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In the present study, a new algorithm for automatic target detection (ATR) in synthetic aperture radar (SAR) images has been proposed. First, moving and stationary target acquisition and recognition image chips have been segmented and then passed to a number of preprocessing stages such as histogram equalisation, position and size normalisation. Second, the feature extraction based on Zernike moments (ZMs) having linear transformation invariance properties and robustness in the presence of the noise has been introduced for the first time. Third, a genetic algorithm-based feature selection and a support vector machine classifier have been presented to select the optimal feature subset of ZMs for decreasing the computational complexity. Experimental results demonstrate the efficiency of the proposed approach in target recognition of SAR imagery. The authors obtained results show that just a small amount of ZMs features is sufficient to achieve the recognition rates that rival other established methods, and so ZMs features can be regarded as a powerful discriminatory feature for automatic target recognition applications relevant to SAR imagery. Furthermore, it can be observed that the classifier performs fairly well until the signal-to-noise ratio falls beneath 5 dB for noisy images.

Point cloud data obtained from scanning propellant grains with 3D scanning equipment exhibit positional uncertainty in space, posing significant challenges for calculating the relevant parameters of the propellant grains. Therefore, it is essential to normalize the position of each propellant grain’s point cloud. This paper proposes a normalization algorithm for propellant grain point clouds, consisting of two stages, coarse normalization and fine normalization, to achieve high-precision transformations of the point clouds. In the coarse normalization stage, a layer-by-layer feature points detection scheme based on k-dimensional trees (KD-tree) and k-means clustering (k-means) is designed to extract feature points from the propellant grain point cloud. In the fine normalization stage, a rotation angle compensation scheme is proposed to align the fitted symmetry axis of the propellant grain point cloud with the coordinate axes. Finally, comparative experiments with iterative closest point (ICP) and random sample consensus (RANSAC) validate the efficiency of the proposed normalization algorithm.

Spine angles are an important measure in biomechanics research and are commonly normalized to a percentage of range of motion. However, standardized methods to collect the reference posture trials for this normalization do not exist. The purpose of this study was to determine posture (seated or standing) and number of trials that should be collected and how to calculate the angle that best represents the maximum range. Forty healthy adults (22 females, 18 males) completed 12 reference trials: 1 upright standing, 5 standing flexion, and 5 seated flexion trials. The maximum lumbar angle was found for each flexion trial. Additionally, different methods to calculate the maximum were applied by taking the maximum of the 5 standing, 5 seated, and all 10 flexion trials. An interaction was found between posture, order, and trial number. 42.5% and 57.5% of participants reached their maximum angle during seated and standing flexion respectively which may be due to back- vs hip-dominant movement strategies. 85% of participants achieved their maximum at some point during the first six flexion trials. The maximum angle of all 10 flexion trials was significantly greater than the angle of the first standing or seated trial only but not significantly greater than the maximum of all seated or standing flexion trials respectively. Secondarily, no differences in the maximum lumbar angle were found between sexes. This study suggests that 6 flexion trials, involving both standing and seated flexion, should be collected to best represent the maximum end range of spine flexion.

Purpose A comprehensive investigation of various maximum voluntary contraction (MVC) positions to determine the optimal positions for vastus lateralis (VL), biceps femoris (BF), gastrocnemius lateralis (GL), and tibialis anterior (TA). Methods Twelve participants performed total of seventeen MVC positions for major lower limb muscle groups (VL, BF, GL, and TA). Neuromuscular activities were recorded by surface electromyography. Signals were smoothed by root mean square (RMS). Each MVC level were expressed as a percentage of MVC (% MVC). Statistical differences were measured with a one-way repeated measures analysis of variance and a Tukey’s HSD ( p  < 0.05). Results Optimal MVC positions were found as follows: (i) VL: the combination of knee extension at 70° and 90° flexed knee in sitting position; (ii) BF: the combination of knee flexion at 30°, 45°, and 60° flexed knee in prone position; (iii) GL: unipedal standing position; (iv) TA: the combination of dorsiflexion in sitting position and ankle neutral, in standing position and ankle 110°, and in standing position and ankle 70°. Conclusion This study confirms that multiple positions were needed to elicit the maximal MVC values for VL, BF, and TA. For GL, single MVC position should be performed to elicit the maximal MVC.

The unconstrained acquisition of facial data in real-world conditions may result in face images with significant pose variations, illumination changes, and occlusions, affecting the performance of facial landmark localization and recognition methods. In this paper, a novel method, robust to pose, illumination variations, and occlusions is proposed for joint face frontalization and landmark localization. Unlike the state-of-the-art methods for landmark localization and pose correction, where large amount of manually annotated images or 3D facial models are required, the proposed method relies on a small set of frontal images only. By observing that the frontal facial image of both humans and animals, is the one having the minimum rank of all different poses, a model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem is solved, concerning minimization of the nuclear norm (convex surrogate of the rank function) and the matrix ℓ 1 norm accounting for occlusions. The proposed method is assessed in frontal view reconstruction of human and animal faces, landmark localization, pose-invariant face recognition, face verification in unconstrained conditions, and video inpainting by conducting experiment on 9 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.

An electronic cigarette (e-cigarette) is a battery attached to a chamber containing liquid that may (or may not) contain nicotine. The battery heats the liquid and converts it into a vapour, which is inhaled, mimicking tobacco smoking. The e-cigarette does not rely on tobacco as a source of nicotine but, rather, vaporizes a liquid for inhalation. E-liquids are often flavoured and may contain nicotine in various concentrations, although actual amounts are seldom accurately reflected in container labelling. The deleterious effects of nicotine on paediatric health are well established. The use of e-cigarettes in the paediatric age group is on the rise in Canada, as are associated nicotine poisonings. E-devices generate substantial amounts of fine particulate matter, toxins and heavy metals at levels that can exceed those observed for conventional cigarettes. Children and youth are particularly susceptible to these atomized products. Action must be taken before these devices become a more established public health hazard. Policies to denormalize tobacco smoking in society and historic reductions in tobacco consumption may be undermined by this new 'gateway' product to nicotine dependency.

