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Background: Health-related physical fitness reduces the risk of chronic disease, promotes quality of life and has enormous economic benefits considering the global health care costs resulting from obesity. However, relatively limited information is available regarding the dose–response relationship between health-related physical fitness and obesity risk. This study aimed to determine the associations of health-related physical fitness with body mass index (BMI) distribution and overweight/obesity risk among adults aged 23-64 years in Taiwan.Methods: We conducted a cross-sectional study and reviewed the data derived from the Scientific Physical Fitness Testing Program, Sports Administration, Ministry of Education in Taiwan. Responses from 16,939 participants (7,761 men; 9,178 women) aged 23-64 years from the database were collected in this study. Each participant completed a series of health-related physical fitness measurements, including cardiorespiratory fitness (3-min progressive knee-up and step [3MPKS] test), muscular fitness (hand grip strength), and flexibility (sit-and-reach test). Anthropometric measurements included body height, weight, and BMI. The quartiles of health-related physical fitness results were identified as the dependent variable in the multiple linear and multiple logistic regression analysis to determine the associations of the health-related physical fitness measurements with the BMI distribution and the overweight/obesity risk as well as the dose–response relationship.Results: The 3MPKS test and relative hand grip strength were significantly negatively associated with BMI and overweight/obesity risk with a dose–response relationship in both men and women. However, the sit-and-reach test was partially related to BMI and overweight/obesity risk in adults.Conclusions: Higher levels of the 3MPKS test and relative grip strength were associated with lower BMI and overweight/obesity risk in both sexes. Cardiorespiratory fitness and muscular fitness were effective predictors of BMI distribution and overweight/obesity risk in Taiwanese adults.

In the current age of information explosion, the extraction of relevant data from a large information pool, which includes images, has become crucial. Because of the easy availability of imaging devices, millions of images are being added to the image pool every day. Current image contrast enhancement methods have some drawbacks. First, for images captured in extreme lighting conditions, such as backlit images and extremely bright or dark images, extant image contrast enhancement methods cannot retain the brightness distribution details of the original image, resulting in image distortion. Second, the methods can only achieve global contrast enhancement but not local contrast enhancement. Third, these methods cannot satisfy the Human Visual System mapping curve, resulting in nonsmooth or distorted images. In this study, a novel image expand enhancement system based on fuzzy theory is proposed. This system has two major features: (1) the expand model is designed such that the correlations between light intensity and the intensity of an image captured in this light are considered to achieve local contrast enhancement, and (2) an extreme case of images processing step that is capable of enhancing. The proposed model has a fusion framework; therefore, the image fusion model can represent the relationship between the original image and the expanded image. Next, the fusion model describes some expected statistical properties of the desired intensity of the expand image. Thus, the details of the original image are retained. Experimental results revealed that the proposed algorithm is capable of adaptively enhancing the contrast of the original image while simultaneously extruding the details of objects in the image. The resulting enhanced image can be effectively used for image information analysis and other image-processing tasks.

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.

In the process of image acquisition, due to different light source distributions and positions, the problem of overexposure or underexposure will be generated during imaging. Such problems are relatively weak in the detailed information of an image, and the purpose of image enhancement technology is to solve them. Based on the fuzzy problem, this study proposed adaptive parameter image contrast enhancement technology, in order to solve the problems of overexposed and underexposed images. The analysis of the characteristics of the exposure value of an image could determine its adaptive adjustment parameters. The proposed fuzzy decision-making theory was used, and its membership function was applied for re-transformation, in order to achieve image enhancement. The experimental result proved that the fuzzy theory-based adaptive parameter image enhancement algorithm can smoothly optimize image visual quality, without over-enhancing the image, can highlight the detailed information of an image, and can conform to the feeling of human vision.

This paper addresses a sonar behavior-based fuzzy controller (BFC) for mobile robot wall-following control. The wall-following task is usually used to explore an unknown environment. The proposed B...

In this work, a nano-cavity patterned sapphire substrate (nc-PSS) is fabricated by using a self-formed meshed Pt thin film on a c-plane sapphire substrate. The light output power of a GaN-based light emitting diode on the nc-PSS is 45% greater than that of a control light emitting diode that was prepared on a flat c-plane sapphire substrate (f-SS) wafer. The GaN-based light emitting diode that was prepared on the nc-PSS exhibited much less drooping than a GaN-based light-emitting diode that was prepared on a commercial semi-sphere patterned sapphire substrate (r-PSS), mainly because the voids that formed at the cavities at the GaN/nc-PSS interface buffered the stress in the GaN epi-layers that was imposed by the sapphire substrate.

This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.

