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https://www.readbyqxmd.com/read/28534785/efficient-hardware-implementation-of-real-time-low-power-movement-intention-detector-system-using-fft-and-adaptive-wavelet-transform
#1
Alireza Chamanzar, Mahdi Shabany, Alireza Malekmohammadi, Sara Mohammadinejad
The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum sensitivity of 88% and selectivity of 78% (only 7% loss of sensitivity)...
May 17, 2017: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/28529760/denoising-techniques-in-adaptive-multi-resolution-domains-with-applications-to-biomedical-images
#2
Salim Lahmiri
Variational mode decomposition (VMD) is a new adaptive multi-resolution technique suitable for signal denoising purpose. The main focus of this work has been to study the feasibility of several image denoising techniques in empirical mode decomposition (EMD) and VMD domains. A comparative study is made using 11 techniques widely used in the literature, including Wiener filter, first-order local statistics, fourth partial differential equation, nonlinear complex diffusion process, linear complex diffusion process (LCDP), probabilistic non-local means, non-local Euclidean medians, non-local means, non-local patch regression, discrete wavelet transform and wavelet packet transform...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529759/high-frequency-based-features-for-low-and-high-retina-haemorrhage-classification
#3
Salim Lahmiri
Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD)...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28511108/wine-yeasts-identification-by-maldi-tof-ms-optimization-of-the-preanalytical-steps-and-development-of-an-extensible-open-source-platform-for-processing-and-analysis-of-an-in-house-ms-database
#4
Cristina Gutiérrez, M Ángeles Gómez-Flechoso, Ignacio Belda, Javier Ruiz, Nour Kayali, Luis Polo, Antonio Santos
Saccharomyces cerevisiae is the most important yeast species for the production of wine and other beverages. In addition, nowadays, researchers and winemakers are aware of the influence of non-Saccharomyces in wine aroma complexity. Due to the high microbial diversity associated to several agro-food processes, such as winemaking, developing fast and accurate methods for microbial identification is demanded. In this context, MALDI-TOF MS mass fingerprint provides reliable tool for fast biotyping and classification of microorganisms...
May 8, 2017: International Journal of Food Microbiology
https://www.readbyqxmd.com/read/28505137/airborne-infrared-and-visible-image-fusion-combined-with-region-segmentation
#5
Yujia Zuo, Jinghong Liu, Guanbing Bai, Xuan Wang, Mingchao Sun
This paper proposes an infrared (IR) and visible image fusion method introducing region segmentation into the dual-tree complex wavelet transform (DTCWT) region. This method should effectively improve both the target indication and scene spectrum features of fusion images, and the target identification and tracking reliability of fusion system, on an airborne photoelectric platform. The method involves segmenting the region in an IR image by significance, and identifying the target region and the background region; then, fusing the low-frequency components in the DTCWT region according to the region segmentation result...
May 15, 2017: Sensors
https://www.readbyqxmd.com/read/28499157/identify-temporal-trend-of-air-temperature-and-its-impact-on-forest-stream-flow-in-lower-mississippi-river-alluvial-valley-using-wavelet-analysis
#6
Ying Ouyang, Prem B Parajuli, Yide Li, Theodor D Leininger, Gary Feng
Characterization of stream flow is essential to water resource management, water supply planning, environmental protection, and ecological restoration; while air temperature variation due to climate change can exacerbate stream flow and add instability to the flow. In this study, the wavelet analysis technique was employed to identify temporal trend of air temperature and its impact upon forest stream flows in Lower Mississippi River Alluvial Valley (LMRAV). Four surface water monitoring stations, which locate near the headwater areas with very few land use disturbances and the long-term data records (60-90 years) in the LMRAV, were selected to obtain stream discharge and air temperature data...
May 9, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28499137/a-comparison-of-the-ground-reaction-force-frequency-content-during-rearfoot-and-non-rearfoot-running-patterns
#7
Allison H Gruber, W Brent Edwards, Joseph Hamill, Timothy R Derrick, Katherine A Boyer
Running with a non-rearfoot pattern has been claimed to reduce injury risk because the impact peak in the vertical ground reaction force (GRF) is visually absent in the time-domain compared with a rearfoot pattern. However, running results in a rapid deceleration of the lower extremity segments immediately following initial contact with the ground, regardless of footfall pattern. Therefore, the frequency content of the GRF is expected to contain evidence of this collision. The purpose of the present study was to characterize the waveform components of the GRF generated during the impact phase by habitual rearfoot and habitual non-rearfoot pattern groups using the continuous wavelet transform...
April 28, 2017: Gait & Posture
https://www.readbyqxmd.com/read/28499122/emd-dwt-based-transform-domain-feature-reduction-approach-for-quantitative-multi-class-classification-of-breast-lesions
#8
Sharmin R Ara, Syed Khairul Bashar, Farzana Alam, Md Kamrul Hasan
Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i...
April 24, 2017: Ultrasonics
https://www.readbyqxmd.com/read/28498855/reversible-integer-wavelet-transform-for-blind-image-hiding-method
#9
Nazeer Muhammad, Nargis Bibi, Zahid Mahmood, Tallha Akram, Syed Rameez Naqvi
In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image...
