Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte.

The morphological analysis test item of urine red blood cells is referred to as "extracorporeal renal biopsy," which holds significant importance for medical department testing. However, the accuracy of existing urine red blood cell morphology analyzers is suboptimal, and they are not widely utilized in medical examinations. Challenges include low image spatial resolution, blurred distinguishing features between cells, difficulty in fine-grained feature extraction, and insufficient data volume. This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification performance for low-resolution urine red blood cells with only category labels required. This method can serve as a practical reference for urine red blood cell morphology examination items.

Real-time self-calibration PGC-Arctan demodulation algorithm in fiber-optic interferometric sensors.

Fiber-optic interferometric sensors (FOISs) are widely used in seismometers, hydrophones, and gyroscopes. The arctangent approach of phase-generated carrier (PGC-Arctan) demodulation algorithm is one of the key demodulation techniques in FOISs. The conventional PGC-Arctan demodulation algorithm requires the specific value of the phase modulation depth C to work properly. However, C will variate with laser wavelength, temperature, and humidity in the actual working environment, which leads to harmonic distortion and even demodulation failure. In this paper, a novel PGC demodulation algorithm called self-calibration PGC-Arctan (PGC-Arctan-SC) demodulation algorithm is presented. The proposed algorithm can jointly estimate the accurate C value by the elliptical parameters and C-related components while suppressing nonlinear distortion by ellipse fitting algorithm (EFA). Then C can be calibrated to the specific predefined optimal value by the closed-loop proportion integration differentiation (PID) module. The simulation results are consistent with theoretical analysis, and the all-digital PGC-Arctan-SC demodulation system is implemented on the embedded SoC. The experimental results show that C can be estimated and calibrated accurately in real time. The signal-to-noise and distortion ratio (SINAD) of the PGC-Arctan-SC demodulation output achieves 61.57 dB.

High-resolution distributed polarization crosstalk measurement for polarization maintaining fiber with considerable dispersion.

We present a high-resolution polarization crosstalk measurement method for polarization maintaining fiber (PMF) with considerable dispersion. The birefringence dispersion of the PMF severely degrades the spatial resolution of the distributed polarization crosstalk measurement. Conventional dispersion compensation methods are effective for modest birefringence dispersion coefficients (for instance, of 0.0014 ps/nm/km). We present an iterative matched filter (IMF) method to cope with the case of considerable birefringence dispersion. We measured the distributed polarization crosstalk of a PMF coil with a birefringence dispersion coefficient of 0.235 ps/nm/km. By applying the IMF method, we obtained a spatial resolution of 0.09 m at any position of the PMF (a maximum of 12.36 m without dispersion compensation).

A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays.

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation.

Nested arrays are considered attractive due to their hole-free performance, and have the ability to resolve O ( N 2 ) sources with O ( N ) physical sensors. Inspired by nested arrays, two kinds of three-parallel nested subarrays (TPNAs), which are composed of three parallel sparse linear subarrays with different inter-element spacings, are proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation in this paper. We construct two cross-correlation matrices and combine them as one augmented matrix in the first step. Then, by vectorizing the augmented matrix, a hole-free difference coarray with larger degrees of freedom (DOFs) is achieved. Meanwhile, sparse representation and the total least squares technique are presented to transform the problem of 2-D DOA searching into 1-D searching. Accordingly, we can obtain the paired 2-D angles automatically and improve the 2-D DOA estimation performance. In addition, we derive closed form expressions of sensor positions, as well as number of sensors for different subarrays of two kinds of TPNA to maximize the DOFs. In the end, the simulation results verify the superiority of the proposed TPNAs and 2-D DOA estimation method.

Quadratic Frequency Modulation Signals Parameter Estimation Based on Two-Dimensional Product Modified Parameterized Chirp Rate-Quadratic Chirp Rate Distribution.

In an inverse synthetic aperture radar (ISAR) imaging system for targets with complex motion, the azimuth echo signals of the target are always modeled as multicomponent quadratic frequency modulation (QFM) signals. The chirp rate (CR) and quadratic chirp rate (QCR) estimation of QFM signals is very important to solve the ISAR image defocus problem. For multicomponent QFM (multi-QFM) signals, the conventional QR and QCR estimation algorithms suffer from the cross-term and poor anti-noise ability. This paper proposes a novel estimation algorithm called a two-dimensional product modified parameterized chirp rate-quadratic chirp rate distribution (2D-PMPCRD) for QFM signals parameter estimation. The 2D-PMPCRD employs a multi-scale parametric symmetric self-correlation function and modified nonuniform fast Fourier transform-Fast Fourier transform to transform the signals into the chirp rate-quadratic chirp rate (CR-QCR) domains. It can greatly suppress the cross-terms while strengthening the auto-terms by multiplying different CR-QCR domains with different scale factors. Compared with high order ambiguity function-integrated cubic phase function and modified Lv's distribution, the simulation results verify that the 2D-PMPCRD acquires higher anti-noise performance and obtains better cross-terms suppression performance for multi-QFM signals with reasonable computation cost.

Measurement error analysis for polarization extinction ratio of multifunctional integrated optic chips.

Measurement error for the polarization extinction ratio (PER) of a multifunctional integrated optic chip (MFIOC) utilizing white light interferometry was analyzed. Three influence factors derived from the all-fiber device (or optical circuit) under test were demonstrated to be the main error sources, including: 1) the axis-alignment angle (AA) of the connection point between the extended polarization-maintaining fiber (PMF) and the chip PMF pigtail; 2) the oriented angle (OA) of the linear polarizer; and 3) the birefringence dispersion of PMF and the MFIOC chip. Theoretical calculations and experimental results indicated that by controlling the AA range within 0°±5°, the OA range within 45°±2° and combining with dispersion compensation process, the maximal PER measurement error can be limited to under 1.4 dB, with the 3σ uncertainty of 0.3 dB. The variations of birefringence dispersion effect versus PMF length were also discussed to further confirm the validity of dispersion compensation. A MFIOC with the PER of ∼50 dB was experimentally tested, and the total measurement error was calculated to be ∼0.7 dB, which proved the effectiveness of the proposed error reduction methods. We believe that these methods are able to facilitate high-accuracy PER measurement.

Real-Valued 2D MUSIC Algorithm Based on Modified Forward/Backward Averaging Using an Arbitrary Centrosymmetric Polarization Sensitive Array.

Two-dimensional multiple signal classification (MUSIC) algorithm based on polarization sensitive array (PSA) has excellent performance. However, it suffers a high computational complexity due to a multitude of complex operations. In this paper, we propose a real-valued two-dimensional MUSIC algorithm based on conjugate centrosymmetric signal model, which is applicable to arbitrary centrosymmetric polarization sensitive array. The modified forward/backward averaging, which can be applied to the PSA, is presented. Hence, the eigen-decomposition analysis process and spectrum function computation are converted into real domain, prominently reducing the computational complexity. Then, the direction-of-arrival (DOA) estimation is decoupled from the polarization parameter estimation so that the four-dimensional spectral peak search process is avoided. The theoretical computational complexity is discussed and the Cramer-Rao bound (CRB) of DOA estimation is derived in this paper. The simulation results indicate that the proposed algorithm achieves superior accuracy in DOA estimation and has low computational complexity.

Analysis of Maneuvering Targets with Complex Motions by Two-Dimensional Product Modified Lv's Distribution for Quadratic Frequency Modulation Signals.

For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF) and modified scaled Fourier transform (mSFT), an effective parameter estimation algorithm is proposed-referred to as the Two-Dimensional product modified Lv's distribution (2D-PMLVD)-for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT) and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified.