBN: (纸本)9783031454608

Even though the computerised assessment of developmental dysgraphia (DD) based on online handwriting processing has increasing popularity, most of the solutions are based on a setup, where a child writes on a paper fixed to a digitizing tablet that is connected to a computer. Although this approach enables the standard way of writing using an inking pen, it is difficult to be administered by children themselves. The main goal of this study is thus to explore, whether the quantitative analysis of online handwriting recorded via a display/screen tablet could sufficiently support the assessment of DD as well. For the purpose of this study, we enrolled 144 children (attending the 3rd and 4th class of a primary school), whose handwriting proficiency was assessed by a special education counsellor, and who assessed themselves by the Handwriting Proficiency Screening Questionnaires for Children (HPSQ–C). Using machine learning models based on a gradient-boosting algorithm, we were able to support the DD diagnosis with up to 83.6% accuracy. The HPSQ–C total score was estimated with a minimum error equal to 10.34%. Children with DD spent significantly higher time in-air, they had a higher number of pen elevations, a bigger height of on-surface strokes, a lower in-air tempo, and a higher variation in the angular velocity. Although this study shows a promising impact of DD assessment via display tablets, it also accents the fact that modelling of subjective scores is challenging and a complex and data-driven quantification of DD manifestations is needed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： machine learning

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IPMV 2021 - 2021 3rd international conference on Image processing and machine Vision

IPMV 2021 - 2021 3rd International Conference on Image Proce...

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3rd international conference on Image processing and machine Vision, IPMV 2021

ISBN: (纸本)9781450390040

The proceedings contain 15 papers. The topics discussed include: a real-time single-shot multi-face detection, landmark localization, and gender classification;an automatic calibration method for kerf angle in wafer automated optical inspection;two stream pose guided network for vehicle re-identification;a multilevel thresholding approach for acne detection in medical treatment;research on UAV signal classification algorithm based on deep learning;towards improving car point cloud tracking via detection updates;robust computation of trifocal tensor based on hybrid particle swarm optimization;encoder decoder based neural network for perspective estimation;reducing the annotation cost of whole slide histology images using active learning;clothing image retrieval based on parts detection and segmentation;deep CNN for classification of image contents;comparison of feature selection methods on arrhythmia dataset;and computation offloading for better real time technical market analysis on mobile devices.

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Empirical Wavelet Transform Grounded Poignant Ground Target Recognition and Classification by Seismic signal processing 1

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Mittal, Aman Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Humans have had the ability to recognise items for hundreds of years, possibly even since they first appeared on Earth. Humans use their senses of sight, smell, hearing, taste, and touch to determine the identities of things in their immediate surroundings. Transmission of sensory organ impulses to the brain allows for the latter's processing and interpretation of the data. That's why repetition is so important for learning. The learned knowledge is put to use in a wide variety of ways, from the mundane to the crucial, such as in the areas of security, surveillance, traffic monitoring, etc. Due to the short range of the human senses and the potential dangers of working in some environments, this approach has spatial and temporal constraints. Seismic signal processing employs the wavelet transform for detection and classification. However, picking the mother wavelet is tricky because it depends on how well it fits the original signal. As a result, the empirical wavelet transform (EWT) is investigated in this research paper as a potential adaptive wavelet transform for detection and classification. In the same way as EWT does, the mother wavelet is chosen mechanically with reference to the input signal. This study shows that EWT-based detection and classification methods perform better than STFT-based methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Seismic response

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Exploration of Pattern Recognition Methods for Motor Imagery EEG signal with Convolutional Neural Network Approach 3

Exploration of Pattern Recognition Methods for Motor Imagery...

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3rd international conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Zahra, Hanina N Zakaria, Hasballah Hermanto, Beni R School of Electrical Engineering and Informatics Bandung Institute of Technology West Java Bandung40132 Indonesia

As an application of EEG, Motor Imagery based Brain-Computer Interface (MI BCI) plays a significant role in assisting patients with disability to communicate with their environment. MI BCI could now be realized through various methods such as machine learning. Many attempts using different machine learning approaches as MI BCI applications have been done with every one of them yielding various results. While some attempts managed to achieve agreeable results, some still failed. This failure may be caused by the separation of the feature extraction and classification steps, as this may lead to the loss of information which in turn causes lower classification accuracy. This problem can be solved by integrating feature extraction and classification by harnessing a classification algorithm that processed the input data as a whole until it produces the prediction, hence the use of convolutional neural network (CNN) approach which is known for its versatility in processing and classifying data all in one go. In this study, the CNN exploration involved a task to classify 5 different classes of fingers' imaginary movement (thumb, index, middle, ring, and pinky) based on the processed raw signal provided. The CNN performance was observed for both non-augmented and augmented data with the data augmentation techniques used include sliding window, noise addition, and the combination of those two methods. From these experiments, the results show that the CNN model managed to achieve an averaged accuracy of 47%, meanwhile with the help of augmentation techniques of sliding window, noise addition, and the combined methods, the model achieved even higher averaged accuracy of 57,1%, 47,2%, and 57,5% respectively. © Published under licence by IOP Publishing Ltd.