The hardware restrictions of surface mount placement machines, such as height, pick and place restrictions, and simultaneous pickup are often in printed circuit board (PCB)-related studies. This study proposes an efficient hybrid genetic algorithm (HGA) for solving the nozzle assignment problem and the component pick and place sequence problem. First, the proposed method obtains the sequence of the automatic nozzle changer (ANC) with the maximum number of simultaneous pickups and the minimum number of picks as the solution of the nozzle setup problem. Then, the proposed method uses the nearest neighbor search (NNS), 2-optimization, and a genetic algorithm (GA) with the known ANC sequences to obtain the PCB assembly time with the optimal component pick and place sequence. Experiments are conducted on the PCB of the EVEST EM-780 surface mount placement machine. Results show that the proposed HGA gives the lowest total number of picks, the shortest total head movement distance, and the minimum total PCB assembly time compared to those of other methods.

In general, a convolutional neural network (CNN) consists of one or more convolutional layers, pooling layers, and fully connected layers. Most designers adopt a trial-and-error method to select CNN parameters. In this study, an AlexNet network with optimized parameters is proposed for face image recognition. A Taguchi method is used for selecting preliminary factors and experiments are performed through orthogonal table design. The proposed method filters out factors that are significantly affected. Finally, experimental results show that the proposed Taguchi-based AlexNet network obtains 87.056% and 98.72% average accuracy of image gender recognition in the CIA and MORPH databases, respectively. In addition, the average accuracy of the proposed Taguchi-based AlexNet network is 1.576% and 3.47% higher than that of the original AlexNet network in CIA and MORPH databases, respectively.

Glomerulus is an important component in human kidney. The appearance of the glomeruli on light microscopic image can provide abundant information for disease diagnosis. Due to the importance of glomeruli on accurate renal disease diagnosis, this paper proposes an automatic method to detect glomeruli in light microscopy images with Periodic Acid Schiff (PAS) or hematoxylin and eosin (H&E) stains at 100x, 200x, or 400x optical magnification. The faster region-based convolutional neural network (R-CNN) is applied to the detection task. The proposed detection approach performs an end-to-end glomerulus detection without any a priori information of the stains and magnifications of the images. The training dataset contains 2,511 images with 3,956 glomeruli. The test dataset contains 482 images with 563 glomeruli. The recall and precision of the test result are 91.54% and 86.50%, respectively, which shows the effectiveness of the proposed detection method.

This paper proposes a fuzzy cerebellar model articulation controller using a strategy-adaptation-based bacterial foraging optimization (SABFO) algorithm to solve classification problems. A strategic approach to the chemotaxis step in the SABFO algorithm was adopted: in this approach, each virtual bacterium swims on different run-lengths, and bacterial diversity is increased. The simulation results indicated that the performance of the proposed method was more favorable than that of other methods.

This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the video game. Both Flappy Bird and Atari Breakout games were implemented to verify the proposed method in this study. Experimental results showed that the scores of our proposed RQDNN were better than those of human players and other methods. In addition, the training time of the proposed RQDNN was also far less than other methods.

Human visual perception is insensitive to certain shades of gray but can distinguish among 20 to 30 shades of gray under a given adaptation level. In this paper, we propose an image fusion pipeline that generates a high vision quality image by fusing the Three-Scale Adaptive Inverse Hyperbolic Tangent (3SAIHT) and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithms to increase detail and edge information. Fusion results are clearer and better with regard to display quality and contrast enhancement.

Liquid crystal displays (LCDs), which serve as receivers of high visual quality video, suffer from motion blur issues. One of the methods in terminating motion blur is motion compensated frame rate upconversion, which is widely adopted in LCDs. In the previous work, i.e., particle-based frame rate upconversion, the computation complexity is high while repeated operations and some improper cost evaluation setup are observed. Therefore, in this paper, hybrid frame rate upconversion is proposed with two features. First, the cost evaluation for particle-based motion trajectory calibration is modified based on the possible noise sources and video resolution variations. Second, repeated operations in particle-based motion trajectory calibration are observed. Therefore, original particle-based motion trajectory calibration is replaced by initial motion vector assignment and subsequent motion vector mapping to achieve computation complexity reduction, while the effective search range is relatively expanded. According to the experiment results, the visual quality is enhanced by 1.87 dB on average, compared with state-of-the-art unidirectional-based frame rate upconversion approaches. On the other hand, the computation complexity of the proposed design is reduced by 25-90% based on target video resolution, concluding a high visual quality and low computation complexity frame rate upconversion design.

The Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm is capable of enhancing the global contrast of the original image adaptively, while extruding the details of objects simultaneously, cannot en hance local contrast. Therefore, we proposed a Contrast- Limited Adaptive Histogram Equalization (CLAHE) modulation based on AIHTcontrast enhancement algorithm from conjugate AIHT and CLAHE image contrast enhance algorithm. The CLAHE has good contrast enhance performance, but excessive contrast enhance will produce the serious chromatic aberration results. We apply the AIHT and CLAHE advantage to present a joint multiple processes algorithm of contrast enhancement to achieve better contrast enhancement effect.