Objectives: The purpose of the present study was to evaluate the level of physical capacities of Italian American Football (AF) players and compare their performances with published data of American college players. A secondary aim was to assess whether the performance of Italian players in the NFL Combine tests has improved over time compared to previously tested players of similar competitive level. A total of 41 Italian AF players (age 28.1 ± 4.7 y, stature 181.1 ± 5.9 cm, body mass 98.3 ± 17.8 kg) competing in the 2020/2021 Division I Championship, participated in this study and performed the NFL Combine test battery. Methods: The NFL Combine test battery includes the 40-yard dash, the 20-yard shuttle, the 3-cone drill tests, the broad jump test, the vertical jump test, and the maximum number of repetitions at bench press with a 100 kg load. Players were divided into three groups based on their playing position: skill players (SP = 14), big skill players (BSP = 9), or linemen (LM = 13). In addition, players’ performance scores were normalized to their stature and body weight. Results: Italian players showed lower performances in all the six tests compared to American college players. Significant differences were observed between player positions. Normalized performances were significantly lower in Italian compared to American players. Conclusions: Despite an improving trend in the NFL Combine tests being registered in Italian AF players, a relevant gap still exists compared to their US counterparts.

Attention mechanisms and grid features are widely used in current visual language tasks like image captioning. The attention scores are the key factor to the success of the attention mechanism. However, the connection between attention scores in different layers is not strong enough since Transformer is a hierarchical structure. Additionally, geometric information is inevitably lost when grid features are flattened to be fed into a transformer model. Therefore, bias scores about geometric position information should be added to the attention scores. Considering that there are three different kinds of attention modules in the transformer architecture, we build three independent paths (residual attention paths, RAPs) to propagate the attention scores from the previous layer as a prior for attention computation. This operation is like a residual connection between attention scores, which can enhance the connection and make each attention layer obtain a global comprehension. Then, we replace the traditional attention module with a novel residual attention with relative position module in the encoder to incorporate relative position scores with attention scores. Residual attention may increase the internal covariate shifts. To optimize the data distribution, we introduce residual attention with layer normalization on query vectors module in the decoder. Finally, we build our Residual Attention Transformer with three RAPs (Tri-RAT) for the image captioning task. The proposed model achieves competitive performance on the MSCOCO benchmark with all the state-of-the-art models. We gain 135.8 % CIDEr on MS COCO “Karpathy” offline test split and 135.3 % CIDEr on the online testing server.

Introduction In Asian population, facial structure may contribute to the primary pathophysiology of obstructive sleep apnea (OSA). We hypothesized that sleep position may have more effect on OSA in Asians compared to the Western population. If this hypothesis is accurate, positional therapy will have a major impact on treatment of OSA among Asians. Patients/methods We reviewed 263 polysomnographic studies from our laboratory from January 1, 2010 to June 30, 2010. Criteria for positional and non-positional OSA were (1) supine respiratory disturbance index (RDI)/non-supine RDI ≥2 and total RDI ≥5 and (2) supine RDI/non-supine RDI <2 and total RDI ≥5, respectively. We aimed to determine the difference in baseline characteristics, polysomnographic findings, and predictors for positional OSA. Results We found 144 patients diagnosed with OSA (RDI ≥5), and 96 patients met the criteria for positional OSA (67%), in which in almost half of these patients (47%), RDI was normalized (RDI < 5) in non-supine position. Snoring frequency were significantly lower among positional OSA and OSA was less severe indicated by lower RDI and arousal index, higher mean and nadir oxygen saturation, and higher %NREM3. We also found that low snoring frequency (less than 20% of total sleep time) was a significant predictor for positional OSA (odd ratio of 3.27; p  = 0.011), contrarily to low mean oxygen saturation (<95%) which was found to be a negative predictor (odd ratio of 0.31; p  = 0.009). Among OSA patients, low RDI (<15) was a significant predictor for normalization of RDI in non-supine position (odd ratio of 8.77; p  = < 0.001), contrarily to low mean oxygen saturation (<95%) which was also found to be a negative predictor (odd ratio of 0.13; p  = 0.001). Conclusion Positional OSA is very prevalent and noted in almost 70% of our patients. Low snoring frequency was noted to be a positive predictor for positional OSA, contrarily to low mean oxygen saturation which was found to be a negative predictor. These findings are encouraging that positional therapy can be very beneficial as the treatment modality for OSA among Asians.

The problem of automatically matching composite sketches to mug-shot is addressed in this paper. To determine the identity of a criminal, composite sketch to photograph is commonly used technique due to budgetary reason. The accuracy in detection is more using forensic sketches than composite sketches using facial composite software. Composite sketches have the same modality as that of the mug-shot has that changed the forensic world. In this paper emphasis is made on matching composite sketches to mug-shots that gives better performance over the more common representation method exists already. The component based framework presented in this paper consists of the following major steps: 1. face normalization using a geometric transformation and color space conversion, 2. facial component localization using Viola-John algorithm, followed by ASM, and 3. per component feature extraction using nearest neighbor symmetry and matrix normalize cross-correlation followed by computing length between facial constituent. The proposed system has been evaluated on mugshot photographs-composite sketches pairs with hundreds of individuals.