关键词： Brain computer interface

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Biomedical signal processing using Matlab and Open Source R Software - A Comparative Study on Running Times 3

Biomedical Signal Processing using Matlab and Open Source R ...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Balaji, M. Sundar Prakash Sivaraju, S.S. Britto, K.R. Aravind Fairooz, Shaik Easwaran, M. Elamaran, V. Rvs College of Engineering and Technology Department of Eee Coimbatore India Psna College of Engineering and Technology Department of Ece Dindigul India Malla Reddy Engineering College Department of Ece Hyderabad India School of Eee Sastra Deemed University Department of Ece Thanjavur India

ISBN: (纸本)9780738146270

In recent years, due to the enormous growth in computer power, memory, storage, and performance, even personal computers are being used to operate a broad range of complicated functions with extraordinary accuracy. Also, with the aid of supercomputers or high-end servers, the most wanted techniques such as machine learning, deep learning, advanced signal processing, and artificial intelligence can be executed quickly that too with higher accuracy. This article focuses mainly on computing the running times of platforms such as Matlab and R open-source software. For better comparison, functions such as dc-bias removal, a 1024-point fast Fourier transform, filtering using median, and moving average filters are considered. This study may become an excellent guide for beginners who are working with R open-source software. For many functions, the Matlab platform provides better performance (lower running times) than R software. The Matlab 9.4 tool and RStudio are used experimental simulations for specific tasks to compare the running times. © 2021 IEEE.

关键词： Open source software

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Integrated EMD-Enhanced Analysis and Neural Network Classification for Robust processing of Motor Imagery EEG signals

Integrated EMD-Enhanced Analysis and Neural Network Classifi...

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3rd international conference on machine learning and Data Engineering, ICMLDE 2024

作者： Kumari, Annu Edlaa, Damodar Reddy Department of Computer Science and Engineering National Institute of Technology Goa Goa Cuncolim403703 India

The rapid evolution of deep learning in the current era has prompted extensive exploration in the realm of Brain-Computer Interface (BCI). Despite the substantial progress made in enhancing the accuracy of BCI systems employing Motor Imagery (MI) through deep learning techniques, especially when compared to certain traditional algorithms, there remains a substantial challenge in clearly interpreting these sophisticated models. This holds particular importance due to the labor-intensive and costly nature of the data collection process. This challenge is addressed in our manuscript through the introduction of a unique methodology to improve the effectiveness and precision of BCI systems using EEG data. The outlined methodology comprises five fundamental steps. The signal pre-processing begins with band-pass filtering, succeeded by Empirical Mode Decomposition (EMD) to capture underlying oscillatory components. Subsequently, correlation calculation is conducted for all the Intrinsic Mode Functions (IMFs) with the original signal. The IMF exhibiting the highest correlation factor is selected for feature extraction, and classification is executed using the adaptive Graph Convolutional Network (GCN). It plays a pivotal role in feature extraction, leveraging its capabilities to capture intricate patterns within EEG signals. Implemented in Python, this model, achieved an accuracy level of 91.3%, underscoring its significance in augmenting the overall functionality of BCI systems based on EEG data. © 2025 The Author(s).

关键词： Empirical mode decomposition

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FPGA processing of Decision Tree Ensembles Stored in External DRAM

FPGA Processing of Decision Tree Ensembles Stored in Externa...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Jakob Winkler Michael Lunglmayr Institute of Signal Processing Johannes Kepler University Linz Linz Austria

