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BACKGROUND Few studies examined the association between depressive symptom trajectories and objectively measured physical activity. OBJECTIVE We aimed to investigate if antecedent depressive symptoms predict subsequent daily step counts among participants in the electronic Framingham Heart Study (eFHS). METHODS We performed group-based multi-trajectory modeling to construct depressive symptom trajectory groups using both depressive symptoms (CES-D >16) and antidepressant use in eFHS participants who attended three FHS research exams over fourteen years. At the third exam, eFHS participants were provided with a study smartwatch for measuring daily step counts. We performed linear mixed models to examine the association between depressive symptom trajectories and daily step counts over one-year follow-up adjusting for age, sex, wear-hour, body mass index, and smoking status. RESULTS We identified two depressive symptom trajectory groups from 724 eFHS participants (mean age 53 years, 60% women). The low symptom group (n=566; mean follow-up 286±111 days) consisted of ≤5% of participants with depressive symptoms and ≤1% reporting antidepressant medication use, and the high symptom group (n = 158; 269±113 days) consisted of ≥28% of participants with depressive symptoms and ≥47% reporting antidepressant medication use across the three exams. Compared to those in the low symptom group, participants in the high symptom group walked fewer daily steps during one-year follow-up (690 fewer; 95% CI: 254-1125). CONCLUSIONS Antecedent depressive symptoms/anti-depressive medication use was associated with lower subsequent daily step counts in eFHS. Our findings suggest that adding interventions to improve mood via mHealth technologies may help promote people’s daily physical activity.

Mobile technology offers a remote method to monitor health in older adults and it may provide a platform for early detection of cognitive decline. We aimed to examine attributes of smartphone survey use in the electronic Framingham Heart Study (eFHS) cohort in relation to cognitive testing performed at the time of enrollment. eFHS participants who returned smartphone surveys and underwent cognitive testing were considered in the study (n=1810). The CERAD recall score, Victoria Stroop test interference score, and dichotomous AD8 and MOCA (MOCA score ≤ 19, AD8 score ≥2) were considered as primary exposures. App-based survey adherence was defined as a dichotomous outcome based on whether at least one survey was completed at each 3-month period from baseline to 12 months. Several time attributes were considered including survey return time, touch time, step time, and question completion time. Linear mixed models (LMM for time attributes outcomes and generalized LMM for adherence outcome) were fitted for each cognitive score as the predictor adjusting for age, sex, race/ethnicity, and education level. Results suggest that higher CERAD recall scores were associated with higher odds of completing surveys. There was a significant association between all cognitive exposures and survey time attributes. Participants with poorer cognitive function (lower CERAD, higher stroop interference, MOCA score ≤ 19, AD8 score ≥2) had delayed survey return times, higher touch time, higher step time and higher question completion time. This study contributes to the growing body of evidence that smartphones may be an important tool to identify cognitive decline.

This meta-analysis was conducted to systematically evaluate the short-term efficacy and safety of the three-dimensional (3D) reconstruction visualization technology (3D-RVT) technique for hepatectomy. A systematic literature search was used to gather information on the 3D reconstruction visualization technology technique for hepatectomy from retrospective cohort studies and comparative studies. The retrieval period was up to March 2022. Publications and conference papers in English were manually searched and references in bibliographies traced. After evaluating the quality of selected studies, a meta-analysis was conducted using Review Manager 5.1 software. We included 12 studies comprising 2053 patients with liver disease. Our meta-results showed that 3D-RVT significantly shortened operation times [weighted mean differences (WMD) = -29.36; 95% confidence interval (CI): -55.20 to -3.51; P = 0.03], reduced intraoperative bleeding [WMD = -93.53; 95% CI: -152.32 to -34.73; P = 0.002], reduced blood transfusion volume [WMD = -66.06; 95% CI: -109.13 to -22.99; P = 0.003], and shortened hospital stays [WMD = -1.90; 95% CI: -3.05 to -0.74; P = 0.001]. Additionally, the technique reduced the use of hepatic inflow occlusion and avoided overall postoperative complications [odds ratio (OR) = 0.60; 95% CI: 0.46 to 0.79; P 0.001]. 3D-RVT is safe and effective for liver surgery and provides safety assessments before anatomical hepatectomy.

