BACKGROUND: There are ever increasing researches implying that noncoded RNAs (ncRNAs) specifically circular RNAs (circRNAs) and microRNAs (miRNAs) in exosomes play vital roles in respiratory disease. However, the detailed mechanisms persist to be unclear in mycobacterial infection. METHODS: In order to detect circRNAs and miRNAs expression pattern and potential biological function in tuberculosis, we performed immense parallel sequencing for exosomal ncRNAs from THP-1-derived macrophages infected by Mycobacterium tuberculosis H37Ra, Mycobacterium bovis BCG and control Streptococcus pneumonia, respectively and uninfected normal cells. Besides, THP-1-derived macrophages were used to verify the validation of differential miRNAs, and monocytes from PBMCs and clinical plasma samples were used to further validate differentially expressed miR-185-5p. RESULTS: Many exosomal circRNAs and miRNAs associated with tuberculosis infection were recognized. Extensive enrichment analyses were performed to illustrate the major effects of altered ncRNAs expression. Moreover, the miRNA-mRNA and circRNA-miRNA networks were created and expected to reveal their interrelationship. Further, significant differentially expressed miRNAs based on Exo-BCG, Exo-Ra and Exo-Control, were evaluated, and the potential target mRNAs and function were analyzed. Eventually, miR-185-5p was collected as a promising potential biomarker for tuberculosis. CONCLUSION: Our findings provide a new vision for exploring biological functions of ncRNAs in mycobacterial infection and screening novel potential biomarkers. To sum up, exosomal ncRNAs might represent useful functional biomarkers in tuberculosis pathogenesis and diagnosis.

BACKGROUND: Amplicon re-sequencing based on the automated Sanger method remains popular for detection of single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (InDels) for a spectrum of genetics applications. However, existing software tools for detecting intra-individual SNPs and InDels in direct amplicon sequencing of diploid samples are insufficient in analyzing single traces and their accuracy is still limited. RESULTS: We developed a novel computation tool, named DiSNPindel, to improve the detection of intra-individual SNPs and InDels in direct amplicon sequencing of a diploid. Neither reference sequence nor additional sample was required. Using two real datasets, we demonstrated the usefulness of DiSNPindel in its ability to improve largely the true SNP and InDel discovery rates and reduce largely the missed and false positive rates as compared with existing detection methods. CONCLUSIONS: The software DiSNPindel presented here provides an efficient tool for intra-individual SNP and InDel detection in diploid amplicon sequencing. It will also be useful for identification of DNA variations in expressed sequence tag (EST) re-sequencing.

A precoding technique applied to symbols transmitted in an ultra-wideband (UWB) system can concentrate the signal power at the receiver for a higher data detection rate. The codeword design problem for optimal UWB precoding is investigated in this work. After the problem formulation, we examine its solution in the full-rank and the reduced-rank subspaces to obtain different tradeoffs between the detection performance and the amount of feedback messages. Furthermore, we propose two subspace selection schemes, each of which corresponds to a code design method. The resultant optimal codewords improve the performance of previous work significantly at the expense of a slightly increased amount of feedback messages. Finally, the performance of a precoded UWB system with various codewords is compared via computer simulation.

Detection of small lesions in fluorodeoxyglucose (FDG) positron emission tomography (PET) is limited by image resolution and low signal to noise ratio. We have previously described a matched subspace detection method that uses the time activity curve to distinguish tumors from background in dynamic FDG PET. Applying this algorithm on a voxel by voxel basis throughout the dynamic image produces a test statistic image or "map" which on thresholding indicates the potential locations of secondary or metastatic tumors. In this paper, we describe a thresholding method that controls familywise error rate (FWER) for the matched subspace detection statistical map. The method involves three steps. First, the PET image is segmented into several homogeneous regions. Then, the statistical map is normalized to a zero mean unit variance Gaussian random field. Finally, the images are thresholded at a fixed FWER by estimating their spatial smoothness and applying a random field theory maximum statistic approach. We evaluate this thresholding method using digital phantoms generated from clinical dynamic images. We also present an application of the proposed approach to clinical PET data from a breast cancer patient with metastatic disease.