2017: PloS One
https://www.readbyqxmd.com/read/28489019/heart-sound-classification-from-unsegmented-phonocardiograms
#10
Philip Langley, Alan Murray
Objective Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. Approach PCG segments of 5 second duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 second segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation...
May 10, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28487724/fast-compressed-sensing-mri-based-on-complex-double-density-dual-tree-discrete-wavelet-transform
#11
Shanshan Chen, Bensheng Qiu, Feng Zhao, Chao Li, Hongwei Du
Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts...
2017: International Journal of Biomedical Imaging
https://www.readbyqxmd.com/read/28485624/express-an-automated-algorithm-of-peak-recognition-based-on-continuous-wavelet-transformation-and-local-signal-to-noise-ratio
#12
Fang Qian, Hui Yi Wu, Peng Hao
No abstract text is available yet for this article.
January 1, 2017: Applied Spectroscopy
https://www.readbyqxmd.com/read/28484720/eeg-based-computer-aided-diagnosis-of-autism-spectrum-disorder-using-wavelet-entropy-and-ann
#13
Ridha Djemal, Khalil AlSharabi, Sutrisno Ibrahim, Abdullah Alsuwailem
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28484325/element-analysis-a-wavelet-based-method-for-analysing-time-localized-events-in-noisy-time-series
#14
Jonathan M Lilly
A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized 'events'. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate...
April 2017: Proceedings. Mathematical, Physical, and Engineering Sciences
https://www.readbyqxmd.com/read/28483469/spectral-properties-of-multiple-myoelectric-signals-new-insights-into-the-neural-origin-of-muscle-synergies
#15
Julien Frère
It is still unclear if muscle synergies reflect neural strategies or mirror the underlying mechanical constraints. Therefore, this study aimed to verify the consistency of muscle groupings between the synergies based on the linear envelope (LE) of muscle activities and those incorporating the time-frequency (TF) features of the electromyographic (EMG) signals. Twelve healthy participants performed six 20-m walking trials at a comfort and fast self-selected speed, while the activity of eleven lower limb muscles was recorded by means of surface EMG...
May 5, 2017: Neuroscience
https://www.readbyqxmd.com/read/28475486/a-new-method-for-qrs-detection-in-ecg-signals-using-qrs-preserving-filtering-techniques
#16
Tanushree Sharma, Kamalesh K Sharma
Detection of QRS complexes in ECG signals is required for various purposes such as determination of heart rate, feature extraction and classification. The problem of automatic QRS detection in ECG signals is complicated by the presence of noise spectrally overlapping with the QRS frequency range. As a solution to this problem, we propose the use of least-squares-optimisation-based smoothing techniques that suppress the noise peaks in the ECG while preserving the QRS complexes. We also propose a novel nonlinear transformation technique that is applied after the smoothing operations, which equalises the QRS amplitudes without boosting the supressed noise peaks...
May 5, 2017: Biomedizinische Technik. Biomedical Engineering
https://www.readbyqxmd.com/read/28468952/reliability-of-an-automatic-classifier-for-brain-enlarged-perivascular-spaces-burden-and-comparison-with-human-performance
#17
Victor Gonzalez-Castro, Maria Del Carmen Valdes-Hernandez, Francesca Chappell, Paul A Armitage, Stephen Makin, Joanna Wardlaw
In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease, poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine(SVM) to classify the burden of PVS in the basal ganglia(BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: 1)statistics obtained from Wavelet transform's coefficients, 2)local binary patterns and 3)bag of visual words (BoW)-based descriptors characterising local keypoints obtained from a dense grid with the scale-invariant feature transform characteristics...
May 3, 2017: Clinical Science (1979-)
https://www.readbyqxmd.com/read/28468360/high-speed-all-optical-haar-wavelet-transform-for-real-time-image-compression
#18
Milad Alemohammad, Jasper R Stroud, Bryan T Bosworth, Mark A Foster
We present a high-speed single pixel flow imager based on an all-optical Haar wavelet transform of moving objects. Spectrally-encoded wavelet measurement patterns are produced by chirp processing of broad-bandwidth mode-locked laser pulses. A complete wavelet pattern set serially illuminates the object via a spectral disperser. This high-rate structured illumination transforms the scene into a set of sparse coefficients. We show that complex scenes can be compressed to less than 30% of their Nyquist rate by thresholding and storing the most significant wavelet coefficients...
May 1, 2017: Optics Express
https://www.readbyqxmd.com/read/28463698/forecasting-of-groundwater-level-fluctuations-using-ensemble-hybrid-multi-wavelet-neural-network-based-models
#19
Rahim Barzegar, Elham Fijani, Asghar Asghari Moghaddam, Evangelos Tziritis
Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing)...
April 29, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28459693/real-time-epileptic-seizure-detection-using-eeg
#20
Lasitha S Vidyaratne, Khan M Iftekharuddin
This work proposes a novel patient-specific real-time automatic epileptic seizure onset detection, using both scalp and intracranial EEG. The proposed technique obtains harmonic multiresolution and self-similarity-based fractal features from EEG for robust seizure onset detection. A fast wavelet decomposition method, known as harmonic wavelet packet transform (HWPT), is computed based on Fourier transform to achieve higher frequency resolutions without recursive calculations. Similarly, fractal dimension (FD) estimates are obtained to capture self-similar repetitive patterns in the EEG signal...
April 25, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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