Introduction: Depression is a common and serious medical illness world-wide. Studies have demonstrated a protective effect of physical activity on depression, but few studies have examined the prospective association between depressive symptoms and objectively measured physical activity. Objective: To investigate if antecedent depressive symptoms predict subsequent daily step counts among participants enrolled in the electronic Framingham Heart Study (eFHS). We hypothesize that the trajectory group with a larger proportion of participants having depressive symptoms and antidepressant use is associated with a lower level of physical activity. Methods: We used group-based multi-trajectory modeling to construct depressive symptom trajectory patterns using both depressive symptoms and antidepressant use collected from eFHS participants who attended three FHS health exams over fourteen years. The presence of depressive symptoms was defined at each exam with a Center for Epidemiological Studies-Depression (CES-D) score > 16. At the final exam, participants were provided with a study smartwatch to measure daily step counts. We excluded: a) person-day records with less than 5 hours of wear-time, or less than 1000 steps recorded in a day; b) participants who failed to return data for ≥1 day per week in the first month. We performed linear mixed effects models to examine the association between depressive symptom trajectories and average daily step counts during the first 30 days of smartwatch use and one-year of follow-up adjusting for age, sex, wear-hour, BMI, and smoking status. We used p Results: We identified two depressive symptom trajectory groups for the 724 eFHS participants (mean age 53 years, 60% women). The “low-stable” group (n=578; mean follow-up 286±110 days) consisted of n = 146; 269±114 days) consisted of >21% of participants with depressive symptoms and >46% of those with antidepressant medication use across the three exams. Compared to the low-stable trajectory group, the participants in the high-increasing depressive symptoms group walked 475 (95% CI: 24-927) fewer daily steps during the first 30 days of smartwatch use ( p = 0.04) and walked 550 (95% CI: 109-991) fewer daily steps during one-year of follow-up ( p = 0.01). Conclusion: Participants in the group with a larger proportion of antecedent depression and anti-depressant use walked significantly fewer steps compared to the group with a smaller proportion of antecedent depression and anti-depressant use. Our findings suggest that interventions via mHealth technologies that target mood may play an important role in promoting physical activity.

Introduction: Depressive symptoms are common and share many biopsychosocial mechanisms with hypertension, but cross-sectional studies on their association have produced inconsistent results. The emergence of digital home blood pressure (BP) measurements may provide additional insight into the relationship. Hypothesis: We hypothesized that depressive symptoms would be modestly associated with higher home BP and hypertension prevalence in participants of the electronic Framingham Heart Study (eFHS). Methods: FHS participants who attended research exam 3 (2016-2019) were invited to enroll in eFHS. They downloaded a smartphone app and were provided a digital BP cuff to measure their weekly BP for up to 1 year. Participants who had at least 3 home BPs were included. Depressive symptoms were measured at the exam using the Center for Epidemiological Studies Depression Scale (CES-D) scored 0-60. We used linear mixed regression models to test the association of CES-D score (independent) with home SBP and DBP (dependent) and a logistic regression model to evaluate the association of depressive symptoms (defined as CES-D ≥16) with prevalent hypertension, adjusting for age, sex, cohort, lifestyle factors, diabetes, and CVD. Results: The study sample included 855 eFHS participants (mean age 53 years, 59% women, 9% multi-ethnic). The prevalence of depressive symptoms was 7% (CES-D score mean±SD, 5.7±6.6). The mean SBP and DBP were 119 and 76 mmHg; the prevalence of hypertension was 48%. CES-D score was modestly associated with home BP; a 1 SD higher CES-D corresponded with 0.9 and 0.6 mmHg higher mean SBP and DBP respectively (Table). Depressive symptoms were not significantly associated with home BP or hypertension prevalence (OR=3.16, 95% CI, 0.96-10.43). Antidepressant use attenuated the relationship. Conclusion: CES-D score had a small positive association with digital home BP, but it was not clinically substantive. The association of depression and hypertension risk warrants more data, which may be supported by mobile health.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí

