Reconfigurable microwave photonic mixer for hybrid macro-micro cellular systems.

Microwave photonic mixing can realize the frequency conversion of microwave signals in the optical domain, breaking through the bandwidth bottleneck and electromagnetic interference problems of traditional microwave mixing methods. In the background of the hybrid macro-micro cellular system, a wideband, large dynamic range and reconfigurable microwave photonic mixer is proposed, theoretically analyzed and experimentally demonstrated in this paper. By adjusting the modulator bias voltages and matching the proper digital domain operations, a microwave photonic mixer with reconfigurable functions including single-ended dispersion immune mixing, I/Q frequency down-conversion, image rejection mixing, and double-balanced mixing are realized, respectively. Meanwhile, optimizing the electrical attenuator using convex optimization can suppress the third-order intermodulation distortion (IMD3), maximize the conversion gain, and finally improve the spur-free dynamic range (SFDR). Experimental results show that the proposed scheme can be operated with a frequency from 5 to 20 GHz, and the SFDR can achieve 118.3 dB·Hz4/5. Over the whole frequency range, I/Q frequency down-conversion can be well conducted with an amplitude imbalance below 0.7 dB and a phase imbalance below ±0.7°. After an I/Q imbalance compensation algorithm, the image rejection ratio of over 60 dB is produced. The power fading caused by fiber dispersion is also compensated successfully. For a vector signal with 16 quadrature amplitude modulation, the best error vector magnitude (EVM) reaches 3.4%.

Photonic generation of frequency-tunable biphase and quadriphase coded pulse signals without background interference enabled by vector modulation and balanced detection.

An approach to generating frequency-tunable biphase and quadriphase coded pulse signals without background interference based on a polarization division multiplexing dual-parallel Mach-Zehnder modulator (PDM-DPMZM) is presented and demonstrated. Two ternary baseband code sequences are separately encoded into a pair of orthogonal optical carriers by exploiting a polyphase encoder on the basis of the principle of vector modulation, which in turn can be mapped to the phase shifts of the generated phase coded waveforms after the balanced detection. The frequency tunability can also be achieved by controlling the bias voltage of the associated modulator, so that the carrier frequency can be tuned to either fundamental or doubled frequency. Additionally, by designing different phase codes, the generated pulse signals can be conveniently switched between the quadriphase and biphase coding waveforms. The major advantage of the proposed approach is that four phase shifts can be obtained by simply adjusting the polarity of the ternary code sequences, overcoming the power-dependent limitation of the previous work. A proof-of-principle experiment is conducted to assess the feasibility of the proposed approach built on the Barker code and Frank code phase coded pulse signals generation. Experimental results show the phase coded pulse signals at 12 and 24 GHz carrier frequency are well behaved in terms of peak-to-sidelobe ratio (PSR), range-Doppler coupling and Doppler tolerance.

DTI Atlases Evaluations.

The cerebral atlas of diffusion tensor magnetic resonance image (DT-MRI, shorted as DTI) is one of the key issues in neuroimaging research. It is crucial for comparisons of neuronal structural integrity and connectivity across populations. Usually, the atlas is constructed by iteratively averaging the registered individual image. In tradition, the fuzzy group average image is easily generated in the initial stage, which is harmful to providing clear guidance for subsequent registration, to the performance of the final atlas. To solve this problem, an improved unbiased DTI atlas construction algorithm based on adaptive weights is proposed in this paper. The adaptive weighted strategy based on diffeomorphic deformable tensor registration is introduced. At the same time, the distance measure for tensors is used as a constraint condition, which ensures the unbiasedness of the atlas. Then, using 77 DTIs from the dataset in http://www.brain-development.org , three study-specific atlases, i.e. the constructed atlases of the proposed algorithm and two open-sourced algorithms (DTIAtlasBuilder and DTI-TK), are compared with two standardized atlases (IIT v. 4.1 and NTU-DSI-122-DTI). The performances of the atlases were evaluated in spatial normalization way with six region-based criteria (including Euclidean distances between diffusion tensors, Euclidean distances of the deviatoric tensors, standard deviation, overlaps of eigenvalue-eigenvector, cross-correlations and three sets angles of eigenvalue-eigenvector pairs between diffusion tensors) and three fiber-based criteria (including distances between fiber bundles, angles between fiber bundles and fiber property profile-based criteria). The experimental results showed that the overall performances of the study-specific atlases are better than those of the standardized atlases for specific datasets, and the comprehensive performance of the improved algorithm proposed in this paper is the best.

Linearity optimization of multi-octave analog photonic links based on power weighting, polarization multiplexing and bias control.

We present and demonstrate an approach to linearizing analog photonic links (APLs) with substantially enhanced multi-octave spurious-free dynamic range (SFDR). Combining with power weighting, polarization multiplexing and bias control techniques, the proposed approach enables the second-order harmonic distortion (HD2) and third-order intermodulation distortion (IMD3) to be suppressed simultaneously. To maximize the RF output power, an optimization model is established. The simulation results indicate that the maximum RF power can be attained when the power weighting factor and polarization incident angle are equal to 0.5 and 0.34 radians, respectively. The link is validated with a proof-of-principle experiment. The third-order SFDR is 112.3 dB·Hz2/3, corresponding to the improvement of 15.5 dB as compared with a quadrature-biased link. The second-order SFDR reaches as high as 94.6 dB·Hz1/2. Furthermore, the adjacent channel power ratio (ACPR) is measured to be up to 54.6 dBc, which is 5.4 dB greater than that of a quadrature-biased link. Finally, the system tolerances for the RF and optical input power are also investigated in terms of error vector magnitudes (EVMs). Therefore, by introducing optimization model, our scheme provides further insight into the APL linearization technique and a better performance is also achieved.

