Multi-Camera Multi-Target Tracking (MCMT) aims to achieve robust identity association of objects across cameras under diverse and challenging real-world conditions, such as varying viewpoints and occlusions. Current mainstream approaches often rely on deploying large-scale models with extensive feature extraction capabilities. However, the high computational demands of these models make them impractical for real-world MCMT scenarios where efficiency is critical. To address these challenges, we propose a novel Dual-Retrieval Knowledge Distillation (DRKD) framework, which enhances the student’s feature representation learning by leveraging multi-teacher guidance and dual-retrieval optimization. Unlike traditional knowledge distillation (KD) methods focusing primarily on feature alignment, DRKD introduces the Cross Triplet Loss, which optimizes the feature space for cross-camera identity association by enhancing intra-class compactness and inter-class separability. This dual-retrieval optimization ensures that the student model learns from the teacher’s feature representations and develops strong retrieval capabilities, which are crucial for robust identity association in MCMT. Additionally, we present the Dynamic Feature Fusion (DFF) module, which integrates short-term and historical features to balance the trade-off between responsiveness in single-camera tracking (SCT) and stability in multi-camera tracking (MCT). To further improve adaptability, we design Scene-Aware Regularization, which dynamically adjusts feature contributions in DFF based on temporal gaps and appearance variations. Extensive experiments on the HST and AI City Challenge S02 datasets demonstrate the effectiveness of our approach. The proposed DRKD framework with DFF achieves state-of-the-art performance, with IDF1 scores of 67.13 and MOTA scores of 60.50, while maintaining high inference speeds of up to 62 FPS. These results highlight the ability of DRKD to combine accuracy, robustness, and efficiency, making it a promising solution for real-world MCMT applications.

Versatile Video Coding (VVC) employs Affine Motion Compensation (AMC) to process scenes with high-order motion. To improve AMC efficiency, the Affine Motion Estimation (AME) process based on the gradient-based iterative algorithm (GIA) and block match algorithm (BMA) is introduced to the VVC Test Model (VTM). However, the AME process is highly complex and difficult for hardware implementation in real-time applications. In this context, this paper proposes a hardware-friendly AME algorithm and implements the corresponding accelerator. Firstly, the weighted least squares regression is used to reduce the iteration of GIA. Then an iteration-free search scheme is proposed to remove the search dependence during the GIA and BMA process. In addition, a motion vector clamping mechanism and four-level memory organization are proposed to solve the problem of reference pixel reading conflict, which reduces 51.7% and 67.5% internal bandwidth of the AME accelerator. Compared with the default AME process of VTM 16.0, experimental results show that the proposed algorithm reduces AME run time by 81.63% while the corresponding Bjontegaard Delta Bit Rate (BDBR) loss is only 0.492%. The proposed AME accelerator can flexibly support AME search tasks in various configurations. Synthesized with the TSMC 28nm process, the proposed architecture has a gate count of 1313K and a power consumption of 156.83 mW. It can achieve$7680\times [email protected]~30fps and the corresponding BDBR loss is 0.492%~1.835%.

Both Versatile Video Coding (VVC) and High Efficiency Video Coding (HEVC) introduce Group of Pictures (GOP) based temporal filter (GBTF) as a pre-filter to improve compression performance. While numerous efforts have been made to optimize GBTF, there is a limited amount of research that explicitly addresses why GBTF could improve compression performance. Additionally, most optimizations have focused on the design of the filter itself, rather than on how to better integrate it with other encoding tools. In this paper, we analyze the reasons behind the superior compression performance of GBTF. Subsequently, we introduce a Coding Tree Unit (CTU)-level adaptive quantization parameter allocation method joint with GBTF to further enhance compression performance for video coding. The experimental results demonstrate that, for VVC, our method provides Bjontegaard delta bit rate (BD-BR) savings of 2.0% for Peak Signal-to-Noise Ratio (PSNR) and 4.0% for Structural Similarity index (SSIM). Furthermore, for HEVC, our method provides BD-BR savings of 3.5% for PSNR and 7.6% for SSIM.

The Ground-Cartesian factorized back-projection (G-CFBP) is an efficient time-domain processing algorithm without image interpolation, and can realize accurate imaging for curved trajectory synthetic aperture radar (SAR). Its superiority shows good potential in airborne circular SAR (CSAR) imaging. However, the motion compensation (MoCo) based on ground Cartesian back-projection (GCBP) in the airborne CSAR is still a challenge. There are two main problems: one is that the existence of the image spectrum aliasing makes the processing of the phase error estimation inaccurate; the other is that the mapping relationship of the phase error between the image spectrum domain and the azimuth time domain still needs to be studied within GCBP processing chain. To tackle the above two problems, a novel MoCo method applicable to the GCBP algorithm is proposed and can be mainly divided into two steps: the first step is to remove the sub-aperture image spectrum aliasing by a spectrum compression operation; the second step is to establish an analytical phase error structure, which includes an auto-selection criterion of the effective support region for GCBP image. The first step ensures the accuracy of the phase error estimation, and the second step establishes the inverse-mapping relationship of the phase error between the image spectrum and azimuth time. These two procedures are both vital in improving the accuracy and robustness of the GCBP-based MoCo for the CSAR imaging. The processed results of simulated and real data are provided to verify the effectiveness of the proposed method.