Circulating miR-195-5p has been proposed as a promising peripheral biomarker for the diagnosis, prognosis and severity assessment of various diseases. However, the demand for its sensitive and convenient quantification has not been met yet. Herein, we proposed a one-pot isothermal approach, in which the target signal acquisition, amplification and conversion (fluorescence read-out) system was integrated by a triple strand displacement amplification (SDA) cascade. Using this triple SDA strategy, miR-195-5p can be at least detected at 1 aM, and the linear dynamic range (from 100 aM to 1 pM) is wide enough to meet the detection needs of clinical miRNA level. A proof-of-principle study, using this novel methodology to directly analyze the spiking serum samples with different levels of miR-195-5p, demonstrated the potential of circulating miR-195-5p detection for clinical point-of-care assay. This one-pot isothermal triple SDA approach, we believe, will be a simple and feasible tool for ultrasensitive quantification of circulating miR-195-5p, and may promote the wide application of this potential biomarker in non-invasive clinical diagnosis.

In this article, the authors examine fundamental linkages between geolocation (human movement) data and financial market equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. The authors concentrate their study on a popular hedge fund trading strategy known as pairs trading. First, the authors collect data regarding Under Armour (UA) and Nike’s stock market price and volume. Second, they investigate the volume of people visiting each physical store of UA and Nike, as proxied from anonymous cell phone geolocation traffic. Third, they monitor the relative activities for tweets related to UA and Nike. Fourth, after combining all the data, the authors glean the following fascinating results: (1) Geolocation information is proven to be an important factor in a pairs trading strategy between UA and Nike, according to the results of feature selection and prediction accuracy for price ratio change; (2) both ensemble methods of machine learning and rolling analysis could significantly raise the prediction accuracy; and (3) a pairs trading strategy incorporating geolocation information can have a cumulative return of 13.72% from January 2018 to June 2018, with an annualized Sharpe ratio of 3.88. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • Geolocation data can encapsulate the relative influx and outflux of consumers from department stores. This is an extremely useful feature in training machine learning models that can be used in lucrative pairs-trading strategies. • In the era of big data, it is tempting to throw every feature into a model and assume that the model will always figure out which features are most important. However, the model will usually perform better when trained on a subset of distinct features. • Ensemble methods are an effective means of eliminating bias in machine learning models because they use the output from a series of independently trained submodels to generate one balanced result.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Background Persistent inflammation is a life-long complication of HIV infection, even in virally suppressed individuals. Elevated plasma concentrations of soluble(s) CD14 and CD163 have been established as biomarkers of chronic inflammation, conferring higher risk for cognitive, neurovascular, and structural abnormalities. Methods Structural magnetic resonance imaging (frontal and temporal regions) as well as plasma inflammatory biomarkers of monocyte activation (sCD14 and sCD163), general inflammation (plasma C-reactive protein, interleukin[IL]-6), and gut microbial translocation (plasma intestinal fatty acid-binding protein) were available on 38 women (25 with HIV) from the Chicago Women's Interagency HIV Study site. Partial least-squares models adjusting for relevant covariates (eg, age, education, and race) were conducted to evaluate the relationship between inflammatory biomarkers and brain volume in the overall sample and among women with HIV (WWH). Results In the total sample, higher plasma sCD14 was associated with smaller volumes in multiple frontal and temporal lobe regions. In the WWH-only sample, sCD163 was associated with smaller volumes only in one region of the left frontal lobe. C-reactive protein, IL-6, and intestinal fatty acid-binding protein were not associated with brain volumes for either group of women. Conclusions Of the inflammatory monocyte markers evaluated, sCD14 was associated with smaller frontal and temporal cortical volume in the overall and WWH-only samples, while plasma sCD163 was only associated with smaller left caudal middle frontal gyrus in the WWH-only group. Validating these monocyte proteins as neurological biomarkers of structural brain deficits in a larger sample is critical for understanding HIV-associated neurobiological complications.

