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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 papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Consumer Electronics (ICCE) is soliciting technical papersfor oral and poster presentation at ICCE 2018. ICCE has a strong conference history coupledwith a tradition of attracting leading authors and delegates from around the world.Papers reporting new developments in all areas of consumer electronics are invited. Topics around the major theme will be the content ofspecial sessions and tutorials.
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.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
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.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission
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 theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...
Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286), 1998
This paper proposes a digital mammogram segmentation method for mammographic images, which is based on the gradient detection between breast tissue and non-tissue area. Detection of tissue edge in medical images is closely related to the correctness of image segmentation. In this paper, we focus on digital mammograms and develop an edge detection method to extract breast tissue against background ...
TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162), 1997
A new method has been presented for real time seismic signal processing. A fuzzy model is extracted for the background noise dynamics, using the ARMA model coefficients. A fuzzy rule base has been generated, and a fuzzy inference engine has been used to detect the variations in the nature of the background noise. The conventional envelope detection algorithm for on-set ...
Final Program and Abstracts on Information, Decision and Control, 2002
There are many algorithms available for detecting noise corrupted signals in background clutter. In cases where the exact statistics of the noise and clutter are unknown, the optimal detector may be estimated from a set of samples of each. One method for doing this is the support vector machine (SVM), which has a detection performance that is dependent on some ...
Proceedings of the IEEE 1996 National Aerospace and Electronics Conference NAECON 1996, 1996
The morphological filters are a class of nonlinear signal processing algorithms, which have been applied extensively to computer vision, image processing, and more recently target detection. This paper presents a morphology-based algorithm for spot target detection in IR clutter. The algorithm utilizes a prior knowledge of target size to suppress background clutter and remove noise. The experimental results show that ...
2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA), 2009
License plate recognition systems have been used extensively for many applications. In order to recognize a license plate efficiently, however, the location of the license plate must be detected in the first place. In our method, the car image first is divided into a set of 5×5 non-overlapping block and a local direction is defined for each block. The M×N ...
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
IMS 2011 Microapps - Ultra Low Phase Noise Measurement Technique Using Innovative Optical Delay Lines
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 7 of 7 - SQUID-based noise thermometers for sub-Kelvin thermometry
IMS 2012 Microapps - Phase Noise Choices in Signal Generation: Understanding Needs and Tradeoffs Riadh Said, Agilent
MicroApps: Phase Noise, Allan Variance, and Frequency Reference (Agilent Technologies)
IMS 2012 Special Sessions: The Evolution of Some Key Active and Passive Microwave Components - E. C. Niehenke
A Transformer-Based Inverted Complementary Cross-Coupled VCO with a 193.3dBc/Hz FoM and 13kHz 1/f3 Noise Corner: RFIC Interactive Forum
A 40GHz PLL with -92.5dBc/Hz In-Band Phase Noise and 104fs-RMS-Jitter: RFIC Interactive Forum 2017
Impact of Linearity and Write Noise of Analog Resistive Memory: IEEE Rebooting Computing 2017
An Analysis of Phase Noise Requirements for Ultra-Low-Power FSK Radios: RFIC Interactive Forum 2017
Noise-Shaped Active SAR Analog-to-Digital Converter - IEEE Circuits and Systems Society (CAS) Distinguished Lecture
Compact 75 GHz LNA with 20-dB Gain and 4-dB Noise Figure - Woorim Shin - RFIC Showcase 2018
IMS 2014: A 600 GHz Low-Noise Amplifier Module
Optical Stealth Communication based on Amplified Spontaneous Emission Noise - Ben Wu - IEEE Sarnoff Symposium, 2019
A Low Power High Performance PLL with Temperature Compensated VCO in 65nm CMOS: RFIC Interactive Forum
Non-Volatile Memory Array Based Quantization - Wen Ma - ICRC San Mateo, 2019
Transistors for THz Systems
The 2010 IEEE Honors Ceremony
Introducing the Kalman Filter
This paper proposes a digital mammogram segmentation method for mammographic images, which is based on the gradient detection between breast tissue and non-tissue area. Detection of tissue edge in medical images is closely related to the correctness of image segmentation. In this paper, we focus on digital mammograms and develop an edge detection method to extract breast tissue against background with noise and/or random artifacts, as a preprocess of image compression. The results show that the developed method performs a precise recovery of boundary between breast tissue and non-tissue area, which is then removed for increasing compression rate. Therefore, problems of image transmission efficiency and data storage amount can be greatly improved. The compression rate is promoted from 3:1 to 5-6:1 by a nonlossy compression method.
A new method has been presented for real time seismic signal processing. A fuzzy model is extracted for the background noise dynamics, using the ARMA model coefficients. A fuzzy rule base has been generated, and a fuzzy inference engine has been used to detect the variations in the nature of the background noise. The conventional envelope detection algorithm for on-set estimation, has been affected by the fuzzy inference engine to make it more flexible and robust, in the presence of a large amount of background noise. The fuzzy inference engine also serves as a proper indicator for sudden variations in the nature of the background noise. If the detected variation is due to the first arrival phase of a seismic event, then a higher order ARMA model is derived and its coefficients are used as the inputs to a trained neural network, for seismic classification. The experimental results are promising and there are some remarkable advantages over the previous methods.
