The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, a code-aided maximum-likelihood and moment-based joint SNR estimator is proposed for M- ary amplitude phase shift keying (APSK) signals over AWGN channels. The proposed estimator significantly improves the performance at low SNRs by utilizing the syndrome in the LDPC codes to act as a reference measurement of estimation performance. Moreover, a methodology to measure the performance...
A maximum likelihood-based code-aided joint carrier phase estimation and ambiguity resolution algorithm is proposed for coded amplitude and phase shift keying (APSK) signals. The proposed estimator iteratively uses the a posteriori probability of coded bits obtained from the channel decoder to improve the performance of phase estimation and ambiguity resolution. Two initialization schemes are employed...
This paper presents a statistical approach to the formation of a complete set of “meaningful contours” conformal to human visual perception, characterized by preserving both illusory edge pixels and their corresponding physical edge pixels through alignment interpretation. The method mainly consists of two stages: (i) learning alignment significance (LAS) based on the evaluation of orientations neighboring...
Cooperative localization has become a novel technique to improve the performance in harsh environment with insufficient anchors. In this paper, distributed cooperative localization is studied based on message passing on factor graph. The factor graph is constructed according to the joint a posteriori distribution of nodes' positions. Because of the nonlinearity between the positions and observations,...
Localization in wireless sensor networks has become an attractive research field in recent years. Most studies focus on the mitigation of measurement noise by assuming the positions of anchors are perfectly known, which may become impractical due to some inevitable errors in the observations of anchors' positions. This paper addresses the problem by taking into account the a priori position information...
In this paper, we consider the effect of different rules of symbol decision on the performance of decision-directed synchronizers for LDPC-coded systems. Different from the conventional hard symbol decision based on the Maximum-A- Posteriori (MAP) criterion, soft symbol decision can be considered as the Minimum-Mean-Square-Error (MMSE) estimation of the transmitted symbol. By whether or not the coding...
Item Response Theory (IRT) is a psychological and educational measurement theory which breaks the limitations of Classical Test Theory (CTT). The core issue of IRT application is parameter estimation. Taking the Logistic model as an example, this article introduces the basic models and parameter estimation methods of IRT, especially the IRT parameter estimation algorithms based on artificial intelligence...
Data-aided (DA) signal-to-noise ratio (SNR) estimation is required especially at low SNR. The conventional maximum likelihood (ML) DA SNR estimator requires perfect carrier phase estimation and frequency recovery. In this paper, we propose a novel carrier frequency robust DA SNR estimator with its improved variant using autocorrelation of received MPSK symbols. Computer simulations are used to examine...
For the aloha based anti-collision algorithm in RFID networks, the tag collisions could greatly reduce the throughput of the system. If the number of tags was gotten, the throughput could be greatly improved. Based on maximum likelihood estimation, the proposed hybrid tag number estimation scheme combined the binary search based anti-collision algorithm and aloha based anti-collision algorithm to...
This paper presents an investigation into ways of integrating articulatory features into hidden Markov model (HMM)-based parametric speech synthesis. In broad terms, this may be achieved by estimating the joint distribution of acoustic and articulatory features during training. This may in turn be used in conjunction with a maximum-likelihood criterion to produce acoustic synthesis parameters for...
This paper presents a comparison and evaluation between the conventional maximum likelihood estimation based adaptation and different discriminative adaptation criteria. The performance of different LR and MAP adaptation are compared respectively, and the strategies of first applying LR then MAP based on both MLE and DT criteria are evaluated. The effect of the amount of available data for adaptation...
In this paper appropriate confidence measures (CMs) are investigated for Mandarin command word recognition, both in the so-called target region and non-target region, respectively. Here the target region refers to the recognized speech part of command word while the non-target region refers to the recognized silence part. It shows that exploiting extra information in the non-target region can effectively...
In order to solve the issues related to the maximum likelihood (ML) based HMM training for HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed. This paper continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis. We introduce a MGE linear regression (MGELR) based model adaptation algorithm, where the transforms from source HMMs to...
Due to the inconsistency between the maximum likelihood (ML) based training and the synthesis application in HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed for HMM training. This paper continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis. We propose a MGE linear regression (MGELR) based model adaptation algorithm, where the...
This paper presents a minimum unit selection error (MUSE) training method for HMM-based unit selection speech synthesis system, which selects the optimal phone-sized unit sequence from the speech database by maximizing the combined likelihood of a group of trained HMMs. Under MUSE criterion, the weights and distribution parameters of these HMMs are estimated to minimize the number of different units...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.