We investigate the fundamental linkages between “geolocation” (human movement) data and financial market’s equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. Our initial data set spans over 54 billion observations and was provided by Fysical. We focus our study on a popular hedge fund trading strategy known as “pairs trading.” First, we collect Under Armour (UA) and Nike’s stock price and volume data. Second, we investigate the relative activity of people visiting a particular physical store of UA and Nike, as proxied from anonymous cell phone foot traffic. Third, we collect the relative sentiment for tweets to UA and Nike. After combining all the data, we glean the following fascinating results: (1) geolocation information is an important variable in pairs trading strategy between UA and Nike, as evidenced by the results from our feature selection popularity methodology; (2) surprisingly, pairs trading only using geolocation data yields positive returns, and employing machine learning methods and rolling analysis enhances the returns; and (3) pairs trading strategy incorporating geolocation information yields a cumulative return of 13.7% from January, 2018 to June, 2018, with an Annualized Sharpe Ratio of 3.9. Some caveats are in order – our results are very sensitive to transaction costs assumptions and our data set has serious limitations.

As a hot research interest of computer vision field, crowd behavior analysis has broad application prospects and great economic value in many aspects, such as intelligent video surveillance, virtual reality, public safety and so on. In high density crowded scenes, pedestrians can be seen as a collection of several groups which consist of people sharing similar motion features. Groups are necessary parts that make up a crowd. To obtain basic crowd interactions for next crowd behavior analysis, a fast crowd segmentation method based on spatiotemporal relationships of crowd is proposed. This method not only considers crowd motion information but also spatial information of pedestrians. The method can achieve the same segmentation results by using less time and can be applied to various crowd scenes. Extensive experiment results on videos of real-world crowd scenes demonstrate that the proposed method is effective as well as efficient for crowd segmentation.

In consideration of problems with traditional augmented reality, including inaccuracy, untimeliness and instability of 3D spatial registration results, this paper proposes an improved algorithm based on ORB (Oriented FAST and Rotated BRIEF) features, which improves recursive adjustment procedures for decision trees during feature point extraction and makes up deficiencies of conventional ORB algorithms such as high computation load and inadequate feature point extraction. In terms of 3D spatial registration, this paper establishes an augmented reality system by RANSAC optimization algorithm. The experimental results suggest that it costs relatively short time to process complicated natural images with the algorithm proposed in this paper, which is highly timely and robust for processing scene images.

In consideration of problems with augmented reality, including untimeliness, inaccuracy and instability of spatial registration results, we proposes an improved algorithm based on FAST-ER (Features from Accelerated Segment Test) and SURF (Speeded-Up Robust Features) in this paper, which does not only improve recursive adjustment methods for decision trees during feature point extraction, but also overcome problems of traditional FAST-ER algorithms such as heavy computation load and ineffective feature point extraction. After information about location parameters of a camera is obtained in this paper, the virtual model is rendered into real scenes with OpenGL to realize virtual-real fusion. The experimental results suggest that it costs short time to process complicated natural images with the algorithm proposed in this paper. In case of any illumination change, scale change, rotation in scenes, it is adaptable to complex outdoor environment, showing relatively high timeliness and robustness.

