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Discrete wavelet transform

Ge Zhang, Meng Dai, Lin Yang, Weichen Li, Haoting Li, Canhua Xu, Xuetao Shi, Xiuzhen Dong, Feng Fu
BACKGROUND: Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data. METHODS: Weighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes...
January 7, 2017: Biomedical Engineering Online
Karim Ferroudji, Nabil Benoudjit, Ayache Bouakaz
Embolic phenomena, whether air or particulate emboli, can induce immediate damages like heart attack or ischemic stroke. Embolus composition (gaseous or particulate matter) is vital in predicting clinically significant complications. Embolus detection using Doppler methods have shown their limits to differentiate solid and gaseous embolus. Radio-frequency (RF) ultrasound signals backscattered by the emboli contain additional information on the embolus in comparison to the traditionally used Doppler signals...
January 9, 2017: Australasian Physical & Engineering Sciences in Medicine
Pew-Thian Yap, Bin Dong, Yong Zhang, Dinggang Shen
In diffusion MRI, the outcome of estimation problems can often be improved by taking into account the correlation of diffusion-weighted images scanned with neighboring wavevectors in q-space. For this purpose, we propose in this paper to employ tight wavelet frames constructed on non-flat domains for multi-scale sparse representation of diffusion signals. This representation is well suited for signals sampled regularly or irregularly, such as on a grid or on multiple shells, in q-space. Using spectral graph theory, the frames are constructed based on quasi-affine systems (i...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Justien Cornelis, Tim Denis, Paul Beckers, Christiaan Vrints, Dirk Vissers, Maggy Goossens
BACKGROUND: Cardiopulmonary exercise testing (CPET) gained importance in the prognostic assessment of especially patients with heart failure (HF). A meaningful prognostic parameter for early mortality in HF is exercise oscillatory ventilation (EOV). This abnormal respiratory pattern is recognized by hypo- and hyperventilation during CPET. Up until now, assessment of EOV is mainly done upon visual agreement or manual calculation. The purpose of this research was to automate the interpretation of EOV so this prognostic parameter could be readily investigated during CPET...
December 29, 2016: International Journal of Cardiology
A Liu, M Chen, S Jiang, W Lu
PURPOSE: To design and implement a GPU-based discrete wavelet transformation (DWT) to be used in medical image reconstruction, image processing, and data compression. DWTs are widely used in medical physics, but the computation of DWTs is time consuming for large volumetric data. An efficient parallel implementation of DWTs is essential for many time sensitive applications, such as 4DCT. METHODS: We choose Daubechies wavelet transformations as a benchmark, implementing both DWT and inverse DWT (IDWT)...
June 2016: Medical Physics
Roman Starosolski
In order to improve bitrates of lossless JPEG 2000, we propose to modify the discrete wavelet transform (DWT) by skipping selected steps of its computation. We employ a heuristic to construct the skipped steps DWT (SS-DWT) in an image-adaptive way and define fixed SS-DWT variants. For a large and diverse set of images, we find that SS-DWT significantly improves bitrates of non-photographic images. From a practical standpoint, the most interesting results are obtained by applying entropy estimation of coding effects for selecting among the fixed SS-DWT variants...
2016: PloS One
Sharifah Mumtazah Syed Ahmad, Ling Yim Loo, Wan Azizun Wan Adnan, Rina Md Anwar
This study presents a wavelet analysis of resultant velocity features belonging to genuine and forged groups of signature sample. Signatures of individuals were initially classified based on visual human perceptions of their relative sizes, complexities, and legibilities of the genuine counterparts. Then, the resultant velocity was extracted and modeled through wavelet analysis from each sample. The wavelet signal was decomposed into several layers based on maximum overlap discrete wavelet transform (MODWT)...
December 21, 2016: Journal of Forensic Sciences
Yuvraj V Parkale, Sanjay L Nalbalwar
BACKGROUND: Compressed sensing is a novel signal compression technique in which signal is compressed while sensing. The compressed signal is recovered with the only few numbers of observations compared to conventional Shannon-Nyquist sampling, and thus reduces the storage requirements. In this study, we have proposed the 1-D discrete wavelet transform (DWT) based sensing matrices for speech signal compression. The present study investigates the performance analysis of the different DWT based sensing matrices such as: Daubechies, Coiflets, Symlets, Battle, Beylkin and Vaidyanathan wavelet families...
2016: SpringerPlus
Unni V S, Deepak Mishra, G R K S Subrahmanyam
The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation...
December 1, 2016: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Jin Li, Zilong Liu
Imaging integrated with compression is considered the holy grail of microsatellite photography because it improves the degree of integration of the camera system, removing the compression system, high capacity storage system, and high-speed image transmission system, which consume lots of resources of the satellite platform. In this paper, we propose an efficient compressed imaging method for remote sensing photography. We consider wavelet coefficients as pixels of a block-wise megapixel sensor (BMPS). We integrate the saliency information stage into the BMPS to perform compressed sampling (CS) in order to further improve imaging performance...