We describe a matched subspace detection algorithm to assist in the detection of small tumors in dynamic positron emission tomography (PET) images. The algorithm is designed to differentiate tumors from background using the time activity curves (TACs) that characterize the uptake of PET tracers. TACs are modeled using linear subspaces with additive Gaussian noise. Using TACs from a primary tumor region of interest (ROI) and one or more background ROIs, each identified by a human observer, two linear subspaces are identified. Applying a matched subspace detector to these identified subspaces on a voxel-by-voxel basis throughout the dynamic image produces a test statistic at each voxel which on thresholding indicates potential locations of secondary or metastatic tumors. The detector is derived for three cases: using a single TAC with white noise of unknown variance, using a single TAC with known noise covariance, and detection using multiple TACs within a small ROI with known noise covariance. The noise covariance is estimated for the reconstructed image from the observed sinogram data. To evaluate the proposed method, a simulation-based receiver operating characteristic (ROC) study for dynamic PET tumor detection is designed. The detector uses a dynamic sequence of frame-by-frame 2-D reconstructions as input. We compare the performance of the subspace detectors with that of a Hotelling observer applied to a single frame image and of the Patlak method applied to the dynamic data. We also show examples of the application of each detection approach to clinical PET data from a breast cancer patient with metastatic disease.

We propose to use the Rake receiver to improve the system performance by collecting more signal power in an ultra-wideband (UWB) system precoded by the channel phase information, called the CPP-UWB system [1], in this work. The performance of the proposed system is determined by the selected channel taps and the corresponding combining schemes for those Rake fingers. Three combing schemes, Le., the maximum ratio combining (MRC), the minimized mean square error (MMSE) and the zero-forcing (ZF), are proposed. When the MRC scheme is applied to the Rake receiver with M fingers, M channel taps can be easily found to maximize the averaged signal power at the receiver output by exploiting the property of the channel- phase-precoded channel. It is shown by simulation results that the proposed scheme significantly outperforms the preceding system using one matched filter as proposed in [1].

Previous work demonstrated that the improvements in spectral efficiency and link reliability can be obtained in wireless relay networks employing terminals with MIMO capability. In order to take the advantage of MIMO relay networks while mitigate the complexity of decoding procedure, we propose a scheme that applies ZF relaying at the relay terminals and utilizes MMSE V-BLAST receiver at the destination terminal for two- hop half-duplex coherent MIMO relay networks. Considering the channel estimation is often imperfect due to the noise and interference, this paper extends the V-BLAST receiver design to the imperfect channel state information (CSI), assuming the channel estimation errors are independent complex Gaussian with zero mean and known second-order statistics. A robust MMSE nulling operator and its corresponding symbol detection ordering criterion is proposed for the MMSE V-BLAST receiver, which aims at providing, in each layer, the minimum average mean square error (MSE), over the random channel errors. The proposed ordering rule achieves global average MSE optimization which results in better performance than the previously proposed robust ZF V-BLAST. It has been shown that the proposed design exploits the characteristics of MIMO relay networks to provide good performance with respect to imperfect CSI while keeping a low complexity of the decoding procedure.

The channel phase precoding (CPP) technique was recently proposed for the ultra-wideband (UWB) communication system in [1] to save the feedback overhead and the computational complexity as compared with the time-reversal prefllter (TRP) technique [2]. Two ideal assumptions have been made in both systems; namely, the availability of accurate channel information and perfect synchronization of transmitted pulses. In this work, we examine the impact of timing jitter on the performance of TRP and CPP and show that the CPP-UWB system is more robust against the timing jitter than the TRP- UWB svstem.

Previous work demonstrated that the improvements in spectral efficiency and link reliability can be obtained in wireless relay networks employing terminals with MIMO capability. We propose to apply the non layered and layered ZF (V-BLAST) receiver to the relay and destination terminals in coherent MIMO relay networks under two-hop half-duplex relaying. Considering the channel estimation is often imperfect due to the noise and interference, this paper assumes that no perfect channel state information (CSI) is available for both backward and forward channels. The V-BLAST receiver is extended to the imperfect CSI under the assumption that the channel estimation errors are independent complex Gaussian random variables with zero mean and known second-order statistics. A robust symbol detection ordering criterion is proposed for applying ZF V-BLAST to the MIMO relay networks. This new ordering rule aims at providing, in each layer, the maximum average signal to interference- plus-noise ratio (SINR), over the random channel errors. Our simulations show that aided by the new ordering criterion, the suboptimal ZF operators, directly resulted from the imperfect CSI, is capable of alleviating the effect of CSI errors and improving the robustness of V-BLAST system.

In practice, channel estimation is often imperfect due to the noise and interference. This paper extends the V-BLAST system to the imperfect channel state information by dividing the channel matrix into two parts. Upon an assumption that the channel estimation error vectors are independent complex Gaussian with zero mean and known second-order statistics, a symbol detection ordering criteria for ZF V-BLAST system is proposed which aims at providing, in each layer, the maximum average signal to interference-plus-noise ratio (SINR), over the random channel errors. This robust V-BLAST ordering rule takes the imperfect CSI into account, while maintaining the simple implementation of the V-BLAST structure. It is shown that the new ordering criterion is capable of achieving global performance optimization and outperforms the standard ZF V-BLAST when estimation errors exist.

