Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm

Infrared small target detection in an infrared search and track (IRST) system is a challenging task. This situation becomes more complicated when high gray-intensity structural backgrounds appear in the field of view (FoV) of the infrared seeker. While the majority of the infrared small target detection algorithms neglect directional information, in this paper, a directional approach is presented to suppress structural backgrounds and develop a more effective detection algorithm. To this end, a similar concept to the average absolute gray difference (AAGD) is utilized to construct a novel directional small target detection algorithm called absolute directional mean difference (ADMD). Also, an efficient implementation procedure is presented for the proposed algorithm. The proposed algorithm effectively enhances the target area and eliminates background clutter. Simulation results on real infrared images prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal

Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps

This paper proposes a fast and accurate surface normal estimation method which can be directly used on depth maps (organized point clouds). The surface normal estimation process is formulated as a closed-form expression. In order to reduce the effect of measurement noise, the averaging operation is utilized in multi-direction manner. The multi-direction normal estimation process is reformulated in the next step to be implemented efficiently. Finally, a simple yet effective method is proposed to remove erroneous normal estimation at depth discontinuities. The proposed method is compared to well-known surface normal estimation algorithms. The results show that the proposed algorithm not only outperforms the baseline algorithms in term of accuracy, but also is fast enough to be used in real-time applications

Association of Helicobacter pylori infection with the risk of metabolic syndrome and insulin resistance : an updated systematic review and meta-analysis

Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Peer reviewedPublisher PD

Helicobacter pylori infection as a risk factor for diabetes: A meta-analysis of case-control studies

Background: There are several studies with varied and mixed results about the possible relationship between H. pylori and diabetes. Therefore, this current meta-analysis performed to determine the association between H. pylori infection and the risk of diabetes mellitus. Methods: A systematic literature searches of international databases, including Medline (PubMed), Web of Sciences, Scopus, EMBASE, and CINHAL (January 1990-March 2019) was conducted to identify studies investigating the relationship between H. pylori infection and diabetes mellitus. Only case-control studies were analyzed using odds ratio (OR) with 95 confidence intervals (CIs). Stratified and subgroup analyses were performed to explore heterogeneity between studies and assess effects of study quality. Logarithm and standard error logarithm odds ratio (OR) were also used for meta-analysis. Results: A total of 41 studies involving 9559 individuals (case; 4327 and control; 5232) were analyzed. The pooled estimate of the association between H. pylori infection with diabetes was OR = 1.27 (95 CI 1.11 to 1.45, P = 0.0001, I2 = 86.6). The effect of H. pylori infection on diabetes mellitus (both types), type 1 and type 2 diabetes was 1.17 (95 CI 0.94 to 1.45), 1.19 (95 CI 0.98 to 1.45), and 1.43 (95 CI 1.11 to 1.85) respectively. Subgroup analysis by the geographical regions showed in Asian population risk of the effect of H. pylori infection on diabetes was slightly higher than other population, Conclusion: In overall a positive association between H. pylori infection and diabetes mellitus was found. © 2020 The Author(s)

The association between diabetes mellitus and musculoskeletal disorders: a systematic review and meta-analysis

BackgroundDespite the fact that DM patients are living longer, research on the prevalence of MSDs and other related illnesses is still lacking compared to that of other comorbidities. This study systematically reviewed and meta-analyzed cohort studies to determine the association between diabetes mellitus (DM) and musculoskeletal disorders (MSDs).MethodsA comprehensive search of international databases, including Medline (PubMed), Web of Science, Scopus, and Embase, was conducted up to June 2023 to identify relevant studies investigating the association between MSDs and DM.ResultsThe meta-analysis included ten cohort studies with a total of 308,445 participants. The pooled risk ratio (RR) estimate for the association between MSDs and DM was 1.03 (95% CI 1.00-1.06). Based on subgroup analysis, the association between longer duration (more than 7), European, below the age of 70, and female patients was higher than the others.ConclusionIn conclusion, the results of this meta-analysis suggest that there may be an association between MSDs and diabetes in people with diabetes. These findings add to the existing knowledge on this topic and highlight the importance of recognition and management of MSDs in people with DM. There is a need for further research to investigate the underlying mechanisms and to develop targeted interventions for the prevention and management of MSDs in this population.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=381787, identifier CRD42022381787

The effect of diabetes on the risk of endometrial Cancer : an updated a systematic review and meta-analysis

Funding This research was funded by Systematic Review Network Center, Iran University of Medical Sciences (Grant Number 97–4–37-13922).Peer reviewedPublisher PD

Sensing and Processing for Infrared Vision: Methods and Applications

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Investigating Psychometric Properties of Wechsler Memory Scale-Third Edition for the Students of Tehran Universities

The major aim of the present research was investigating psychometric properties of Wechsler Memory Scale-Third Edition (WMS-III) for the students of Tehran Universities. Therefore, this scale was administrated to 266 (120 male, 144 female) students of two universities "Shahed" and "Tarbiat Moallem". The participants were selected through mulitistep cluster sampling. Reliability coefficient of the subtests ranged from 0.65 to 0.85, and for indexes ranged from 0.75 to 0.86. Then, the interscorer agreement of the subtests that needed clinical judgment (such as Logical memory I, II and Family Pictures I, II) was evaluated. The correlation coefficients among scorers was higher than 0/80.The correlation between WMS-III and WAIS-R Short Form was computed for the construct validity which was low. This indicates that WMS-III and WAIS-R short form assessed separate construct although they have significant relations. The intercorrelation between WMS-III subtests and indexes revealed high correlation between modality-specific subtests and indexes, whereas, it has low correlation with other subtests and indexes. Exploratory Factor Analysis (Principle Component Analysis with varimax rotation) was used in order to investigate Factor Structure of WMS-III. The findings obtained showed three factor structures (auditory memory, visual memory and working memory) explain %76/845 of the total variance. These results are similar to previous researches in case of number of factors, on the other hand, this data in subtests of auditory memory was different from the research literature

Multiple Cylinder Extraction from Organized Point Clouds

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction