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A Study of the Accuracy of Electromyographic Differential Diagnostics of Patients with Parkinson's Disease and Essential Tremor in various Postures of the Patient

A Study of the Accuracy of Electromyographic Differential Di...

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Digital signal processing and its applications (DSPA), International Conference on

作者： Olga S. Sushkova Alexei A. Morozov Alexandra v. Gabova Larisa A. Chigaleichick Alexei v. Karabanov Kotelnikov IRE RAS Moscow Russia IHNA and NPh RAS Moscow Russia FSBI “Research Center of Neurology” Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The accuracy of electromyographic differential diagnostics of essential tremor and Parkinson's disease in various patient postures was studied. To assess the accuracy of differential diagnostics, a mathematical method was developed on the basis of the analysis of wave trains of the electrical activity of antagonist muscles. To compare the accuracy of differential diagnostic methods, we used a computational experiment. Patients with the first stage of Parkinson's disease with a tremor in the left arm (14 subjects) and right arm (14 subjects), as well as patients with essential tremor (14 subjects), were studied. A relaxed posture and posture with arms extended forward were compared. The results of comparing the relaxed posture and the posture with outstretched arms demonstrated that differential diagnostics should be performed in the relaxed posture of the patient regardless of which side of the patient's body tremor is observed. If diagnostics in the relaxed posture is impossible due to the fact that the patient does not have resting tremor, it is advisable to use the posture with outstretched arms.

关键词： Training Drilling Accuracy Continuous wavelet transforms Parkinson's disease Digital signal processing Muscles Solids Principal component analysis

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Reducing the complexity of distributed video coding by improving the image enhancement post processing

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signal processing: image Communication 2025年 138卷

作者： Boudechiche, Djamel Eddine Benierbah, Said D. E. Boudechiche is with SP_Lab and electronics dept. University of Mohamed El Bachir El Ibrahimi Bordj Bou Arreridj34000 Algeria S. Benierbah is with SP_Lab and electronics dept. Mentouri University of Constantine Algeria

The main attractive feature of distributed video coding (DvC) is its use of low-complexity encoders, which are required by low-resource networked applications. Unfortunately, the performance of the currently proposed DvC systems is not yet convincing, and further improvements in the rate, distortion, and complexity tradeoff of DvC are necessary to make it more attractive for use in practical applications. This requires finding new ways to exploit side information in reducing the transmitted rate and improving the quality of the decoded frames. This paper proposes improving DvC by exploiting image enhancement post-processing at the decoder. In this way, we can either improve the quality of the decoded frames for a given rate or reduce the number of transmitted bits for the same quality and hence reduce the complexity of the encoder. To do this, we used a conditional generative adversarial network (cGAN) to restore more of the details discarded by quantization, with the help of side information. We also evaluated numerous existing deep learning-based enhancement methods for DvC and compared them to our proposed model. The results show a reduction in the number of DvC coding operations by 46 % and an improvement in rate-distortion performance and subjective visual quality. Furthermore, despite reducing its complexity, our DvC codec outperformed the DISCOvER codec with an average Bjøntegaard PSNR of 0.925 dB. © 2025 Elsevier B.v.

关键词： image enhancement

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wavelet domain partition -based signal processing with applications to image denoising and compression

Wavelet domain partition -based signal processing with appli...

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作者： Kim, Il-Ryeol University of Delaware

学位级别：Ph.D.

This dissertation addresses the problems of image denoising and compression. image denoising and compression are the fundamental problems in image processing, and often use transform techniques. In this dissertation, the wavelet transform is used and new signal processing techniques are applied in the wavelet domain. The sparsity of the wavelet transform, i.e., a signal energy is concentrated on few large magnitude coefficients, is a main property exploited by wavelet based image processing applications. The new methods for image denoising problem introduced in this dissertation exploits the directional structure of the two-dimensional wavelet transform. For image compression, correlations between adjacent scales (levels) and coefficient pixels, i.e., inter and intra-correlation, are adaptively exploited in the proposed algorithm. The most frequently used technique in image denoising problems is a thresholding operation. Thresholding the wavelet coefficients exploits the sparse property of the wavelet transform. Applying thresholding to all coefficients uniformly, however, produces oversmoothing of edges and undersmoothing in uniform regions. Recently, adaptive wavelet thresholding utilizing spatial and adjacent scale correlations has been shown to yield good results. This dissertation introduces a related, but more direct, technique for adaptively processing wavelet coefficients based on partitioning of the coefficient space. In the wavelet domain, the coefficient space is partitioned through vector quantization and mask functions are used to obtain the denoised wavelet coefficients. This approach is better able to exploit structure in the coefficient domain and presented simulations show that the proposed technique yields superior performance compared with current wavelet denoising methods. A new embedded wavelet image compression method, using quad-partition-based wavelet domain image compression, is also proposed. The introduced method uses a quad-partition-base

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Industrial applications of the wavelet and multiresolution based signal-image processing, a review

Industrial applications of the wavelet and multiresolution b...

