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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.
The 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies including Computing, communication, IOT, LiDAR, Image Analysis, wireless communication and other new technologies
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 ...
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.
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-- ...
The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes
Provides leading edge information that is critical to the creation of reliable electronic devices and materials, and a focus for interdisciplinary communication in the state of the art of reliability of electronic devices, and the materials used in their manufacture. It focuses on the reliability of electronic, optical, and magnetic devices, and microsystems; the materials and processes used in the ...
2011 Chinese Control and Decision Conference (CCDC), 2011
YATSI may suffer more from the common problem in semi-supervised learning, i.e. the performance is badly influenced due to the unlabeled examples may often be wrongly labeled. In this paper a semi-supervised k-nearest neighbor classifier named De-YATSI (YATSI with Data Editing) is proposed. A data editing based on estimating class conditional probability is used to identify and discard mislabeled examples ...
IEEE Transactions on Cognitive and Developmental Systems, None
This paper proposes a system which uses a three-stage serio-parallel video- oculographic framework for computing the saccadic eye parameters to indicate the amount of cognitive loading. The three stages are viz. face and eye detection, iris and eye corner localization, and finally saccadic parameter computation. Since saccades are fast movements of the eyeballs, accurate estimation of these parameters requires high ...
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012
In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer ...
2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2011
Cooperative sensing, a key enabling technology for dynamic spectrum access, is vulnerable to various sensing-targeted attacks, such as the primary user emulation or spectrum sensing data falsification. These attacks can easily disrupt the primary signal detection process, thus crippling the operation of dynamic spectrum access. While such sensing-targeted attacks can be easily launched by an attacker, it is very challenging ...
IEEE Transactions on Cybernetics, 2013
This paper shows that, by simply adding a triangle aperture (TA) in front of a camera lens, iris autofocus can be easily achieved. Through the TA, the corneal reflection of a light source forms a triangle glint on the image plane. The size and orientation of the glint can be used to infer the amount and the direction of the ...
YATSI may suffer more from the common problem in semi-supervised learning, i.e. the performance is badly influenced due to the unlabeled examples may often be wrongly labeled. In this paper a semi-supervised k-nearest neighbor classifier named De-YATSI (YATSI with Data Editing) is proposed. A data editing based on estimating class conditional probability is used to identify and discard mislabeled examples of the pre-labeled data set. A k-nearest neighbor classifier with weights is trained by the labeled data set and the edited “pre-labeled” data set. Experiments on UCI datasets show that DE-YATSI could more effectively and stably utilize the unlabeled examples to improve classification accuracy than YATSI.
This paper proposes a system which uses a three-stage serio-parallel video- oculographic framework for computing the saccadic eye parameters to indicate the amount of cognitive loading. The three stages are viz. face and eye detection, iris and eye corner localization, and finally saccadic parameter computation. Since saccades are fast movements of the eyeballs, accurate estimation of these parameters requires high frame rates of acquisition and processing. Our proposed framework meets such deadlines by accelerating the process using Graphics Processing Units (GPU). The first stage comprises of the face and eye detection using respective Haar classifiers followed by tracking of a region of interest (ROI) using a Minimum Output Sum of Squared Error (MOSSE) filter. In the second stage, the filter parameters are transferred to the GPU, where our proposed parallel scheme is implemented. In the detected eye region, the iris candidates are ranked using a sum of dot products of normalized displacement vectors with gradient vectors. We also localize the eye corners as the reference points. The saccadic velocity and duration are obtained using this eye position signal in the third stage. Finally, the amount of cognitive loading is determined based on these parameters.
In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.