A preceding technique applied to symbols transmitted in an ultra-wideband (UWB) system was proposed, which can concentrate the signal power at the receiver to facilitate data detection. The design of the optimal codeword for preceding is investigated in this work. After the problem is formulated, we examine its solution in the full-rank space as well as the reduced-rank subspace to get different tradeoff between detection performance and the amount of feedback messages. Furthermore, we propose two subspace selection schemes that are suitable for different channel conditions. The codeword obtained from this work improves the performance of the result significantly at the price of a larger amount of feedback messages.

The optimal codeword length that generates the maximum output signal to interference ratio (SIR) for the channel-phase-precoded ultra-wideband (CPPUWB) system proposed in [1] is studied in this work to mitigate the interference in high data rate transmission. As compared with the proposed MMSE receiver in [1], this code length optimization technique demands no additional training symbols, which sacrifices the actual data rate, and maintains a simple receiver structure. This optimization problem is highly nonlinear in nature, and a fast search algorithm is developed to speed up the optimization process. The signal to interference plus noise ratio (SINR) performance of the proposed scheme degrades slightly when the input SNR is low, and it will converge to the maximum SINR as the input SNR becomes large.

In practical V-BLAST systems, channel estimation is often imperfect due to noise and interference. This paper analyzes the impacts of imperfect channel estimation on MMSE V-BLAST system under the assumption that the channel estimation errors are unknown deterministic and norm-bounded. Two novel V-BLAST ordering algorithms are therefore presented with the idea of worst-case post-processing performance optimization. The proposed algorithms organize the order of symbol processing according to their worst-case post-processing performance while maintain the simple implementation of the V-BLAST structure. The simulation results show our approaches are robust to the channel estimation errors.

A new indoor data communication scheme called the channel phase preceded ultra wideband (CPPUWB) system is proposed in this work. The proposed CPPUWB system is efficient in computational power saving by encoding the transmit symbol with a channelized codeword. The channelized codeword is determined by the channel phase information that is estimated at the receiver and then fed back to the transmitter. A method to estimate the channel phase information using training symbols is presented. For a given number of training symbols, we derive a lower bound for the average output SNR, which can be used to evaluate the system performance. Finally, an MMSE receiver is proposed to suppress the residual intersymbol interference (ISI) for the high data rate scenario

A reduced-rank space-time processing algorithm is proposed in this work to estimate the time-delay and the direction-of-arrival (DOA) in short-code chip-asynchronous DS-CDMA systems. Most algorithms proposed before demand the inversion of the received signal correlation matrix, which has a high computational cost. Here, we attempt to reduce the complexity by decomposing the correlation matrix inversion step into the cascade of multiple stages, where each stage outputs a scalar value that is obtained without an explicit matrix inversion operation. Furthermore, it is proved that the interference signals can be suppressed effectively using a small number of stages, and the performance is not much improved by applying more stages. Computer simulations are conducted to compare the performance of the reduced rank and the full rank approaches.

A blind time-delay and direction-of-arrival (DOA) estimation algorithm using beamforming arrays is proposed for short-code chip-asynchronous DS-CDMA systems. The proposed space-time processing technique consists of two stages. The time delay estimation process is performed at the first stage. Then, the soft information obtained from the first stage is used for DOA estimation at the second stage. The time-delay estimation algorithm is based on the minimum output energy (MOE) criterion. It consists of two steps, coarse and fine synchronization, where integer and fractional delays of the desired signal are estimated, respectively. The DOA estimation is performed based on the estimated power of the desired signal (or soft information) from each beamforming filter steering at a different direction. Experiments are conducted to demonstrate the superior performance of the proposed scheme.

Abstract — A novel full rate space-time block code that diagonalizes the channel matrix without the channel knowledge at the transmitter is proposed in this research. The main advantage of channel diagonalization is that it allows a simple detection scheme at the receiver end. Our method for channel matrix diagonalization consists of two steps. First, we propose a code structure that can transform the channel matrix to a circulant one. Then, the circulant channel matrix can be diagonalized using DFT and IDFT in the transmitter and the receiver, respectively. The proposed scheme not only diagonalizes any channel matrix with an even number of transmit antennas, but also guarantees that the real and imaginary parts of each received symbol are attenuated equally. Simulation results show that the proposed code structure outperforms the G4 code about 2 dB in an environment of four transmit and four receive antennas with the bit error probability equal to 10−6. Keywords — Space-time block code (STBC), full rate, channel diagonalization and MIMO systems. I.