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8th International Conference on Quality Control by Artificial vision

作者： Truchetet, Fred Laligant, Olivier Univ Bourgogne CNRS Le2i UMR 5158 F-21004 Dijon France

ISBN: (纸本)9780819464514

25 years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space frequency or space scale approaches are considered standard tools by researchers in image processing, and many applications have been proposed that point out the interest of these techniques. This paper proposes a review of the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. More than 180 recent papers are presented.

关键词： wavelets multiresolution multiscale signal processing image processing industrial applications

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A Micro-Topography Enhancement Method for DEMs: Advancing Geological Hazard Identification

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REMOTE SENSING 2025年第5期17卷 920-920页

作者： He, Qiulin Dong, Xiujun Li, Haoliang Deng, Bo Sima, Jingsong Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA's effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA's potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments.

关键词： digital elevation model LiDAR micro-topographical features vision enhancement wavelet decomposition edge detection geological hazard identification

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Automatic Filtreing of Quasy-Periodic Noise

Automatic Filtreing of Quasy-Periodic Noise

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Digital signal processing and its applications (DSPA), International Conference on

作者： Anatoly Novikov vladimir Ovchinnikov Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The problem of filtering periodic noise in digital images in an automatic mode has been addressed by scientists in many countries. Several alternative methods have been proposed. Currently, approaches to solving the problem of quasi-periodic noise filtering in automatic mode are actively being investigated. This type of noise is more common in real images. This paper briefly discusses the methods that best meet the criteria for quasi-periodic noise filtering quality and computational complexity requirements. Additionally, a formalized description of the iterative method proposed by the authors is provided, along with the results of experimental studies comparing the proposed method with other approaches.

关键词： Fourier transforms Filtering Digital images Noise Noise reduction signal processing algorithms Digital signal processing Iterative algorithms image restoration Computational complexity

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Acoustic signal detection of search-phase echolocation bat calls using hybrid classification model

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Expert Systems with applications 2025年 284卷

作者： Ramakrishna, Anala Maddur Ramakrishna, Hemavathy Puttaswamaiah, Rajesh Department of Information Science & Engineering R V College of Engineering Mysuru Road Karnataka Bengaluru560059 India Department of Computer Science & Engineering R V College of Engineering Mysuru Road Karnataka Bengaluru560059 India Bat Conservation India Trust Kadabagere Magadi Road Karnataka Bengaluru562130 India

Passive acoustic sensing has emerged as an effective method for measuring the biodiversity of bat species. Accurate, dependable and open-source technologies, which enable the classification and the detection of bat calls from massive sets of audio recordings are urgently needed to improve the understanding of bat population dynamics. The majority of currently available methods are either commercial or they have been designed with the objective of species categorization in view, omitting the crucial issue of localizing the echolocation calls in the audio among the noisy recordings. Hence, this paper develops an Acoustic signal Detection of Search-Phase Echolocation Bat Calls (ASD-SPEBC), which includes preprocessing, feature extraction and hybrid classification as its major steps. In the preprocessing stage, wavelet de-noising and Fourier Transform (FT) analysis are done to preprocess the input signal. Subsequently, wavelet Transform (WT), Modified S-Transform, Correlation Dimension and Common Spatial Pattern (CSP) based features are extracted in the feature extraction stage. Finally, Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (Bi-GRU) classifiers are combined to form the hybrid model for classification purposes. The efficiency of the hybrid model is ensured with a novel optimal training algorithm, termed as ASOA (Addition and Subtraction-assisted Optimization Algorithm), that optimally-tunes the weights. Eventually, the performance of the proposed work is compared and validated over the conventional methods to confirm its supremacy. © 2025 Elsevier Ltd

关键词： image segmentation

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Detection of Offline Handwritten Signature Forgery Using Inceptionv4 with Sobel Edge Detection

Detection of Offline Handwritten Signature Forgery Using Inc...