Traditionally, frame rate up conversion can be realized by either bidirectional or unidirectional approaches. Bidirectional approach suffers from motion ambiguity. However, unlike unidirectional approach, bidirectional approach does not suffer from holes and occluded regions. In this article, the particle-based frame rate up conversion algorithm is proposed by combining hierarchical motion vector field construction stage and particle-based motion trajectory calibration stage. First of all, color-oriented adaptive cost function is applied in the hierarchical motion vector field construction stage to retrieve initial motion vectors. Second, the retrieved motion vectors are memorized and calibrated by minimizing the distance between interpolated block and particle trajectory intersecting points. Besides the minimization, joint cost-function evaluation further prevents untrue motion vectors from being selected. According to the experiment results, proposed pixel-based frame rate up conversion outperforms existing works by 0.9-3.9 dB. On the other hand, the computation complexity of proposed work is less than existing bilateral approaches by 5-100, while it is larger than the unidirectional approach by 3-100, according to the target video resolution. This concludes a high visual quality frame rate up conversion design.

In this work, we investigate the feasibility of detecting quantitatively DNA molecules utilizing the technology named after the immunomagnetic reduction (IMR) assay. Magnetic nanoparticles dispersed in a phosphate buffer saline solution were bio-functionalized with probing single-strand DNA. A superconducting quantum interference device (SQUID) ac magnetosusceptometer was employed to detect IMR signals related to the concentration of the target DNA. The results reveal that use of IMR assay had merits such as a high convenience level, e.g. wash-free processes and high sensitivity, down to pM, for DNA detection.

Two-dimensional ordered Ag and Pb nanopuck arrays have been grown on two different types of Pb quantum islands. Electronic patterns on the surfaces of Pb islands serve as the templates for the growth. Nanopucks of different materials display dissimilar growth behaviors, reflecting the varied interaction strength between adatoms and the substrate. Due to a strong interaction existing between Ag atoms and the substrate, perfectly ordered Ag nanopucks of nearly identical size can be fabricated. The Ag array can sustain a wide growth temperature range from 70Kto150K, while for Pb nanopucks, the similar result can be obtained only below 70K. The stronger interaction also synchronizes the behavior of Ag nanopucks with that of the Pb island substrate, which exhibits a bilayer alternating character.

In this study using dark channel prior for single image remove strong light and atomized. The method used to dark channel estimate transfer and Soft Matting transmittance, of a method for thinning processing, obtain a depth map image of strong light. the light reflected from the scene is affected surface of airborne particles, which leads to a decrease in image contrast, while the ambient scene light will be scattered in the air suspended particles, the scattered light into the imaging device and will cause the image color drift, the overall look of the image becomes blurred images and details of the contents illegible gray color bias. Image of outdoor scenes are degraded by the medium. Can be effective remove strong light, so that the overall image contrast in natural.

The Eight-Scale parameter adjustment is a nature extending of Adaptive Inverse Hyperbolic Tangent (8SAIHT) algorithm. It has long been known that the Human Vision System (HVS) heavily depends on detail and edge in the understanding and perception of scenes. Our main goal is to produce a contrast enhancement technique to recover an image from a blurred and darkness, also improve visual quality at the same time. Eight-scale coefficients adjustments can provide a further local refinement in detail under the Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm. The proposed 8SAIHT method is using the sub-band to calculate the local mean and local variance before the AIHT algorithm is performed. We also show that this approach is convenient and effective to do the enhancement process for a various types of images. The 8SAIHT is also capable of enhancing the local contrast of the original image adaptively while extruding more on the details of objects simultaneously.

The ongoing success of three-dimensional (3D) cinema fuels increasing efforts to spread the commercial success of 3D to new markets. The possibilities of a convincing 3D experience at home, such as three-dimensional television (3DTV), has generated a great deal of interest within the research and standardization community. A central issue for 3DTV is the creation and representation of 3D content. Acquiring scene depth information is a fundamental task in computer vision, yet complex and error-prone. Dedicated range sensors, such as the Time­ of-Flight camera (ToF), can simplify the scene depth capture process and overcome shortcomings of traditional solutions, such as active or passive stereo analysis. Admittedly, currently available ToF sensors deliver only a limited spatial resolution. However, sophisticated depth upscaling approaches use texture information to match depth and video resolution. At Electronic Imaging 2012 we proposed an upscaling routine based on error energy minimization, weighted with edge information from an accompanying video source. In this article we develop our algorithm further. By adding temporal consistency constraints to the upscaling process, we reduce disturbing depth jumps and flickering artifacts in the final 3DTV content. Temporal consistency in depth maps enhances the 3D experience, leading to a wider acceptance of 3D media content. More content in better quality can boost the commercial success of 3DTV.

