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检索条件"任意字段=Conference on Wavelet Applications in Signal and Image Processing V"

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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...

引用

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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Automatic Filtreing of Quasy-Periodic Noise

Automatic Filtreing of Quasy-Periodic Noise

引用

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

Detection of Offline Handwritten Signature Forgery Using Inc...

引用

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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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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Comparative Study of Autoencoders Used for Noisy images Restoration

Comparative Study of Autoencoders Used for Noisy Images Rest...

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

作者： Y.S. Bekhtin P.E. Zhgutov Department of automatics and information technologies in control Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper is devoted to solving the problem of restoring noised images by convolutional autoencoders. It is provided a comparative research of three types of convolutional autoencoder architectures: the standard architecture based on convolution and deconvolution operations; the architecture based on Residual-blocks; the architecture based on Residual-blocks using a discriminator network. The effectiveness of the autoencoder architectures have been evaluated on synthetically noisy color images. Subjective and objective metrics as MSE, RMSE, PSNR, SSIM were used to evaluate the quality of the restored images. In the course of a comparative study, some recommendations were drawn on which of the explored autoencoders to choose providing the best effectiveness restoring the quality of distorted images.

关键词： Deconvolution Convolution Autoencoders Computer architecture Digital signal processing image restoration Computational efficiency Noise measurement Convolutional neural networks Standards

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： G.v. Thanuja Ramya S Dept. Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using vIvADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications.

关键词： image edge detection Computed tomography Software algorithms signal processing algorithms Computer architecture Table lookup IP networks Logic Field programmable gate arrays Kidney

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wavelet applications in signal and image processing v

Wavelet Applications in Signal and Image Processing V

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wavelet applications in signal and image processing v

The proceedings contain 56 papers. The topics discussed include: review of recent results on optimal orthonormal subband coders;comparison of wavelet image coding schemes for seismic data compression;image quality measurement using the Haar wavelet;lossless image compression using wavelets over finite rings and related architectures;on consistent signal reconstruction from wavelet extrema representation;seismic imaging in wavelet domain: decomposition and compression of imaging operator;application of differential mapping and wavelet transform;usage of short wavelets for scalable audio coding;enhanced resolution control for video sequences;regularized multiresolution methods for astronomical image enhancement;weighted time-frequency and time-scale transforms for non-stationary signal detection;and a wavelet detector for distributed objects.

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Innovative Approaches to Low-Cost Multiplier Design Using Approximate Computing and Clock Gating

Innovative Approaches to Low-Cost Multiplier Design Using Ap...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Nihar Buxy Shilpa D R Dept. of Electronics and Communication Eng. R.V. College of Engineering Bengaluru India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In this paper, we propose a novel design methodology for low-cost approximate radix-4 booth multipliers that significantly reduce energy consumption in error-tolerant signal processing applications. Unlike prior works that focus solely on either partial product generation (PPG) or partial product accumulation (PPA), our approach co-designs these units to balance approximation errors, resulting in an internal error mean that is nearly zero. This integration enables substantial energy savings while maintaining comparable accuracy to existing designs. To further enhance energy efficiency, we incorporate a Synthesized-based clock gating technique, reducing unnecessary switching activity in the design. Experimental evaluations on Finite Impulse Response (FIR) filtering and image classification tasks demonstrate that the proposed multiplier achieves up to 39.3% reduction in total power consumption while maintaining accuracy within an acceptable range for error-tolerant applications. Additionally, delay and area utilisation are reduced by approximately 15.18% and 8.26%, respectively, compared to the existing method. Compared to traditional accurate booth multipliers, our design provides a better trade-off between power efficiency and computational precision, making it a promising alternative for energy-constrained digital signal processing (DSP) systems.

关键词： Performance evaluation Accuracy Power demand Approximate computing Digital signal processing Switches Energy efficiency Delays Clocks image classification

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Broken Rotor Bar Fault Diagnosis Using Spectrum Distortion for Data Augmentation

Broken Rotor Bar Fault Diagnosis Using Spectrum Distortion f...

引用

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

作者： Alexander L. Shestakov Dmitrii v. Galyshev victoria A. Eremeeva Olga L. Ibryaeva laboratory of self-diagnostics and self-control of devices and systems South Ural State University Chelyabinsk Russia dept. of Applied Mathematics and Programming South Ural State University Chelyabinsk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The problem of diagnostics of broken rotor bars in an induction (IM) motor based on its current signals is considered. The proposed fault detection technique is based on filtering the current signal in the region around the frequency 7f, where f is the power supply frequency, and further obtaining its envelope using the Hilbert Transform. Scalograms are constructed from the envelopes using a Continuous wavelet Transform and form a training dataset for the Convolutional Neural Network (CNN) classifier. In real industrial conditions, we have a lack of defective data which is simulated in this paper. It is shown that a CNN model trained on normal data obtained under different motor operating conditions and defective data obtained under one operating mode will poorly detect a defect when changing the operating mode. The reason for this lies in the characteristic defect frequencies change with the change in the operating mode. A data augmentation method based on spectrum distortion is proposed to solve this problem. We distort the spectrum of a real defective signal taking into account its structure and create synthetic data that simulate missing defective signals with different operating conditions. The paper presents the experimental rig and dataset, and shows that the spectral distortion-based augmentation technique significantly improves the performance of the CNN model under conditions of insufficient defective training data.

关键词： Training Power supplies Rotors Training data Distortion Data augmentation Data models Convolutional neural networks Bars Synthetic data

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StableTalk: Advancing Audio-to-Talking Face Generation with Stable Diffusion and vision Transformer 27th

StableTalk: Advancing Audio-to-Talking Face Generation with...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Nazarieh, Fatemeh Kittler, Josef Rana, Muhammad Awais Kanojia, Diptesh Feng, Zhenhua School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Institute for People-Centred AI University of Surrey Guildford United Kingdom School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9783031781711

Audio-to-talking face generation stands at the forefront of advancements in generative AI. It bridges the gap between audio and visual representations by generating synchronized and realistic talking faces. Despite recent progress, the lack of realism in animated faces, asynchronous audio-lip movements, and computational burden remain key barriers to practical applications. To address these challenges, we introduce a novel approach, StableTalk, leveraging the emerging capabilities of Stable diffusion models and vision Transformers for Talking face generation. We also integrate the Re-attention mechanism and adversarial loss to improve the consistency of facial animations and synchronization with a given audio input. More importantly, the computational efficiency of our method has been notably enhanced by optimizing operations within the latent space and dynamically adjusting the focus on different parts of the visual content based on the provided conditions. Our experimental results demonstrate the superiority of StableTalk over the existing approaches in image quality, audio-lip synchronization, and computational efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Animation

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