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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
All fields of satellite, airborne and ground remote sensing.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
2000 10th European Signal Processing Conference, 2000
There are contradictory reports on the usefulness of the Wavelet Packet Transform for feature extraction. In this paper we continue the investigation of this subject with reference to non-stationary speech signals, namely unvoiced plosive consonants /p/,/t/, /k/. We concentrate on the influence of the feature reduction method on the classification rate. Two strategies have been applied: feature selection, performed using ...
International Multi Topic Conference, 2002. Abstracts. INMIC 2002., 2002
1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996
This paper presents a new speech coding algorithm based on a fast wavelet packet transform algorithm and psychoacoustic modeling. The employed FFT-like overlapped block orthogonal transform allows us to approximate the auditory critical band decomposition in an efficient manner, which is a major advantage over previous approaches. Owing to such a decomposition of the original signal, we make use of ...
2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2017
Wavelet transform is widely used in feature extraction and pattern recognition, the choice of wavelet basis directly affects the processing result. This paper constructs feature vector based on the continuous wavelet transform coefficients, The method of selecting the best wavelet base is extracted combined the general characteristics with distance criterion and proposed two feature optimization methods. Three types of actual ...
2013 29th Southern Biomedical Engineering Conference, 2013
Wheezes are abnormal lung sounds, which usually imply obstructive airways diseases. The objective of this study is to design an automatic wheeze detector for a wearable health monitoring system, which is able to locate the wheezes inside the respiratory cycle with high accuracy, and low computational complexity. We compute important features of wheezes, which we classify as temporal and spectral ...
There are contradictory reports on the usefulness of the Wavelet Packet Transform for feature extraction. In this paper we continue the investigation of this subject with reference to non-stationary speech signals, namely unvoiced plosive consonants /p/,/t/, /k/. We concentrate on the influence of the feature reduction method on the classification rate. Two strategies have been applied: feature selection, performed using the Local Discrimination Basis and feature projection performed using Primary Components Analysis (Singular Value Decomposition). Classification has been performed by cluster analysis and neural network. The classification results obtained for PCA outperform those for LDB and other methods examined earlier.
This paper presents a new speech coding algorithm based on a fast wavelet packet transform algorithm and psychoacoustic modeling. The employed FFT-like overlapped block orthogonal transform allows us to approximate the auditory critical band decomposition in an efficient manner, which is a major advantage over previous approaches. Owing to such a decomposition of the original signal, we make use of the human ear masking properties to decrease the mean bit rate of the encoder.
Wavelet transform is widely used in feature extraction and pattern recognition, the choice of wavelet basis directly affects the processing result. This paper constructs feature vector based on the continuous wavelet transform coefficients, The method of selecting the best wavelet base is extracted combined the general characteristics with distance criterion and proposed two feature optimization methods. Three types of actual underwater acoustic targets signal is used to validate the optimization method. The classification results show that the two methods can improve the recognition accuracy under the optimal wavelet basis. For different characteristics, the optimization effect is different.
Wheezes are abnormal lung sounds, which usually imply obstructive airways diseases. The objective of this study is to design an automatic wheeze detector for a wearable health monitoring system, which is able to locate the wheezes inside the respiratory cycle with high accuracy, and low computational complexity. We compute important features of wheezes, which we classify as temporal and spectral characteristics and employed to analyze recorded lung sounds including wheezes from patients with asthma. Time-frequency (TF) technique as well as wavelet packet decomposition (WPD) is used for this purpose. Experimental results verify the promising performance of described methods.
Spectrum Sensing is the quintessence of Cognitive Radio Network and helps in decongesting the RF band. A lot of research has already been done in detection of free space in the frequency spectrum and the need of the hour is exploitation of a novel characteristic of a signal from a newer perspective. Cognitive radio works in a multi-antenna based system with a number of secondary user sensors wherein the signals from primary user is received from different directions. Thus, the angle dimension of the received signal can also be exploited to propose new sensing schemes. In this paper, a novel technique based on direction of arrival estimation has been augmented with wavelet packet decomposition to enhance the performance characteristics in case of closely-spaced DOA and low SNR. The proposed algorithm provides high resolution as well as significantly reduced computation load. The better performance and reliability of the proposed WDOA estimation have been experimentally validated and presented in form of computer simulations.
Threshold choosing is critical in wavelet speech enhancement. In the paper, a novel wavelet packet speech enhancement algorithm is presented based on time- frequency (TF) threshold. Different from the conventional methods in threshold choosing, e.g. invariant threshold and time-variant threshold, the proposed threshold is modulated according to speech TF details other than rough envelops adopted in the recent algorithms based on eager energy operator (TEO) and adaptive noise estimation (ANE). In the new algorithm, the speech TF information is obtained from the frequency-based pre-estimate, and the threshold is modulated with TF characteristic of the pre-estimate. Then via thresholding the wavelet packet coefficients, the contaminated speech can be denoised adaptively. Compared with the former wavelet based algorithms, the proposed algorithm offers more pleasant enhanced speech with less distortion and residual noise in additive Gaussian noise case. Experimental results show its better performance in subjective test, input and output SNR test and modified bark spectral distortion measurement (MBSD).
The training time of classifier based on neural network is very long using the conventional normalization when the distances between samples of different classes are too small. To overcome the disadvantage, the normalization method based on rough set theory is proposed. By normalizing samples using rough ser theory, the samples which are near but belong to different classes are taken apart. The normalized samples are used to train neural network The method is applied into neural network based fault line detection for distribution network The simulation results show that the training time of neural network with processed samples is shorter markedly.
An important issue in ultrasonic nondestructive evaluation is the detection of flaw echoes in the presence of coherent background noise associated with the microstructure of materials. Many signal processing techniques have proven to be useful for this purpose. However, when considering B-scan images, it appears that fully two-dimensional robust flaw detection techniques remain desirable. In this paper, we describe a novel automatic flaw detection method based on the wavelet packet transform, which is particularly well adapted to B-scan image analysis. Some experimental detection results in B-scan images of austenitic stainless steel samples comprising artificial flaws are also presented.
Different methods are compared for the evaluation of the event related synchronization (ERS) in the beta rhythm corresponding to finger movements. In addition to the standard procedure usually employed, the realistic Surface Laplacian (SL) is here introduced to improve the spatial localization of the phenomenon, while a Wavelet Packet (WP) decomposition approach is intended to better detect the time dependent characteristics. The parameters of interest (ERS amplitude and latency) were statistically analyzed through Analysis of Variance (ANOVA) and Scheffe's test. The WP filtering results are well comparable with the traditional filtering procedure. On the other hand, the realistic SL considerably improves the spatial localization and the consistency of the estimation (decreased variance) of the ERS amplitude.
The project defines a standard for high-speed (>100 Mbps at the physical layer) communication devices via electric power lines, so-called broadband over power line (BPL) devices. This standard uses transmission frequencies below 100 MHz. It is usable by all classes of BPL devices, including BPL devices used for the first-mile/last-mile connection (<1500 m to the premise) to broadband services as ...