In this paper, we propose an effective method to recognize human actions. Combined with the relationship between 3D skeleton model of joint position and particle group optimization algorithm is used to optimize the support vector machine (PSO-SVM) and depth through the Kinect sensor to obtain human 3D skeleton model, each skeletal model with 20 joints and 19 joints, the relative geometry between various body parts provides a more meaningful description than their absolute locations, we explicitly model the relative 3D geometry between different body parts in our skeletal representation. Mathematically, rigid body rotations and translations in 3D space are members of the special Euclidean group SE(3), which is a matrix Lie group. Hence, we represent the relative geometry between a pair of body parts as a point in SE(3), We then perform classification using a combination of dynamic time warping, and particle swarm optimization on support vector machine (PSO-SVM), Experimental results on three action datasets: MSR-Action 3D, UT-Kinect, Florence 3D-Action, show that the proposed representation performs better than many existing skeletal representations. The proposed approach also outperforms various state-of-the-art skeleton-based human action recognition approaches.

With the rapid increase in data and multimedia services, demand for positioning has increased especially in complex indoor environment which often needs to determine the location information of the mobile terminal. There is a lack of accuracy and robustness in current indoor positioning system. This paper designs and implements an indoor positioning system based on iBeacon. We adopt Gaussian filter and Unscented Kalman filter method to robustly extract strong signals from iBeacon device. With the extracted signals, we compared them with-in database. The goal of this paper is to design and implement a mobile-based indoor location system which has the mobile applications with the Bluetooth Low Energy technology based on the iBeacon. Using a mobile terminal our system can show position results. Moreover, our system can run on both Android systems and IOS ones. Our method has better performance compared with WiFi method. The experimental results demonstrates that the error is only within 4 meters and our system can achieve accurate and robust positioning.

With the development of Internet technology, dynamic password token have won more and more favor in the field of identity authentication with the good security and ease of operation. Nowadays, foreign's SHA series of algorithms of dynamic password productsare widely using in our country, which have caused a potential threaten for the localization of information security. To realize the research and development of the independent technology products based state-owned cipher algorithm will make encryption products be better safeguarded. In this paper, I select the dynamic password authentication technology based on time synchronization, and a dynamic password is designed by using the state-owned SM3 algorithm Experimental result show that this method has high security and value.

ECG signal plays an important role in the early detection and treatment of atrial fibrillation diseases. Detection and treatment of atrial fibrillation diseases attracted a wide range of national and international experts. The electrophysiological mechanism of paroxysmal atrial fibrillation is the foundation of studying the therapy for atrial fibrillation. R peak detection has become an important part of the ECG processing due to the R-wave amplitude with a large, easy-detection feature. However, the ECG signal in the acquisition process will be subject to a certain degree of interference, such as frequency interference, baseline drift and other issues, common R peak threshold algorithm cannot detect lower baseline ECG waveform, resulting undetected. To solve this problem, this paper presents R peak adaptive threshold extraction algorithm by setting thresholds and reexamining the mechanism for each cardiac cycle to improve the accuracy of the threshold algorithm. Simulation results show that the method is much simpler and more effective, and can real-time processing, which significantly improves the accuracy of the R peak detection and meets the requirements of clinical applications.

In this paper a novel 3D object recognition method which can improve the recognition accuracy of object recognition in the cluttered scenes was proposed. The proposed method use the adaptive-scale to detect the keypoint (ASDK) of 3D object in the cluttered scenes, it use the algorithm of Sparse Principal Component Analysis Augmented Lagrangian Method (SPCA-ALM) to extract the feature of object, the algorithm of SPCA-ALM has a good performance in the high dimensional due to the Spares PCA, and the ALM can raise the speed of the SPCA. The experiment shows that the proposed method can decrease the time of 3D object recognition and improve the recognition accuracy.

Mobile devices are leading the second revolution in the computer graphics arena, especially with regard to 3D graphics. The mobile phone is the most widespread mobile devices with rendering capabilities. Those capabilities have been very limited because the resources on such devices are relatively scarce with respect to the PC; small amounts of memory, little bandwidth, little chip area dedicated for special purposes, and limited power consumption. In order to enhance domestic research strength in the mobile graphics chips and break foreign patent blockade, we presents a fixed 3D graphics rendering pipeline for both geometry and rendering operation in this paper. The research of the 3D graphics rendering pipeline has gone through a rigorous design process: starting from system modeling (using Verilog HDL), algorithm validation on simulation platform, RTL implementation, and hardware/software co-simulation on FPGA development board. The maximum performance of the pipeline is 8.33M Polygons/s and 100M Pixels/s at an operating frequency of 100 MHz.