MOTIVATION: High-dimensional mass cytometry (CyTOF), which provides both cellular signatures and inter-cluster interactions like the antagonism between immune activation and suppression, and the pro-inflammatory synergy, sheds light on the cellular and molecular basis of disease pathogenesis. However, revealing the aberrance of inter-cluster communication networks in CyTOF datasets remains a significant challenge. RESULTS: Here, we developed Sample Classification and direct Association Network among Cell clusters (SCANCell) that quantifies the direct association (DA) network of cell clusters. SCANCell was applied to profile inter-cluster interaction patterns of a well-recruited systemic lupus erythematosus (SLE) cohort, including 8 healthy controls, 10 active SLE patients (APs) and 8 remission SLE patients (RPs). SCANCell identified decreased inter-cluster interactions of CD8+ T cells in APs compared with RPs, and enhanced DA of CD8+ T cells after stimulation with immunostimulatory cytokine interleukin-2 in vitro. These discoveries prove that SCANCell can uncover pathology- and drug stimulation-associated inter-cluster interactions, which potentially benefits understanding of pathogenesis and novel therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The main processing scripts of SCNACell are available at https://github.com/Lxc417/SCANCell. Other codes for the following data statistics are available from the corresponding author upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conventional beam-center approximation-based motion compensation (MOCO) algorithms fail to achieve an optimally focused image in the case of the high-resolution and high-frequency (HRHF) synthetic aperture radar (SAR) system. In this letter, a novel MOCO algorithm based on Doppler adjustment is developed with the ability to compensate the azimuth variant motion error. The change of the Doppler spectrum caused by the azimuth variant motion error is investigated and is eliminated by Doppler scaling. The proposed MOCO algorithm has dramatically improved precision when compared with the conventional MOCO methods in HRHF SAR imaging. Simulation experiments and extensive comparisons with other MOCO algorithms verify the effectiveness of the proposed algorithm.

High-resolution wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system possesses a number of receiving channels along the azimuth direction, so it has the capacity of moving target indication and imaging. However, due to the radial velocity of the moving target, false targets occur in the focused image. By estimating the radial velocity and combining it with moving target imaging, false targets can be effectively suppressed. In this letter, a method of radial velocity estimation of a moving target is proposed based on the theory of subspace projection. This method does not need to estimate the real azimuth position of the moving target and can predict the processing time. Simulation and airborne measured data show the effectiveness of the proposed method.

Geosynchronous-Earth-orbit (GEO) synthetic aperture radar (SAR) provides excellent continuous observing capability and large swath. However, the extremely long synthetic aperture time, the curved orbit, and the nonplanar ground surface cause serious spatial variance in the GEO SAR signal. In this article, a novel fast Cartesian back-projection (BP) algorithm based on subaperture imaging on ground and multistage fusion is proposed for accurately and efficiently imaging of GEO SAR. The imaging grids are arranged on the ground surface to avoid the azimuth defocusing caused by the flat ground approximation. Then, a new two-step spectrum compression method is derived to solve the spectrum aliasing of subaperture images. Also, a multistage image fusion method is adopted to combine all the subaperture images with high efficiency. The computational complexity and the approximation of the proposed algorithm are also discussed. Simulation results verify the effectiveness of the proposed algorithm.

For ultra-high resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultra-high resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation for ultra-high resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm.

This article presents a fast back-projection (BP) algorithm based on subaperture (SA) image coherent combination in a downsampled Cartesian coordinate grid for high squint diving terrain observation by progressive scans (HSD-TOPS) synthetic aperture radar (SAR) ground plane imaging. A two-step spectrum compression (SC) method is proposed to coherently combine the aliasing SA images by exploiting the relationship between the wavenumber and the image frequency. The first-step SC is introduced to align the spectrum support region centers. The second-step SC effectively corrects the space-variant spectrum inclination. The proposed algorithm does not need interpolation in the process of image combination, which ensures the accuracy and the efficiency of the algorithm. Furthermore, the SC method is well-modified to suppress the sidelobes of the focused image. Simulation and measured data processing verify the effectiveness of the proposed method.

Fast back projection algorithm (FBPA) is commonly used for image formation of complex SAR mode. However, traditional sidelobe reduction algorithm is not applicable to remove the image sidelobes because the spectrum of the image formed by FBPA is aliased. In this paper, a novel sidelobe reduction algorithm is proposed based on spectrum compression (SC). The spectrum aliasing is eliminated by SC first. Then a modified spatial variant apodization (SVA) is used for sidelobe suppression. The mainlobe preserves without widening and the sidelobe is suppressed. Simulation and measured data processing verify the effectiveness of the proposed method.

According to the main shortcoming of the traditional Maximum Likelihood Estimation(MLE) based algorithm for its high computational complexity, a new fast wind vector retrieval algorithm for SeaWinds Scatterometer is derived in this paper. The new fast algorithm adopts wind speed standard deviation instead of objective function as its criterion for searching possible wind vector solutions, which leads to lower complexity than traditional MLE algorithm. In order to further reduce retrieval computations, the new algorithm is implemented by a two-step method. First step accomplishes a coarse searching for likely wind vector solutions, while the second step functions as a fine adjustment for each coarse solution. Using some SeaWinds L2A and corresponding L2B data, the new algorithm is validated. The results indicate good performance and high retrieval accuracy of the new algorithm for experiment data.