A new efficient method for test set embedding based on phase shifters was recently proposed This method suffers from high average and peak power consumption. In this work we propose a new phase shifter-based test set ...
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A new efficient method for test set embedding based on phase shifters was recently proposed This method suffers from high average and peak power consumption. In this work we propose a new phase shifter-based test set embedding method, which, by using interleaving and two LFSRs that change state in a non-overlapping way, significantly reduces the average and peak power consumption.
In this paper a new blind image-adaptive watermarking technique is proposed. The main contributions in this work are the following. First, a new spatial mask taking into account the Human Visual System (HVS) propertie...
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In this paper a new blind image-adaptive watermarking technique is proposed. The main contributions in this work are the following. First, a new spatial mask taking into account the Human Visual System (HVS) properties, is proposed. The mask is constructed based on the local variance of the cover image prediction error sequence. Second, an improved detection scheme has been developed, which is blind, in the sense that no knowledge concerning the cover image is required. The similarity measure used in the detector is the normalized correlation between the reproduced watermark and the prediction error of the watermarked and possibly attacked image (instead of the image itself). Due to the above modifications the proposed technique exhibits superior performance as compared to the conventional HVS-based blind adaptive watermarking. This performance improvement has been justified theoretically and verified through extensive simulations. In particular, the proposed technique is robust to additive white noise, JPEG and Wavelet compression, filtering etc.
Taxonomic reasoning can be a useful organization paradigm to address issues dealing with the management of WWW bookmarks. Management of WWW bookmarks comprise one of the most frequent activities of WWW users. In this ...
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INADA is an enhanced C++ persistent programming language, compliant with ODMG standard. INADA supports multiple-type objects facility which enables any persistent objects to obtain any type at any time the type is nee...
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ISBN:
(纸本)9781581136241
INADA is an enhanced C++ persistent programming language, compliant with ODMG standard. INADA supports multiple-type objects facility which enables any persistent objects to obtain any type at any time the type is needed, and to lose any unnecessary types dynamically. Using the facility, we can model changes in roles/aspects which a real-world entity possesses. Access to a multiple-type object needs to select one from among its own types. The selection is conventionally left to an object accessing a multiple-type object, A real-world entity is, however, flexible enough to decide its roles/aspects depending on a meeting entity whom it exchanges messages with. From the consideration, this paper proposes AccessEE Controlled Type Selection (AEE) method in which a multiple-type object selects one from among its own types depending on an object accessing it. The implementation of AEE method in INADA is also presented, which does not need any modification to the language specification and the processing system of INADA.
In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery. And learning methods were in...
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In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery. And learning methods were investigated. Also, correlation between C3 and C4 signals were investigated, and thereby which AR (combine AR or multivariable AR) model must be used in each EEC recording method.
Enterprise software infrastructure traditionally comprises a number of independent systems in order to support customer needs and business processes complicating productivity, performance and maintenance. Instead ther...
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Enterprise software infrastructure traditionally comprises a number of independent systems in order to support customer needs and business processes complicating productivity, performance and maintenance. Instead there are hypermedia facilities based on widespread standards that provide over a user-friendly, well-known framework richer functionality and features. In the paper, we present the design, implementation and evaluation of the combined use of Web services and adaptive Web-based techniques. We describe a large-scale hypermedia framework that improves the efficiency and performance of conventional enterprise activities like customer services (registration, activation, billing, support) and document management. The presented approach aims to cover these fundamental needs for a network of customers, collaborators and intranet employees.
Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communicatio...
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Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). In this study, statistical pattern recognition method based on AR model was introduced to discriminate the EEG signals recorded during right and left motor imagery. And learning methods (processing period for parameter estimation, AR order, etc.) were investigated. Finally, the effectiveness of our method was confirmed through the experimental studies.
We study the combinatorial structure and computational complexity of extreme Nash equilibria, ones that maximize or minimize a certain objective function, in the context of a selfish routing game. Specifically, we ass...
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A parametric method for estimating the unknown channel impulse response (CIR) in a semi-blind manner is proposed. The main trait of this method is that, instead of seeking the whole CIR sequence, only the unknown time...
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A parametric method for estimating the unknown channel impulse response (CIR) in a semi-blind manner is proposed. The main trait of this method is that, instead of seeking the whole CIR sequence, only the unknown time delays and attenuation factors of the physical channel multipath components are estimated. The technique is based on a suitable application of the subchannel response matching (SRM) criterion. The resulting cost function is separable with respect to the two sets of unknown parameters, i.e. time delays and attenuations. Thus, an efficient two step optimization procedure can be applied. The new method offers significant computational savings and a lower mean square estimation error as compared to existing semi-blind channel estimation methods.
Many diverse methods have been developing in the field of biometric identification as human-friendliness has been emphasized in the intelligent system's area. And one of emerging method is to use human walking beh...
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Many diverse methods have been developing in the field of biometric identification as human-friendliness has been emphasized in the intelligent system's area. And one of emerging method is to use human walking behavior. But, in the previous methods based on human gait, stable somewhat long-term walking data are an essential condition for person recognition. Therefore, these methods are difficult to cope with various change of walking velocity which may be generated frequently during real walking. In this paper, we suggest a new method which uses just one-step walking data from mat-type pressure sensor. When a human walk through the pressure sensor, we get quantized COP (center of pressure) trajectory and HMM (hidden Markov model) is used to make probability models for user's each foot. And then, HMMs for two feet are combined for better performance by Levenberg-Marquart learning method. Finally, we prove the usefulness of the suggested method using 8 people recognition experiments.
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