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Digital signal processing and its applications (DSPA), International Conference on

作者： Ericka F. Rudico John Mar Y. Armocilla Meo vincent C. Caya School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Handwritten signature forgery detection is essential for authenticating signatures to address the risks of document falsification, identity theft, and financial fraud from signature forgeries. This study presents a reliable approach to accurately classify genuine and forged signatures using deep learning and image pre-processing techniques. Specifically, the proposed method utilizes Inceptionv4 with Sobel edge detection. Inceptionv4, a deep convolutional neural network, is used to extract features from signature images, while Sobel edge detection enhances these features. By applying Sobel edge detection during the image pre-processing stage, we emphasize the edges of the signatures, which aids in the classification process. Furthermore, we implemented data augmentation techniques to effectively generate additional datasets, overcoming the significant limitations of the lack of custom signature datasets. By integrating Inception v4’s capabilities and Sobel edge detection, the model achieved a high accuracy of 96.19% and an F1-score of 96.18%. These results demonstrate the potential and effectiveness of the proposed approach in detecting signature forgeries.

关键词： Deep learning image edge detection Identity theft Finance Digital signal processing Feature extraction Data augmentation Forgery Reliability Convolutional neural networks

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Study of 0.1-Hz vasomotion in microcirculation under local heating by means of imaging photoplethysmography

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BIOMEDICAL signal processing AND CONTROL 2025年 100卷

作者： Mizeva, Irina A. Podolyan, Natalia P. Mamontov, Oleg, v Sakovskaia, Anastasiia, v Kamshilin, Alexei A. Russian Acad Sci Inst Continuous Media Mech Ural Branch Perm Russia Russian Acad Sci Far East Branch Inst Automat & Control Proc Lab New Funct Mat Photon Vladivostok Russia Almazov Natl Med Res Ctr Dept Circulat Physiol St Petersburg Russia Pavlov First St Petersburg State Med Univ Dept Dept Therapy St Petersburg Russia Pacific State Med Univ Inst Therapy & Instrumental Diagnost Vladivostok Russia

Low-frequency oscillations in the human circulatory system is important for basic physiology and practical applications in clinical medicine. Our objective was to study which mechanism (central or local) is responsible for changes in blood flow fluctuations at around 0.1 Hz. We used the method of imaging photoplethysmography synchronized with electrocardiography to measure blood-flow response to local forearm heating of 18 healthy male volunteers. The dynamics of peripheral perfusion was revealed by a correlation processing of photoplethysmography data, and the central hemodynamics was assessed from the electrocardiogram. wavelet analysis was used to estimate the dynamics of spectral components. Our results show that skin heating leads to multiple increase in local perfusion accompanied by drop in blood flow oscillations at 0.1 Hz, whereas no changes in heart rate variability was observed. After switching off the heating, perfusion remains at the high level, regardless decrease in skin temperature. The 0.1 Hz oscillations are smoothly recovered to the base level. In conclusion, we confirm the local nature of fluctuations in peripheral blood flow in the frequency band of about 0.1 Hz. A significant, but time-delayed, recovery of fluctuation energy in this frequency range after cessation of the skin warming was discovered. This study reveals a novel factor involved in the regulation microcirculatory vascular tone. A comprehensive study of hemodynamics using the new technique of imaging photoplethysmography synchronized with electrocardiography is a prerequisite for development of a valuable diagnostic tool.

关键词： Microcirculation Cardiovascular oscillations vascular tone regulation Imaging photoplethysmography

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Identical Human Preference Alignment Paradigm for Text-to-image Models

Identical Human Preference Alignment Paradigm for Text-to-Im...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoyuan Sun Bo Xia Yifei Zhao Yongzhe Chang Xueqian Wang Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Implicit reward mechanism of Direct Preference Optimization (DPO) has facilitated its recent applications beyond large language models (LLMs), notably in aligning text-to-image models with human preferences. While promising results have been achieved with algorithms such as Diffusion-DPO, their reliance on the assumptions of the Bradley-Terry model could potentially lead to significant overfitting. In this paper, we propose the Step Identical Preference Alignment (SIPA) method, departing text-to-image alignment from the assumptions of Bradley-Terry preference model. We assess the performance of four models, Diffusion-DPO, SPO, and SIPA, alongside the original model, on the HPS-v2 test set, which focus on three key aspects: text-image alignment, human value alignment, and generation diversity. Experimental results show that SIPA matches or outperforms existing SOTA alignment methods, and even exceeds the original model in terms of generation diversity, which compellingly demonstrates SIPA’s superiority in mitigating alignment overfitting.

关键词： Large language models Text to image signal processing algorithms signal processing Acoustics Speech processing Optimization Overfitting

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