Hasil Pencarian  ::  Simpan CSV :: Kembali

"This book provides an overview of computational approaches to medical image examination and analysis in oral radiology utilizing dental radiograph to detect and diagnose dental caries in cases of decayed teeth. Coverage includes basic image processing techniques; approaches for Region of Interest extraction and analysis; and the role of computational clustering techniques for segmentation of teeth and dental caries. The book also presents a novel multiphase level set method for automatic segmentation of dental radiographs."

"This book presents the overall vision and research outcomes of Nano-Tera.ch, which is a landmark Swiss federal program to advance engineering system and device technologies with applications to Health and the Environment, including smart Energy generation and consumption. The authors discuss this unprecedented nation-wide program, with a lifetime of almost 10 years and a public funding of more than 120 MCHF, which helped to position Switzerland at the forefront of the research on multi-scale engineering of complex systems and networks, and strongly impacted the Swiss landscape in Engineering Sciences. "

"This book presents fascinating, state-of-the-art research findings in the field of signal and image processing. It includes conference papers covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. It addresses various types of signals, such as image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals; MRI, PET and CT scan images; THz signals; solar wind speed signals (SWS); and photoplethysmogram (PPG) signals, and demonstrates how new paradigms of intelligent computing, like quantum computing, can be applied to process and analyze signals precisely and effectively. The book also discusses applications of hybrid methods, algorithms and image filters, which are proving to be better than the individual techniques or algorithms."

"This book contains interesting findings of some state-of-the-art research in the field of signal and image processing. It contains twenty one chapters covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. Various types of signals including image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals, MRI, PET and CT scan images, THz signals, solar wind speed signals (SWS) and photoplethysmogram (PPG) signals have been dealt with. It demonstrates how new paradigms of intelligent computing like quantum computing can be applied to process and analyze signals in a most precise and effective manner. Processing of high precision signals for real time target recognition by radar and processing of brain images, ECG and EMG signals that feature in this book have significant implications in defense mechanism and medical diagnosis. There are also applications of hybrid methods, algorithms and image filters which are proving to be better than the individual techniques or algorithms. Thus the present volume, enriched in depth and variety of techniques and algorithms concerning processing of various types of signals, is likely to be used as a compact yet handy reference for the young researchers, academicians and scientists working in the domain of signal and image processing and also to the post graduate students of computer science and information technology."

"ntroductionTypical Measurement Systems Sources of Variability: Noise Analog Filters: Filter Basics Analog-to-Digital Conversion: Basic Concepts Time Sampling: Basics Data Banks Problems Basic Concepts Noise Data Functions and Transforms Convolution, Correlation, and Covariance Sampling Theory and Finite Data Considerations Problems Spectral Analysis: Classical Methods Introduction The Fourier Transform: Fourier Series Analysis Aperiodic Functions MATLAB Implementation: Direct FFT Truncated Fourier Analysis: Data Windowing MATLAB Implementation: Window Functions Power Spectrum MATLAB Implement."

"This book constitutes the refereed proceedings of the 5th International Conference on Image and Signal Processing, ICISP 2012, held in Agadir, Morocco, in June 2012. The 75 revised full papers presented were carefully reviewed and selected from 158 submissions. The contributions are grouped into the following topical sections, multi/hyperspectral imaging, image itering and coding, signal processing, biometric, watermarking and texture, segmentation and retieval, image processing, and pattern recognition."

"Sinyal dalam konteks telekomunikasi membawa informasi dengan variasi terhadap waktu, termasuk sinyal suara yang bersifat non-stasioner. Kehadiran noise dalam sinyal suara dapat mengurangi kualitas informasi yang ditransmisikan. Penggunaan transformasi wavelet telah menjadi pendekatan yang efektif dalam denoising sinyal suara, namun untuk hasil optimal, diperlukan pemilihan model threshold dan wavelet families yang tepat. Penelitian ini mengeksplorasi kinerja berbagai model threshold dalam denoising sinyal suara. Hasil penelitian menunjukkan bahwa waktu komputasi untuk denoising meningkat seiring dengan peningkatan level dekomposisi, dengan threshold Donoho memiliki waktu komputasi tercepat, diikuti oleh modifikasi, dan acuan Gang Yang [9] paling lambat. Penggunaan wavelet families juga memengaruhi nilai Mean Squared Error (MSE) dan waktu komputasi. Model threshold acuan Gang Yang [9] memberikan MSE terbaik dengan waktu komputasi 119,252 detik pada level dekomposisi 4, sedangkan threshold modifikasi menawarkan waktu komputasi lebih cepat yaitu 87,965 detik dengan MSE hampir setara pada level dekomposisi 2. Peningkatan panjang filter wavelet meningkatkan kompleksitas program dan waktu komputasi, namun efeknya bervariasi pada tiap model threshold. Selain itu, dilakukan denoising pada noise teras rumah (SPL 83,445 dB) dan noise mesin konstruksi (SPL 87,439 dB). Pada noise teras rumah, level dekomposisi 1 dengan Biorthogonal 3.3 (bior33) paling efektif, mengurangi SPL menjadi 40,216 dB. Pada noise mesin konstruksi, level dekomposisi 1 dengan Reverse Biorthogonal 3.3 (rbio33) paling efektif, menurunkan SPL menjadi 69,569 dB. Berdasarkan hal tersebut, dalam memilih model threshold yang optimal, perlu dipertimbangkan nilai MSE dan efisiensi komputasi. Penelitian ini memberikan wawasan penting dalam memilih metode denoising yang efektif untuk meningkatkan kualitas sinyal suara.

---

In telecommunications, signals carry information with variations over time, including non-stationary audio signals. Noise in audio signals can degrade the quality of transmitted information. Wavelet transform is an effective approach for denoising audio signals, but optimal results require appropriate threshold models and wavelet families. This study explores the performance of various threshold models in denoising speech signals. Results indicate that computation time for denoising increases with decomposition levels; the Donoho threshold is the fastest, followed by the modified model, with Gang Yang [9]'s reference model being the slowest. Wavelet family choice significantly impacts Mean Squared Error (MSE) and computation time. The Gang Yang [9] reference model offers the best MSE at SNR 20-27 with a slight computation time increase (119.252 seconds at level 4), while the modified model achieves faster computation (87.965 seconds at level 2) with nearly equivalent MSE. Longer wavelet filters increase program complexity and computation time, varying by threshold model. Additionally, denoising was performed on residential porch noise (SPL 83.445 dB) and construction machinery noise (SPL 87.439 dB). For residential porch noise, decomposition level 1 with Biorthogonal 3.3 (bior33) was most effective, reducing the SPL to 40.216 dB. For construction machinery noise, decomposition level 1 with Reverse Biorthogonal 3.3 (rbio33) was most effective, lowering the SPL to 69.569 dB. Thus, selecting an optimal threshold model involves considering both MSE and computational efficiency. This study provides key insights for effective denoising methods to enhance speech signal quality."