Modified Maximum A Posteriori Research Articles

Generating continuous fine-scale land cover mapping by edge-guided maximum a posteriori based spatiotemporal sub-pixel mapping

Global land cover mapping activities are of great important for retrieving the environment we are living in. However, the trade-off between spatial and temporal resolution makes it difficult to obtain the continuous fine-scale land cover product for detail and frequent land surface analysis. To overcome the difficulty, this study proposed a modified maximum a posteriori (MAP) based spatiotemporal sub-pixel mapping (SSM), for a long-term and fine-scale land cover mapping. The proposed approach consists of three parts, i.e., data fidelity term, spatial regularization term, and enhanced temporal regularization term, which can make use of historical fine-scale image for a better super-resolution of the current coarse image. One regional experiment was implemented for the algorithm validation, and one real experiment was investigated, by generating continuous land cover mapping products (2010–2015) at Landsat-like spatial resolution from time series of MODIS images. Compared with traditional methods, the proposed approach reconstructed more pleasant spatial details and had greater quantitative accuracy, outperforming the state-of-the-art spatiotemporal sub-pixel mapping method by averagely 2.58% for all the inspected dates in OA metric. This study not only provides a generalized research framework for generating continuous fine-scale land cover product from daily available MODIS images, but also validates the usability of the time series products for detail land cover monitoring, and reveals a finding that the study area of Wuhan is a well practitioner of the sustainable development policy during its urbanization process.

A Modified Adaptive GMM Approach Based GMM Supervector and I-vector Using NMF Decomposition for Robust Speaker Verification

We propose a new method to enhance performance of speaker verification by investigating a novel modification of adaptive Gaussian Mixture Model (GMM) training. This model is trained using a modified Expectation Maximization (EM) algorithm, combined with a modified Maximum A Posteriori (MAP) estimation based weight factor of observation probabilities, called the observation confidence. The observation confidence is calculated based on the SNR estimation. Based on this modified adaptive GMM training algorithm, we propose to construct GMM supervectors and i-vectors, which are considered as input feature vectors for SVM. Besides, the discriminant features for speaker verification are also exploited by using non-negative matrix factorization (NMF) in the GMM-supervector and i-vector space. Experiment results on utterances from Korean drama (“You came from the stars”) show that our proposed methods significantly outperform the baseline GMM-UBM, GMM-supervector and i-vector based SVM under various noisy conditions.

Turbo Code with Modified LLR and Performance Analysis for Bluetooth Packets in Indoor Environments

Most of the restrictions in indoor environments do not exist for outdoor sites. The main limitations for indoors are the attenuation of walls and the multipath fading effects. The signal propagation is limited by the standard free space path loss in outdoor environments or free space. The loss in free space is usually equal to the range squared whereas the loss exponent for indoors is more like the 4th power. This study investigates the performance of turbo codes with modified Maximum-A-Posteriori (MAP) decoding algorithm for Bluetooth data packets in indoor environments over the frequency selective Rayleigh fading channels; which are also called as indoor wireless communication channels. In order to reduce the computational complexity of turbo decoders, MAP algorithm is modified in this study. Bit error rate (BER) versus energy of bit-to-noise ratio (E b /N o ) of modified MAP algorithms for Bluetooth data packets at 2.4 GHz industrial-scientific-medical (ISM) band are evaluated by means of computer simulations. Thus, modified logarithmic likelihood ratio (LLR) method can significantly reduce the computational complexity of the MAP algorithm. Furthermore, the performances of different types of forward error correction (FEC) coding for Bluetooth data packets are compared in the study.

Voice Activity Detection Based on Conditional MAP Criterion

In this letter, we propose a novel approach to voice activity detection (VAD) based on the modified maximum a posteriori (MAP) criterion conditioned on the voice activity decision made in the previous frame. To exploit the inter-frame correlation of voice activity, the probability of the voice presence conditioned on both the observed spectrum and the voice activity decision in the previous frame is employed instead of the conventional strategy that depends only on the current observation. The proposed conditional MAP criterion incorporating temporal correlations leads to two separate thresholds for the likelihood ratio test (LRT) depending on the previous VAD result. Experimental results show that the VAD based on the proposed conditional MAP criterion outperforms the VAD based on the conventional MAP criterion under various noise environments.

Iterative Multiuser Detection/Decoding for Coded CDMA Systems in Non-Gaussian Noise

We propose a robust iterative multiuser receiver for decoding convolutional coded code-division multiple access (CDMA) signals in both Gaussian and non-Gaussian channel noise. The receiver is derived from a modified maximum a-posteriori (MAP) algorithm called the maxlog-MAP algorithm for robustness against erroneous channel variance estimation. Furthermore, the effect of destructive outliers arising from impulsive noise is mitigated in the proposed receiver by incorporating the robust Huber penalty function into the multiuser detector. The proposed receiver is shown to perform satisfactorily over Gaussian and non-Gaussian impulsive channels. In every iteration, cumulative improvement in the quality of the a-posteriori probabilities is also demonstrated.

Sea ice mapping method for SeaWinds

A sea ice mapping algorithm for SeaWinds is developed that incorporates statistical and spatial a priori information in a modified maximum a posteriori (MAP) framework. Spatial a priori data are incorporated in the loss terms of a Bayes risk formulation. Conditional distributions and priors for sea ice and ocean statistics are represented as empirical histograms that are forced to conform to a set of expected histograms via principal component filtering. Tuning parameters for the algorithm allow adjustments in the algorithm's performance. Results of the algorithm exhibit high correlation with the Remund-Long sea ice mapping algorithm for SeaWinds and the Special Sensor Microwave/Imager National Aeronautics and Space Administration Team 30% ice edge, and are verified with RADARSAT-1 ScanSAR imagery. The resulting sea ice maps exhibit high edge detail, preserve polynyas and ice bodies disjoint from the primary ice sheet, and thus are suitable for use with wind retrieval and sea ice studies. Principles employed in the algorithm may be of interest in other classification studies.

Modified maximum a posteriori decoding algorithm

A modified maximum a posteriori (MMAP) decoding algorithm that uses two extrinsic information values as the thresholds to determine the log-likelihood ratio, forward recursion probability and backward recursion probability is proposed. The MMAP requires less decoding time and complexity than an MAP decoder. Each probability of the proposed algorithm is derived and compared to that obtained using the MAP algorithm.