There are many algorithms available for detecting noise corrupted signals in background clutter. In cases where the exact statistics of the noise and clutter are unknown, the optimal detector may be estimated from a set of samples of each. One method for doing this is the support vector machine (SVM), which has a detection performance that is dependent on some regularisation parameter C, and cannot be determined a-priori. The standard method of choosing C is by trying values and choosing the one which minimises the detection error on a cross-validation set. It is often assumed that as the size of the training set increases, the resulting discriminant will give the best possible detection rate on an independent test set. This paper investigates two simple 1D examples: uniform and normal distributions. An example is provided where the optimum detection rate cannot be achieved by an SVM regardless of the C chosen value.
The morphological filters are a class of nonlinear signal processing algorithms, which have been applied extensively to computer vision, image processing, and more recently target detection. This paper presents a morphology-based algorithm for spot target detection in IR clutter. The algorithm utilizes a prior knowledge of target size to suppress background clutter and remove noise. The experimental results show that the approach can detect spot target in the presence of clutter for low signal-to-noise ratio (SNR). The probability of detection and false alarm is presented as ROC curves for sky, ground and sea clutter background.
License plate recognition systems have been used extensively for many applications. In order to recognize a license plate efficiently, however, the location of the license plate must be detected in the first place. In our method, the car image first is divided into a set of 5×5 non-overlapping block and a local direction is defined for each block. The M×N car image is converted to a direction image and a vertical edges image of size (M×N)/25. It reduces much processing time. The direction and the vertical edges of block are calculated by using Sobel operator. We remove most of the background and noise regions in the direction and vertical edges image by an effective algorithm and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of the method.
Background noise is usually an important factor that affects speech recognition performance and many other applications. In this paper, background noise (wideband noise and sinusoidal noise) can be significantly suppressed using the proposed adaptive noise cancellation scheme based on adaptive line enhancer (ALE) and normalized least mean square (NLMS) filters. The proposed scheme is comprised of two stages. The first stage uses an ALE filters, which are used to reduce sinusoidal noise from the primary and reference input signals, whereas the wideband noise is reduced using NLMS adaptive filter in the second stage. To demonstrate the effectiveness of the proposed scheme ,it is compared to the traditional adaptive noise cancellation scheme. The good performance of the proposed scheme have been verified via computer simulations in noisy and reverberant environment.
The brain evoked potentials (BEP) are related directly to series of diseases and physical states. It is helpful to prevent and diagnose the brain diseases by analyzing evoked potentials. The traditional averaged method can show the shape of evoked potentials in the rough but losses some important components. At the same time, the plus number is so many that it will increase the pain of the testee. Wavelet transformation is a rising technology in signal processing which has the feature of multi-resolution analysis. In this paper, we use wavelet transformation for the feature extraction of brain evoked potential and compare with the traditional averaged method. We can see that the evoked potential can be identified only with few plus signals by wavelet transformation technology and the wave of the signal is very smooth which not only saves the useful signal well but also rejects almost all the background noises. Experiments show that the wavelet transformation has good efficiency in the feature extraction of the BEP which has upper application value in the clinic.
Time frequency analysis is used to study RF chirps, transcribed in ionospheric radio science experiments. To date the short time fourier transform has been the algorithm of choice, but other distributions are attractive if improved combinations of execution speed, resolution, and cross-term resistance can be found. The authors have evaluated several popular energy distributions in terms of these attributes, finding each to have desirable features, but none uniquely preferable. As yet untreated are the various linear algorithms, which show promise for improved performance.<<ETX>>
A variable step size normalized sign algorithm (VSS-NSA) is proposed, for acoustic echo cancelation, which adjusts its step size automatically by matching the L<sub>1</sub> norm of the a posteriori error to that of the background noise plus near-end signal. Simulation results show that the new algorithm combined with double-talk detection outperforms the dual sign algorithm (DSA) and the normalized triple-state sign algorithm (NTSSA) in terms of convergence rate and stability.
The detection of steady-state visual evoked potentials (SSVEP) is important in some clinical audiometry and ophthalmology applications. The SSVEPs are usually concealed in the ongoing background electroencephalogram (EEG) generated in the brain. The EEG is highly colored with unknown covariance matrix. In this paper we model the background noise using an autoregressive (AR) model whose parameters are estimated on line, on a block by block basis. The problem of estimating the AR parameters in the presence of the signal and its effect on the bias of the parameter estimates is addressed. We show that in the case of a low level sinusoidal signal, the parameters of the AR model are only slightly perturbed and an accurate estimate of the parameters can be found using the Yule-Walker equations.