A novel self-synchronizing receiver with a J-element antenna array is developed for asynchronous DS-CDMA multipath fading channels in this paper. The primary requirement is knowledge of the desired user's signature sequence. There's no training period of signal-free observations is required. Also, no information is presumed about the interfering users. Multipath diversity is exploited via two combining schemes, namely the maximal ratio combining (MRC) and the equal gain combining (ECG). A computationally efficient receiver was developed to utilize the concept of the truncated multistage Wiener filter (TMWF) introduced by Goldstein and Reed. This technique obviates the necessity of either a covariance matrix inversion or an eigen-decomposition. Moreover, this multistage scheme achieves a rapid adaptive convergence under limited observation-data support. Simulation results indicate that the proposed detector provides superior performance as an increasing function of the size of the J-element antenna array and is nearly independent on the number of signals.

An adaptive near-far-resistant self-synchronizing detector for asynchronous DS-CDMA systems with a J-element antenna array is presented in this paper. The primary requirement is knowledge of the spreading-code sequence of the desired user. A low complexity version of the proposed detector is developed that utilizes the concept of the multistage reduced-rank Wiener filter introduced by Goldstein and Reed (1998). This results in a self-synchronizing detection criterion that requires no inversion or eigen-decomposition of a covariance matrix. It also achieves a rapid adaptive convergence under limited data support. Simulation results show that the proposed detector provides superior performance both as an increasing function of the size of the J-element antenna array and the amount of sample support.

An adaptive near-far resistant detector using multiple antennas for acquiring synchronization of an asynchronous DS-CDMA system is presented. The only requirement is the prior knowledge of the user-spreading sequence of interest. Also, there is no need of a training sequence and a training period. Synchronization acquisition of asynchronous DS-CDMA signals is treated as a binary-hypothesis test. Under the binary hypotheses, an adaptive generalized maximum likelihood ratio test (GLRT) is developed to acquire the multipath code timings of a single desired user in both a fading and a near-far interference environment. The synchronization is obtained by sliding a search window in a sample-by-sample manner to perform the adaptive GLRT with the non-constrained maximum likelihood estimator (MLE) of direction-of-arrival (DOA) during the symbol interval. The acquisition performance of the proposed detector is illustrated by computer simulations of an asynchronous BPSK DS-CDMA system and is shown to have potential against multipath fading and the problems of near-far interference.

Multispectral or hyperspectral sensors can facilitate automatic target detection and recognition in clutter since natural clutter from vegetation is characterized by a grey body, and man-made objects, compared with blackbody radiators, emit radiation more strongly at some wavelengths. Various types of data fusion of the spectral-spatial features contained in multiband imagery developed for detecting and recognizing low-contrast targets in clutter appear to have a common framework. A generalized hypothesis test on the observed data is formulated by partitioning the received bands into two groups. In one group, targets exhibit substantial coloring in their signatures but behave either like grey bodies or emit negligible radiant energy in the other group. This general observation about the data generalizes the data models used previously. A unified framework for these problems, which utilizes a maximum likelihood ratio approach to detection, is presented. Within this framework, a performance evaluation and a comparison of the various types of multiband detectors are conducted by finding the gain of the SNR needed for detection as well as the gain required for separability between the target classes used for recognition. Certain multiband detectors become special cases in this framework. The incremental gains in SNR and separability obtained by using what are called target-feature bands plus clutter-reference bands are studied. Certain essential parameters are defined that effect the gains in SNR and target separability.

In Xu and Reed a matched-filter based detector was developed for the problem of detecting a 2-D target signal where prior information about the target pattern or template as well as the statistical properties of the clutter is limited. This was accomplished by an ad hoc substitution of the maximum likelihood estimate (MLE) of unknown clutter covariance matrix and the MLE's of the complex amplitudes of the significant features components of target into the matched filter test. The present paper provides a new approach for the problem based on the generalized likelihood ratio (GLR) principle which maximizes the GLR function over unknown clutter covariance matrix and the unknown significant feature components of target signal to be detected. This new GLR test is compared with the matched-filter based test in Xu and Reed for performance. The feature mapping and representation which can be incorporated into the test to characterize the unknown target pattern are various, including the short time Fourier transform, the discrete cosine transform, and the discrete wavelet transform.

A robust multi-spectral adaptive object detection algorithm is derived for a multiple spatial resolution and orientation decomposition image mode by using a wavelet transform. The second-order statistical moments of the wavelet transform are computed for a random image field and then used to develop a generalized maximum likelihood ratio test and to analyze detection performance. The computational cost of the new detector can be reduced substantially when compared to conventional spatial size and orientation matched filter-bank approach by using a coarse-to-fine spatial matching strategy.>