A Dynamic 3D Geometry Compression Scheme Based on the Lifted Wavelet Transform

2007 ◽  
Author(s):  
Malic Dekkar ◽  
Yan Wang
Keyword(s):  
Wavelet Transform ◽  
Visual Information ◽  
Hybrid Approach ◽  
High Volume ◽  
Compression Scheme ◽  
Geometry Compression ◽  
Engineering Collaboration ◽  
3D Geometry ◽  
Support Engineering ◽  
Audiovisual Presentation

In distributed environments, efficient visual information sharing is critical for effective communication in real-time engineering collaboration. Methods of geometry compression are needed for high-volume geometry data distribution over networks with limited bandwidths and heterogeneous storage capacities. In this paper, a new compression scheme for time-varying 3D geometry is introduced to support engineering and scientific visualization while showing potential for the audiovisual presentation and entertainment fields. This hybrid approach allows geometric and topological information to be uniformly encoded as volume grid values then compressed based on the lifted wavelet transform. The compression ratio is significantly increased without compromising surface quality due to rescaling and integer-to-integer lifting. This approach also allows for scalability in terms of additional data streams such as color, audio, and other types of concurrent data necessary for the desired customization of this method.

scholarly journals A Hybrid Approach to Gender Classification using Speech Signal

2019 ◽  
pp. 17-24
Author(s):  
M. Yasin Pir ◽  
Mohamad Idris Wani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Speech Signal ◽  
Hybrid Approach ◽  
Gender Classification ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Feed Forward Network ◽  
Artificial Neural

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

P119 SURGICAL OUTCOMES AFTER ESOPHAGECTOMY IN THE ELDERLY POPULATION - A SINGLE CENTER COHORT STUDY

2019 ◽  
Vol 32 (Supplement_2) ◽  
Author(s):  
Söderström Henna ◽  
Ilonen Ilkka ◽  
Andersson Saana ◽  
Kauppi Juha ◽  
Räsänen Jari
Keyword(s):  
Elderly Patients ◽  
Neoadjuvant Treatment ◽  
Hybrid Approach ◽  
High Volume ◽  
Good Health ◽  
The Elderly ◽  
Pulmonary Complications ◽  
Long Term Results ◽  
High Morbidity ◽  
Survival Times

Abstract Aim To evaluate morbidity and mortality after esophagectomy among elderly patients Background & Methods Esophagectomy is associated with significant morbidity1, and with the aging population we are faced with an increasing number of elderly patients eligible for surgery. In this retrospective study we analyzed both minor and major postoperative complications (Clavidien-Dindo II-V), in-hospital and 90-day mortality, and overall survival in all carcinoma patients ≥75 yo undergoing esophagectomy for cancer between 2009 and 2018 at a high-volume center. Results 47 patients underwent esophagectomy during the 10-yr. period, 95,7% either minimally invasively or with a hybrid approach. Median age was 77, and the oldest patient 85 yo. The majority were in otherwise good health, 39 had an ASA score of 1-2, and all but one was ECOG 0. 70% had adenocarcinomas, and 70% received neoadjuvant treatment. 68,1% of the patients suffered some sort of complication. 19 patients (40,4%) had a C-D III-IV complication, 9 of which were pulmonary requiring a median of 5 days in the ICU. Only 4 patients (8,5%) had anastomotic leakage requiring an intervention, 2 were managed endoscopically, 1 early dehiscence was sutured and one required a revision and LD plasty. One patient had non-fatal gastric tube necrosis that was excised. Atrial fibrillation (34%) was the most common but easily managed issue, followed by pulmonary complications (C-D II 5pts /10,6%, C-D III-IV 10 pts / 21,3%). We had 3 re-operations for bowl herniation, and one for bleeding. Our in-hospital and 90-day mortality were 0%, in spite of the high complication rate. 63,8% were discharged home. Mean and median survival times 68,2 mo. and 47 mo., respectively. At time of follow up, 28 patients (59,6%) were still alive. Conclusion Esophagectomy comes with high morbidity, but with acceptable long term results it should be considered for elderly patients otherwise fit for surgery. Our results show that in select cases age is just a number 1. Low DE, Kuppusamy MK, Alderson D, Cecconello I, Chang AC, Darling G, Davies A, D'Journo XB, Gisbertz SS, Griffin SM, Hardwick R, Hoelscher A, et al. Benchmarking Complications Associated with Esophagectomy. Ann Surg 2017;:1.

scholarly journals A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jujie Wang
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Experimental Simulation ◽  
Wind Speed Forecasting ◽  
Optimal Network ◽  
Partial Autocorrelation Function ◽  
Parks Management ◽  
Hidden Layer

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China’s wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

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