Analog radio-over-fiber links with a high SFDR for passive distributed antenna system applications using Lagrange multiplier optimization.

An analog radio-over-fiber scheme with a high spurious-free dynamic range (SFDR) is proposed for constructing a passive distributed antenna system (DAS). By developing a Lagrange multiplier constrained optimization model, the best trade-off among RF output power, the polarization incident angle, and the RF power ratio is obtained. Consequently, the third-order intermodulation distortion and second-order harmonic distortion can be suppressed simultaneously simply by varying the polarization incident angle. The simulated and experimental results show that the proposed scheme is effective and feasible. Additionally, this Letter offers valuable insights into the nonlinear optimization, and it may be of great significance in future design and manufacture.

Analysis of channel correlation and channel capacity for indoor MIMO visible light communication systems.

In indoor multi-input-multi-output (MIMO) visible light communication (VLC) systems, spatial multiplexing (SMP) is employed to improve spectral efficiency. However, the performance of SMP in an indoor VLC system depends on a low channel correlation. In this paper, a receiver model with angular diversity detectors is considered. The objective is to reduce the channel correlation and hence the system performance in terms of bit error rate (BER) and channel capacity compared to vertically oriented detectors under no-line-of-sight (NLOS) channel conditions. For a vertical detector setup, the results show that the channel correlation cannot be further reduced by varying the transmitter separation, transmitter semi-angle, or field of view of the receiver in NLOS conditions due to the design of receiver separation being very small in small mobile devices. In comparison to vertical detector setups, by varying the detector axis of each photodetector (PD) detector axis in angular diversity detector setups, the channel matrix rank is improved under LOS conditions, and the channel correlation is effectively reduced under NLOS conditions without requiring any implementation complexity at the receiver. Therefore, it is found that the angular diversity detector setup can substantially improve the BER performance of SMP, since it makes each PD more spatially separated to improve channel conditions. Notably, we deduce the channel capacity expression to approximate the capacity of the indoor highly correlated MIMO channel and verify the theoretical analysis by numerical simulations. The results show that the angular diversity detector setup provides capacity improvement when compared with the vertical detector setup. Even though it diminishes the received power when the elevation angle exceeds the optimal elevation angle, it outweighs this degradation by providing reduced channel correlation.

A Method for Analyzing the Impact of Intra-System and Inter-System Interference on DME Based on Queueing Theory.

In order to use Distance Measuring Equipment (DME) properly, the impact of intra-system and inter-system electromagnetic interference must be analyzed firstly. However, the error of interference analysis using present methods based on pulse overlap is large when there are more aircraft. The aim of this article is to study a method of analyzing interference on DME whether the number of aircraft is small or not. According to the flow chart of DME signal, we studied the limitations of present methods; then constructed a model of analyzing the collision between duration of desired signal and dead time of receiver based on M/M/1/0 queueing system. Combing this model with other methods, we present a analytic model of analyzing intra-system and inter-system interference on DME. Using this analytic model, we analyzed reply efficiency (RE) and capacity of DME under intra-system and Joint Tactical Information Distribution System (JTIDS) interference. The result shows that the calculation for the probability of overlap between DME dead time and subsequent signals using queueing model agrees well with simulation. Consequently, the analytic model is more accurate than using a single method to analyze interference on DME.

Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector.

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K ≤ ( M 4 - 2 M 3 + 7 M 2 - 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

All-optical and broadband microwave fundamental/sub-harmonic I/Q down-converters.

Microwave I/Q down-converters are frequently used in image-reject super heterodyne receivers, zero intermediate frequency (zero-IF) receivers, and phase/frequency discriminators. However, due to the electronic bottleneck, conventional microwave I/Q mixers face a serious bandwidth limitation, I/Q imbalance, and even-order distortion. In this paper, photonic microwave fundamental and sub-harmonic I/Q down-converters are presented using a polarization division multiplexing dual-parallel Mach-Zehnder modulator (PDM-DPMZM). Thanks to all-optical manipulation, the proposed system features an ultra-wide operating band (7-40 GHz in the fundamental I/Q down-converter, and 10-40 GHz in the sub-harmonic I/Q down-converter) and an excellent I/Q balance (maximum 0.7 dB power imbalance and 1 degree phase imbalance). The conversion gain, noise figure (NF), even-order distortion, and spurious free dynamic range (SFDR) are also improved by LO power optimization and balanced detection. Using the proposed system, a high image rejection ratio is demonstrated for a super heterodyne receiver, and good EVMs over a wide RF power range is demonstrated for a zero-IF receiver. The proposed broadband photonic microwave fundamental and sub-harmonic I/Q down-converters may find potential applications in multi-band satellite, ultra-wideband radar and frequency-agile electronic warfare systems.

Evaluations of diffusion tensor image registration based on fiber tractography.

BACKGROUND: Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development. METHODS AND RESULTS: In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template. CONCLUSION: All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it.

DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning.

Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.

Level set segmentation of medical images based on local region statistics and maximum a posteriori probability.

This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.

Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation.

This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy c-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the corresponding tissues. Therefore, segmentation and bias field estimation are simultaneously achieved by minimizing the global fuzzy energy. Besides, to reduce the impact of noise, the proposed algorithm incorporates spatial information into the membership function using the spatial function which is the summation of the membership functions in the neighborhood of each pixel under consideration. Experimental results on synthetic and real images are given to demonstrate the desirable performance of the proposed algorithm.