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Target detection in infrared remote sensing images has important practical applications. Among the current high‐performance methods, the deep learning‐based methods require training samples, and their generalization ability is also limited by the training set. The separation of low‐rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track‐before‐detect algorithms based on particle filtering also have high computational complexity. In this paper, the low‐rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

This retrospective study examines risk factors and electromyographic (EMG) characteristics associated with acquired weakness in critically ill patients and assesses their impact on patient prognosis. Methods: Ninety-seven critically ill patients, ventilated for over 48 hours, were included. Patient data, encompassing general condition, medical history, Medical Research Council (MRC) scores, serum markers (c-reactive protein, calcitonin gene, albumin, brain natriuretic peptide, urea nitrogen, creatinine), EMG characteristics, respiratory treatment modalities, and parameters, were recorded. Mechanical ventilation duration, ICU stay duration, hospitalization duration, and patient prognosis were documented. Based on MRC scores, patients were categorized into the ICU-acquired weakness (ICU-AW) group (MRC < 48 points) and the non-ICU-AW group (MRC ≥ 48 points). Results: The study comprised 47 ICU-AW and 50 non-ICU-AW patients. Significant differences (p < 0.05) were observed in age, MRC scores, albumin levels, c-reactive protein, calcitonin gene, brain natriuretic peptide, urea nitrogen, creatinine, mechanical ventilation duration, ICU stay duration, and hospitalization duration between groups. In the ICU-AW group, nerve conduction examinations revealed slow conduction velocity, reduced wave amplitude, and in severe cases, a complete loss of motor and sensory potentials. Multivariate logistic analysis identified low serum albumin levels and MRC scores as potential ICU-AW risk factors. Conclusion: This study suggests that low serum albumin levels and MRC scores may contribute to ICU-AW risk. The ICU-AW group exhibited varied peripheral nerve damage and slow conduction velocities on EMG. Additionally, severe systemic inflammatory responses, renal function, brain natriuretic peptide levels, prolonged mechanical ventilation, and peripheral nerve damage may be associated with ICU-AW. Follow-up studies are essential for further understanding these complex interactions. [ABSTRACT FROM AUTHOR]

Multi-target tracking using infrared images is receiving more and more attention. There are many state-of-the-art methods, and the deep learning network and low-rank and sparse matrix separation are two kinds of methods with high accuracy. However, the former suffers from heavy training samples, and the latter requires high-dimensional processing, meaning its computing cost is huge. In this work, a united detection and tracking method with matrix separation and PMBM filtering is proposed. In the detection process, a low-rank and sparse matrix separation algorithm with a differentiable form based on a single image is constructed. In the filtering process, the multi-target state is modeled as a PMBM distribution, which is conjugate in the Bayesian framework. The two processes interact mutually in that the detection provides measurements, and the filtering offers prior information for the next detection to improve accuracy. The computational complexity is given by a theoretical analysis, which shows a significant reduction. The numerical analysis, carried out on a practical dataset, verifies an enhancement in the BSF and SCRG metrics and ROC curves. [ABSTRACT FROM AUTHOR]