A Robust Algorithm for Shadow Removal of Foreground Detection

Abstract-We proposed an accurate algorithm to prevent moving shadows from being misclassified as part of moving objects in video target segmentation in this paper. Firstly, moving objects were achieved through background subtraction using adaptive Gaussian mixture models. Then, moving shadows were eliminated by a shadow detection algorithm. Finally, we performed a morphological reconstruction algorithm to recover the foreground distorted after shadow removal process. The experimental results proved its validity and accuracy in various fixed outdoor video scenes

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

BackgroundRecent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3–6 months post-stroke.MethodsA systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters.ResultsA total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively.ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260

Optimization of Spoken Term Detection System

Generally speaking, spoken term detection system will degrade significantly because of mismatch between acoustic model and spontaneous speech. This paper presents an improved spoken term detection strategy, which integrated with a novel phoneme confusion matrix and an improved word-level minimum classification error (MCE) training method. The first technique is presented to improve spoken term detection rate while the second one is adopted to reject false accepts. On mandarin conversational telephone speech (CTS), the proposed methods reduce the equal error rate (EER) by 8.4% in relative

Sustainable port development: the role of Chinese seaports in the 21st century Maritime Silk Road

International audienceWe evaluate the respective roles, functions and prospects of the 15 Chinese ports, intended to be the 'eastern end' of China's Belt and Road Initiative (BRI). To achieve this, and thus contribute to the future development of BRI, we introduce the concept of sustainable development capability. We employ principal component analysis and analytic hierarchy process to build a model which evaluates the (cooperative) sustainability of the ports, based on four dimensions: capacity of port operations; (ambient) economic conditions; environmental factors; and human intellect and technology (HIT). Sensitivity and cluster analysis are used, to classify the ports into four categories (respective roles): international hub ports, regional hub ports, node ports, and regional gate ports. We hope that the port system we present and assess here will provide guidance to ports and countries, especially in Europe, leading to the right 'port alliances', that could turn BRI into an efficient global transportation system

The Feasibility of 3D Printing Technology on the Treatment of Pilon Fracture and Its Effect on Doctor-Patient Communication

Purpose. The aim of this study was to assess the feasibility and effectiveness of the three-dimensional (3D) printing technology in the treatment of Pilon fractures. Methods. 100 patients with Pilon fractures from March 2013 to December 2016 were enrolled in our study. They were divided randomly into 3D printing group (n=50) and conventional group (n=50). The 3D models were used to simulate the surgery and carry out the surgery according to plan in 3D printing group. Operation time, blood loss, fluoroscopy times, fracture union time, and fracture reduction as well as functional outcomes including VAS and AOFAS score and complications were recorded. To examine the feasibility of this approach, we invited surgeons and patients to complete questionnaires. Results. 3D printing group showed significantly shorter operation time, less blood loss volume and fluoroscopy times, higher rate of anatomic reduction and rate of excellent and good outcome than conventional group (P<0.001, P<0.001, P<0.001, P=0.040, and P=0.029, resp.). However, no significant difference was observed in complications between the two groups (P=0.510). Furthermore, the questionnaire suggested that both surgeons and patients got high scores of overall satisfaction with the use of 3D printing models. Conclusion. Our study indicated that the use of 3D printing technology to treat Pilon fractures in clinical practice is feasible

Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results

Niosome-Assisted Delivery of DNA Fluorescent Probe with Optimized Strand Displacement for Intracellular MicroRNA21 Imaging

MicroRNAs play a vital role in cancer development and are considered as potential biomarkers for early prognostic assessment. Here, we propose a novel biosensing system to achieve fluorescence imaging of miRNA21 (miR21) in cancer cells. This system consists of two components: an optimized &ldquo;off-on&rdquo; double-stranded DNA (dsDNA) fluorescent for miR21 sensing by efficient strand-displacement reaction and a potent carrier vesicle, termed niosome (SPN), to facilitate the efficient intracellular delivery of the dsDNA probe. A series of dsDNA probes based on fluorescence energy resonance transfer (FRET) was assembled to target miR21. By optimizing the appropriate length of the reporter strand in the dsDNA probe, high accuracy and sensitivity for miR21 recognition are ensured. To overcome the cellular barrier, we synthesized SPN with the main components of a nonionic surfactant Span 80 and a cationic lipid DOTAP, which could efficiently load dsDNA probes via electrostatic interactions and potently deliver the dsDNA probes into cells with good biosafety. The SPN/dsDNA achieved efficient miR21 fluorescent imaging in living cells, and could discriminate cancer cells (MCF-7) from normal cells (L-02). Therefore, the proposed SPN/dsDNA system provides a powerful tool for intracellular miRNA biosensing, which holds great promise for early cancer diagnosis

Recursively Learning Causal Structures Using Regression-Based Conditional Independence Test

This paper addresses two important issues in causality inference. One is how to reduce redundant conditional independence (CI) tests, which heavily impact the efficiency and accuracy of existing constraint-based methods. Another is how to construct the true causal graph from a set of Markov equivalence classes returned by these methods.For the first issue, we design a recursive decomposition approach where the original data (a set of variables) is first decomposed into three small subsets, each of which is then recursively decomposed into three smaller subsets until none of subsets can be decomposed further. Consequently, redundant CI tests can be reduced by inferring causality from these subsets. Advantage of this decomposition scheme lies in two aspects: 1) it requires only low-order CI tests, and 2) it does not violate d-separation. Thus, the complete causality can be reconstructed by merging all the partial results of the subsets.For the second issue, we employ regression-based conditional independence test to check CIs in linear non-Gaussian additive noise cases, which can identify more causal directions by x−E(x|Z)⊥z (or y−E(y|Z)⊥z). Therefore, causal direction learning is no longer limited by the number of returned Vstructures and the consistent propagation.Extensive experiments show that the proposed method can not only substantially reduce redundant CI tests but also effectively distinguish the equivalence classes, thus is superior to the state of the art constraint-based methods in causality inference

Ultra-Sensitive Intensity Modulated Strain Sensor by Tapered Thin-Core Fiber Based Modal Interferometer

In this paper, to enhance practicality, a novel tapered thin-core fiber (t-TCF) based modal interferometer is proposed and demonstrated experimentally. The light field distribution of t-TCF structure is investigated by a beam propagation method, and the quantitative relationship is gained between light intensity loss and waist diameter. Under ~30 μm waist diameter, multiple t-TCF based sensor heads are fabricated by arc-discharged splicing and taper techniques, and comprehensive tests are performed with respects to axial strain and temperature. The experimental results show that, with near-zero wavelength shift, obvious intensity strain response is exhibited and negative-proportional to the reduced length of TCF. Thus, the maximum sensitivity reaches 0.119 dB/με when the TCF length is equal to 15 mm, and a sub-micro-strain detection resolution (about 0.084 με) is obtained. Besides, owing to the flat red-shifted temperature response, the calculated cross-sensitivity of our sensor is compressed within 0.32 με/°C, which is promising for high precision strain related engineering applications