October 1, 2016: Applied Optics
Zhiwen Chen, Thomas Q Hu, Ho Fan Jang, Edward Grant
The hemicellulose composition of a pulp significantly affects its chemical and physical properties and thus represents an important process control variable. However, complicated steps of sample preparation make standard methods for the carbohydrate analysis of pulp samples, such as high performance liquid chromatography (HPLC), expensive and time-consuming. In contrast, pulp analysis by attenuated total internal reflection Fourier transform infrared spectroscopy (ATR FT-IR) requires little sample preparation...
December 2016: Applied Spectroscopy
Hongqiang Li, Danyang Yuan, Youxi Wang, Dianyin Cui, Lu Cao
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features...
October 20, 2016: Sensors
Siyuan Lu, Xin Qiu, Jianpin Shi, Na Li, Zhi-Hai Lu, Peng Chen, Meng-Meng Yang, Fang-Yuan Liu, Wen-Juan Jia, Yudong Zhang
(Aim) It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information that it is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. Although there are automatic detection methods, they suffer from low accuracy...
October 19, 2016: CNS & Neurological Disorders Drug Targets
Zhenhu Liang, Yue Gu, Xuejing Duan, Lei Cheng, Shujuan Liang, Yunjie Tong, Xiaoli Li
Monitoring the changes of cerebral hemodynamics and the state of consciousness during general anesthesia (GA) is clinically important. There is a great need for developing advanced detectors to investigate the physiological processes of the brain during GA. We developed a multichanneled, functional near-infrared spectroscopy (fNIRS) system device and applied it to GA operation monitoring. The cerebral hemodynamic data from the forehead of 11 patients undergoing propofol and sevoflurane anesthesia were analyzed...
October 2016: Neurophotonics
Sandeep Raj, Kailash Chandra Ray, Om Shankar
BACKGROUND AND OBJECTIVE: The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals...
November 2016: Computer Methods and Programs in Biomedicine
Nima Karimian, Zimu Guo, Mark Tehranipoor, Domenic Forte
Traditional passwords are inadequate as cryptographic keys, as they are easy to forge and are vulnerable to guessing. Human biometrics have been proposed as a promising alternative due to their intrinsic nature. Electrocardiogram (ECG) is an emerging biometric that is extremely difficult to forge and circumvent, but has not yet been heavily investigated for cryptographic key generation. ECG has challenges with respect to immunity to noise, abnormalities, etc. In this paper, we propose a novel key generation approach that extracts keys from real valued ECG features with high reliability and entropy in mind...
September 8, 2016: IEEE Transactions on Bio-medical Engineering
F C Cruz, E F Simas Filho, M C S Albuquerque, I C Silva, C T T Farias, L L Gouvêa
This work studies methods for efficient extraction and selection of features in the context of a decision support system based on neural networks. The data comes from ultrasonic testing of steel welded joints, in which are found three types of flaws. The discrete Fourier, wavelet and cosine transforms are applied for feature extraction. Statistical techniques such as principal component analysis and the Wilcoxon-Mann-Whitney test are used for optimal feature selection. Two different artificial neural network architectures are used for automatic classification...
August 24, 2016: Ultrasonics
Hansa Kundra, Jason C Park, J Jason McAnany
PURPOSE: To compare measurements of the full-field photopic negative response (PhNR), as well as intra-subject variation in the PhNR, using time and time-frequency domain analyses. METHODS: Full-field ERGs were recorded from 20 normally sighted subjects (aged 24-65 years) elicited by a long-wavelength pulse (3 cd s m(-2)) presented against a short-wavelength adapting field (12.5 cd m(-2)). Three to 10 waveforms were obtained from each subject, and each waveform was analyzed using standard time domain analyses of the PhNR, as well as a discrete wavelet transform (DWT) to extract time-frequency components that correspond to the PhNR...
October 2016: Documenta Ophthalmologica. Advances in Ophthalmology
Saleha Khatun, Ruhi Mahajan, Bashir I Morshed
Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data...
2016: IEEE Journal of Translational Engineering in Health and Medicine
Aodhán Hickey, Brook Galna, John C Mathers, Lynn Rochester, Alan Godfrey
BACKGROUND: Multi-resolution analyses involving wavelets are commonly applied to data derived from accelerometer-based wearable technologies (wearables) to identify and quantify postural transitions (PTs). Previous studies fail to provide rationale to inform their choice of wavelet and scale approximation when utilising discrete wavelet transforms. This study examines varying combinations of those parameters to identify best practice recommendations for detecting and quantifying sit-to-stand (SiSt) and stand-to-sit (StSi) PTs...
September 2016: Gait & Posture
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