Cooperative sensing, a key enabling technology for dynamic spectrum access, is vulnerable to various sensing-targeted attacks, such as the primary user emulation or spectrum sensing data falsification. These attacks can easily disrupt the primary signal detection process, thus crippling the operation of dynamic spectrum access. While such sensing-targeted attacks can be easily launched by an attacker, it is very challenging to design a robust cooperative spectrum sensing scheme due mainly to the practical constraints inherent in spectrum sensing, particularly the shared/open nature of the wireless medium and the unpredictability of signal propagation. In this paper, we develop an efficient, yet simple attack detection framework, called IRIS (robust cooperatIve sensing via iteRatIve State estimation), that safeguards the incumbent detection process by checking the consistency among sensing reports via the estimation of system states, namely, the primary user's transmit-power and path-loss exponent. The key insight behind the design of IRIS is that the sensing results are governed by the network topology and the law of signal propagation, which cannot be easily compromised by an attacker. Consequently, the sensing reports must demonstrate consistency among themselves in estimating system states. Our analytical and simulation results show that, by performing consistency-checks, IRIS provides high attack-detection capability, and preserves satisfactory performance in estimating the system states even under very challenging attack scenarios. Based on these observations, we propose a new incumbent detection rule that can further improve the spectrum efficiency. IRIS can be readily deployed in infrastructure-based cognitive radio networks, such as IEEE 802.22 WRANs, with manageable processing and communication overheads.
This paper shows that, by simply adding a triangle aperture (TA) in front of a camera lens, iris autofocus can be easily achieved. Through the TA, the corneal reflection of a light source forms a triangle glint on the image plane. The size and orientation of the glint can be used to infer the amount and the direction of the focus adjustment. A gradient-descent autofocus control law is proposed for uncalibrated lenses. Results from theoretical analysis and real experiments show that the proposed method is more efficient and accurate than the conventional circular aperture approach.
It used to be difficult to precisely understand the situation of miners underground according to the existed miner safety management system. To solve this problem, we propose an improved safety management system based on iris identification and RFID (Radio Frequency Identification) technique. It combines several modern identification and communication techniques such as iris identification, RFID, computer networks and database technique. Iris identification technique is used to identify the miners by the physiological characteristics of iris and specific database for every individual is build to record the precise attendance information. Moreover, RFID is used to realize real-time tracking miners underground. Practical results show that the improved miner safety management system is able to count the number of miners underground exactly and on time, providing the reliable and useful information for daily miner management and emergency rescue. The improved system has high quality of reliability, veracity and security.
Several system require authenticating a person's identity before giving access to resources. With new advances in technology, biometrics is one of the most promising techniques in recognition human. Biometrics intends to identify a person by his physical and/or behavioural characteristic. Among biometric technologies, iris recognition has received increasing attention due to its high reliability. Iris recognition is a biometric authentication technology that uses pattern recognition techniques based on iris characteristics. In this paper we propose a new biometric-based Iris segmentation system. The system acquires the biometric data in the data base CASIA version 1.0. The experimental results are compared to release the performances of our algorithms.
An effective multiple eye-state detection method based on projection is proposed. The structure of an eye sketch map is analyzed. Using integral projection method, the height and width of eye iris could be evaluated. And then the ratio between the height and width of the eye iris visible is selected as the criterion to determine the eye states. The experimental results showed the efficiency of eye states detection method proposed.
Deformation of iris pattern caused by pupil dilation and contraction is one of the most influential intra-class variations. Most state-of-the-art iris recognition methods only focus on the description of local iris texture features. We believe that both geometric and photometric features are important to achieve a robust matching result of deformed iris images. This paper proposes to decompose iris images into lowpass and bandpass components using nonsubsampled contourlet transform (NSCT) and then extract different features. Geometric features are extracted in bandpass components based on key point detection to align deformed iris patterns. And then aligned Ordinal features are extracted in lowpass components to characterize the ordinal measures of local iris regions. Finally, key point features in bandpass components and Ordinal features in lowpass components are fused for deformed iris image matching. Extensive experiments on two challenging iris image databases namely CASIA-Iris-Lamp and ICE'2005 demonstrate that the proposed method outperforms state-of-the-art methods in deformed iris recognition.
For iris recognition, it is inevitable to encounter a large portion of off- angle iris images in less constrained conditions. This paper proposes a feature-level solution to off-angle iris recognition which is less dependent on iris image preprocessing. Firstly, we use geometric features of corneal reflections and multiclass SVM to classify iris images into five categories (i.e., frontal, right, left, up and down) according to the off-angle orientation of iris region. And then a feature learning method based on linear programming is used to select the most effective ordinal features of each iris category. Finally, the input off-angle iris image is recognized with the specific ordinal feature template belonging to the corresponding iris category. Experimental results on the Clarkson Angle database demonstrate that our feature-level solution significantly outperforms the mainstream methods based on off-angle iris image preprocessing.
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