Most image enlargement techniques suffer the problem of zigzagged edges and jagged images following enlargement. Humans are sensitive to the edges of objects; if the edges in the image are sharp, the visual is considered to be high quality. To solve this problem, this paper presents a new and effective method for image enlargement and enhancement based on adaptive inverse hyperbolic tangent (AIHT) algorithm. Conventional image enlargement and enhancement methods enlarge the image using interpolation, and subsequently enhance the image without considering image features. However, this study presents the method based on Adaptive Inverse Hyperbolic Tangent algorithm to enhance images according to image features before enlarging the image. Experimental results indicate that the proposed algorithm is capable of adaptively enhancing the image and extruding object details, thereby improving enlargements by smoothing the edge of the objects in the image.

Stereo matching technique has been extensively investigated for depth map extraction, while most depth-map applications (such as 3D gaming, vehicle collision detection, etc.) adopt active scan systems to measure the distance. It is challenging to design an efficient hardware architecture of stereo matching which meets the real-time/high-resolution requirement. Moreover, external factors such as lighting condition, variance of lens distortion, and stereo image misalignment may impact the accuracy of the depth measurement significantly. To address these issues, we propose a real-time stereo matching architecture which is optimized for the accuracy of pose/gesture recognition and vehicle collision detection. The proposed architecture features sub-pixel estimation and programmable features in lens distortion, misalignment, lighting factor, working range, and refinement parameters. FPGA implementation of the proposed architecture produces stable depth map stream, reaching 1920×1080 image resolution at 60fps.

In this paper, a memory-efficient and low-complexity architecture is proposed for the scalable video encoder, achieving the requirement of the multi-source digital home environment. The proposed very-large-scale integration architecture of the scalable video encoder is implemented in TSMC 0.18-μm 1P6M CMOS technology. The proposed hardware is synthesized under 0.18-μm CMOS technology; resulting throughput is 93.3M samples/sec, occupying 182K gates. Resulting power dissipation is 42.13 mW, operating at 150 MHz clock source. The performance of proposed work (the throughput) meets 1080p@30 fps real-time encoding, constrained by wireless high definition video interface for mobile environment.

Exercise training lowers the mortality rate of animals under heat stress. It is currently unknown whether heat preconditioning can attenuate exercise-induced increases in circulating interleukin-6 (IL-6) for athletes. Sixteen male basketball players were randomly assigned to the control (n = 8) and heat preconditioning (n = 8) groups. The heat preconditioning program consisted of a 30-min hot water immersion at 41°C performed every 72 h up to 4 sessions, which slightly elevated circulating creatine kinase (CK) and thiobarbituric acid-reactive substances (TBARS) by 27% and 21%, respectively (P ＜ 0.05). Seventy two hours after the last hot water immersion, both groups were challenged with an acute bout of eccentric exercise (6 squats at 85% maximal voluntary effort for 6 sets), which elicited significant increases in IL-6 (+120%) and CK (+34%) above pre-exercise level of the control group (P ＜ 0.05). This increase was absent in the heat preconditioning group. Heart rate variability (HRV) and high frequency power (HF) were increased following heat preconditioning, and remained elevated after exercise challenge. Our study provides evidence that eccentric exercise-induced stress response can be attenuated by a heat preconditioning protocol at 41°C.

Saussurea involucrata(Kar. et Kir.), known as the snow lotus, grows in the Tian Shan and A’er Tai areas of China. It has recently been reported that the ethyl acetate extract ofS. involucrata(SI-2) can inhibit proliferation and induce apoptosis in PC-3 human prostate cancer cells. This study investigated the protective effect of ethyl acetate extract ofS. involucrata(SI-2) or rutin, a flavonoid extracted from ethyl acetate extract ofS. involucrata(SI-2), on D-galactose- (D-gal-) induced brain injury in mice. Administering SI-2 or rutin (30 mg/kg/d and 30 mg/kg/d) for 6 weeks, concomitant with D-gal injection, significantly increased superoxide dismutase and glutathione peroxidase activities and decreased the MDA level in plasma. Furthermore, the result showed that the percentages of cleaved caspase-3 and PARP in the D-gal-treated mice were much higher than those in the control. Pretreatment using SI-2 or rutin decreased the expression of cyclooxygenase-2 via downregulation of NF-kappaB, resulting in a decrease in lipid peroxidation. Furthermore, our results also showed that oral administration of rutin to these mice significantly improved behavioral performance in a step-through passive avoidance task and these results suggest that SI-2 or rutin exerts potent antiaging effects on D-gal in mice via antioxidative mechanisms.