Multiple symbol differential detection based on sphere decoding for unitary space-time modulation

Recently, a multiple symbol differential （MSD） sphere decoding （SD） algorithm for unitary spacetime modulation over quasi-static channel has been proved to achieve the performance of maximumlikelihood （ML） detection with relatively low complexity. However, an error floor occurs if the algorithm is applied over rapid-fading channels. Based on the assumption of continuous fading, a multiple symbol differential automatic sphere decoding （MSDASD） algorithm is developed by incorporating a recursive form of an ML metric into automatic SD （ASD） algorithm. Furthermore, two algorithms, termed as MSD approximate ASD （MSDAASD） and MSD pruning ASD （MSDPASD）, are proposed to reduce computational complexity and the number of comparisons, respectively. Compared with the existing typical algorithms, i.e., multiple symbol differential feedback detection （MS-DFD） and noncoherent sequence detection （NSD）, the performance of the proposed algorithms is much superior to that of MS-DFD and a little inferior to that of NSD, while the complexity is lower than that of MS-DFD in most cases and significantly lower than that of NSD.

Application of transmit diversity for improved robust watermarking

Many common multimedia signal processing, including cropping, filtering, and perceptual coding, make watermark signal fading-like modification. A scheme that applies transmit diversity technique to improve robustness of digital watermarking is presented. First, the scheme decomposes the original image using wavelet pyramid algorithm and chooses the middle-frequency band for transmission channel that the watermark will be embedded into. Then the watermark is pseudo-randomly permuted. The scheme makes use of space-time coding and differential detection technique to embed and extract watermark. The extracting process has access to neither the original image nor channel state information. Experimental results demonstrate that the scheme improves the performance of robust watermarking.

The progress on the estimation of DNA methylation level and the detection of abnormal methylation

Background:DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes.The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.Results:In this review,the main high-throughput detection technologies are summarized,and then the three trends of computational estimation of DNA methylation levels were analyzed,especially the expanding of the methylation data with lower coverage.Furthermore,the detection methods of differential methylation patterns for sequencing and array data were presented.Conclusions:More and more research indicated the great importance of DNA methylation changes across different diseases,such as cancers.Although a lot of enormous progress has been made in understanding the role of DNA methylation,only few methylated genes or functional elements serve as clinically relevant cancer biomarkers.The bottleneck in DNA methylation advances has shifted from data generation to data analysis.Therefore,it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.

Comparison of diagnostic value of TIRADS,BSRTC,BRAF^(V600E) mutation detection and their combined use in differentiating thyroid nodules

Objective To compare the diagnostic efficiency of the thyroid imaging reporting and data system(TIRADS),the Bethesda system for reporting thyroid cytopathology(BSRTC)and BRAFV600Edetection,and their combined use in the differentiation between benign and malignant thyroid nodules.Methods One hundred

A Fault-tolerance Estimating Method for Ionosphere Corrections in Satellite Navigation System

Aiming to the reliable estimates of the ionosphere differential corrections for the satellite navigation system in the presence of the ionosphere anomaly, a fault-tolerance estimating method, which is based on the distributed Kalman filtering, is proposed. The method utilizes the parallel sub-filters for estimating the ionosphere differential corrections. Meanwhile, an infinite norm （IN） method is proposed for the detection of the ionosphere irregularity in the filter processing. Once the anomaly is detected, the sub-filter contaminated by the anomaly measurements will be excluded to ensure the reliability of the estimates. The simulation is conducted to validate the method and the results indicate that the anomaly can be found timely due to the novel fault detection method based on the infinite norm. Because of the parallel sub-filter architecture, the measurements are classified by the spatial distribution so that the ionosphere anomaly can be positioned and excluded more easily. Thus, the method can provide the robust and accurate ionosphere differential corrections.