Ensemble methods are an interesting alternative to neural networks, especially due to their inherent parallelization capabilities. Forming ensembles from decision trees (e.g. in so-called random forests) has the additional advantage that processing a decision tree can be performed with low-complexity operations. When compared to other machine learning approaches such as (deep) neural networks, tree-based ensemble methods can deliver similar accuracy while being considerably simpler in terms of implementation effort and computational complexity. A major challenge for implementation in resource-constrained hardware such as FPGAs are the memory requirements of decision trees. While on-chip memories on current FPGAs are typically very fast, the available space is often not enough for complex problems, leading to a need to integrate external dynamic memories into the system architecture. In this paper, we propose a hardware architecture and an accompanying subtree-based memory layout that considers the inherent problems of decision trees and the memories’ physical latency constraints to speed up the processing of decision tree ensembles by increasing the bandwidth efficiency of memory transfers. We show that this can increase the processing speed by up to 3.38 times when compared to a naive (linear) memory layout for a single tree and 3.72 in a parallel scenario. This way it enables efficiently implementing tree-based ensemble learners for practical problem sizes on FPGAs.

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A Spectral Clustering-Based Pilot Assignment Method for Cell-Free Massive MIMO

A Spectral Clustering-Based Pilot Assignment Method for Cell...

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international conference on Electronics and Information Technology (EIT)

作者： Yu Zhao Fengming Zhang Yangjun Gao Zhongliang Zhou Pengtao Zhang Air Force Engineering University Xi’an China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

This paper studies pilot assignment (PA) in cell-free (CF) massive multiple-input multiple-output (MIMO). PA is a key issue to facilitate signal processing, e.g. beamforming, by providing reliable channel estimation at access points (APs). Most existing methods face intractable local optima problem, which degrades the channel estimation accuracy. Moreover, these methods usually encounter the unaffordable complexity. To this end, we propose a low-complexity PA method based on the spectral clustering (SC), which is named as SC-based PA method. SC is a popular machine learning method for clustering problem in image segmentation, bioinformatics, etc. It leverages the eigenvectors of a similarity matrix to map the data samples to a low-dimensional space for easy clustering. By assigning the orthogonal pilot sequences to the same-cluster user equipments (UEs), the proposed SC-based PA method properly degrades the pilot contamination between the UEs. To verify the performance of the proposed method, we show by simulations that the proposed SC-based PA method outperforms the existing methods in both channel estimation accuracy and execution time.

关键词： Image segmentation Accuracy Laplace equations Channel estimation Massive MIMO machine learning Euclidean distance Reliability Information technology Faces

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Fault Detection in Induction Motors: Recent Advances and Future Prospects

Fault Detection in Induction Motors: Recent Advances and Fut...

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Informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Sarvarbek Ruzimov Zhang Jiangzhong Sherkhon Latipov Muhammad Aziz Shahzad School of Electrical Engineering Southeast University Nanjing China Department of Power plants networks and systems Tashkent State Technical University Tashkent Uzbekistan

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The induction motor is the most prevalent type of electrical equipment utilized in residential, commercial, and industrial settings. Because of electric motor failures in industry, unplanned downtime, lost output, increased costs of operation, and lost revenues are common. It is required to guarantee the reliable functioning of these motors, which calls for the use of strong fault detection approaches. This study provides a thorough overview of fault detection strategies for induction motors through a comprehensive assessment of the literature. The goal is to compile and integrate knowledge from previous studies to ensure that practitioners, engineers, and academics have a comprehensive understanding of the current state of fault detection in induction motors. In the paper, the first section covers the introductory ideas behind induction motor fault detection, such as the kinds of problems that are repeatedly found and how they affect motor performance. Also, a thorough examination of the present fault detection methods is provided. A critical analysis is conducted to evaluate the advantages and disadvantages of common monitoring signals. The association of cutting-edge technology in fault detection for induction motors, such as machine learning and signal processing techniques, is covered in the literature study. Recent developments in the subject are summed up, and an evaluation of how well these new techniques work to enhance fault detection accuracy and flexibility is given. A discussion of new developments, possible avenues for future research, and areas where defect detection methods for induction motors could be enhanced are covered in the review's conclusion.

关键词： Fault diagnosis Knowledge engineering Induction motors Fault detection Rotors machine learning Stators signal processing Informatics Monitoring

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Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection

Time-Weighted Frequency Domain Audio Representation with GMM...

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

作者： Jian Guan Youde Liu Qiaoxi Zhu Tieran Zheng Jiqing Han Wenwu Wang Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University China School of Computer Science and Technology Harbin Institute of Technology China Centre for Audio Acoustics and Vibration University of Technology Sydney Australia Centre for Vision Speech and Signal Processing University of Surrey UK

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram’s average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2.

关键词： Measurement Deep learning Visualization Time-frequency analysis signal processing Complexity theory Task analysis

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