Detection of the third and fourth heart sounds using Hilbert-Huang transform

<p>Abstract</p> <p>Background</p> <p>The third and fourth heart sound (S3 and S4) are two abnormal heart sound components which are proved to be indicators of heart failure during diastolic period. The combination of using diastolic heart sounds with the standard ECG as a measurement of ventricular dysfunction may improve the noninvasive diagnosis and early detection of myocardial ischemia.</p> <p>Methods</p> <p>In this paper, an adaptive method based on time-frequency analysis is proposed to detect the presence of S3 and S4. Heart sound signals during diastolic periods were analyzed with Hilbert-Huang Transform (HHT). A discrete plot of maximal instantaneous frequency and its amplitude was generated and clustered. S3 and S4 were recognized by the clustered points, and performance of the method was further enhanced by period definition and iteration tracking.</p> <p>Results</p> <p>Using the proposed method, S3 and S4 could be detected adaptively in a same method. 90.3% of heart sound cycles with S3 were detected using our method, 9.6% were missed, and 9.6% were false positive. 94% of S4 were detected using our method, 5.5% were missed, and 16% were false positive.</p> <p>Conclusions</p> <p>The proposed method is adaptive for detecting low-amplitude and low-frequency S3 and S4 simultaneously compared with previous detection methods, which would be practical in primary care.</p

Cogging Torque Reduction of Interior Permanent-Magnet Synchronous Motors by Finite-Element Method

The cogging torque of a permanent-magnet motor is an oscillatory torque that always induces vibration, acoustic noise, possible resonance and speed ripples, and its minimization is a major concern for electric motor designers. This paper presents an effective approach for the cogging torque reduction of interior permanent-magnet motors by modifying the magnet span angle of the rotor and the shoe depth and shoe ramp of the stator. The cogging torque is calculated by employing a commercial finite-element analysis software Ansoft/Maxwell. The results show that the peak value of the cogging torque for the modified design decreases 50% in comparison with that of the original design

Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.Comment: CVPR 2023 camera-ready versio

Lightly Weighted Automatic Audio Parameter Extraction for the Quality Assessment of Consensus Auditory-Perceptual Evaluation of Voice

The Consensus Auditory-Perceptual Evaluation of Voice is a widely employed tool in clinical voice quality assessment that is significant for streaming communication among clinical professionals and benchmarking for the determination of further treatment. Currently, because the assessment relies on experienced clinicians, it tends to be inconsistent, and thus, difficult to standardize. To address this problem, we propose to leverage lightly weighted automatic audio parameter extraction, to increase the clinical relevance, reduce the complexity, and enhance the interpretability of voice quality assessment. The proposed method utilizes age, sex, and five audio parameters: jitter, absolute jitter, shimmer, harmonic-to-noise ratio (HNR), and zero crossing. A classical machine learning approach is employed. The result reveals that our approach performs similar to state-of-the-art (SOTA) methods, and outperforms the latent representation obtained by using popular audio pre-trained models. This approach provide insights into the feasibility of different feature extraction approaches for voice evaluation. Audio parameters such as jitter and the HNR are proven to be suitable for characterizing voice quality attributes, such as roughness and strain. Conversely, pre-trained models exhibit limitations in effectively addressing noise-related scorings. This study contributes toward more comprehensive and precise voice quality evaluations, achieved by a comprehensively exploring diverse assessment methodologies.Comment: Published in IEEE 42th International Conference on Consumer Electronics (ICCE 2024

An Empirical Study of Factors Influencing the Intention to Use SNS App─The Case of Facebook

Mobile Internet is coming. The social networking site application (SNS app) has become an important portal for users accessing social networking services. Based on the point of view of existing social network users, this study integrates the technology value-based adoption model and social influence to propose a framework to investigate factors influencing the use intention of the SNS app. A sample of 223 subjects surveyed from Facebook, it was found that user’s perceived value regarding the SNS app positively affects the use intention of the social networking app. Usefulness and Technicality provided by SNS app positively affects perceived value of the app. In addition, user perceived social influence also positively affects the use intention of the social networking app. Results not only advance knowledge related to social network research, as well as provide practical advice to social networking companies. They also suggest how to attract users to continually participate in social networks. Increase activeness and stickiness is critical for social network companies to facilitate long-term development