Clustering is a very special property of social network. Understanding the network cluster structure is of great important, which provides helpful information in developing more social-aware strategies for social network problems. The paper tries to analyse the topology and the clusters for a localized social network in SINA Weibo and try to find whether they have small world properties. The data set used is composed by 4499 SINA Weibo's users from University of Shanghai. The open source software Gephi is used in the analysis of the data. The layout OpenOrd reveals the clusters in the network. The topology shows that the network and the clusters have the small-world property. Furthermore, the paper visualizes the small world phenomenon for the whole network and for clusters with Layered Layout in Gephi.

Computer generated images by default render the entire scene in perfect focus. And computer graphics cameras lack the finite depth of field present in real world, this result in reducing the realism of the scene. Several depth of field methods have been proposed, in this paper, object space methods depth of field will be introduced, and the ray-racing method will be put as the representative method. After then, this paper will focus on post-processing method based on GPU. The comparison result will be shown as the experiment section.

Subdivision has been a very popular technology in graphics at home and abroad. It can be splited into global subdivision and local subdivision, and the methods to subdivide are various including butterfly, modified-butterfly, loop, linear, Catmull-Clark, Doo-Sabin and so on. These methods can be divided into interpolation subdivision and approach subdivision. In this paper, it mainly involves approach and interpolation algorithm, and it mainly introduces a local subdivision algorithm based on triangle mesh reconstruction by Poisson. Aiming at the global subdivision's disadvantages, it is not only save memory space and shorten run time. Local subdivision is very popular in various fields In many case, global subdivision is too waste resource and low efficiency, so global subdivision is not a fine method to optimize model. According to the request and experimental verification, linear algorithm is more suitable for the subject in this paper.

3D graphics hardware acceleration has become more and more important in mobile devices. In the paper, we review some triangle rasterization algorithm previously. On that basis, we propose a novel triangle rasterization algorithm — midpoint traversal, which reduce the number of traversal points and improve the efficiency of graphic acceleration. At last, we use FPGA to implement the algorithm, drawing a conclusion: in the equality resource, compared with others triangle rasterization algorithm based edge function, the midpoint traversal can improve the efficiency.

Model optimization is a commonly used method in reverse engineering and other aspects. Triangle mesh subdivision is one of them, which is used in many fields from computer vision to virtual reality. In this paper, linear subdivision and loop subdivision are included. Linear subdivision has a perfect result because of its interpolation subdivision algorithm but slow speed compared with other subdivision algorithms. In many cases, the image may be not meet visual effect, so local subdivision is the key point by linear subdivision in this paper and will not take so much memory space, and loop subdivision is generally an approach algorithm, which can't get a model similar with original. Here it shows the comparison between loop subdivision and linear subdivision. Through the experiment, it has got a perfect result.

The 3D devices have been widely applied in people's life, for example, smart phone, internet games, high-definition video, geography navigation, etc. The large scale graphics rendering depends on the computing power of the hardware greatly, the calculation of model rasterization operations need amounts of data. It has become the bottleneck of system performance. This paper proposed the method which can accelerate graphics rasterization procedure, based on the idea of tile to meet the needs of graphical applications on embedded platform. The rules and procedures of algorithm are introduced. By using of the XUP-LX110T, the experiments are carried out. It is verified that the method should been compensated the features for the lack of resources and poor computing power of embedded platforms. It can apply smaller chip area to achieve graphics acceleration with fewer resources.

During the past few years, mobile phones and other handheld devices have gone from only handling dull text-based menu systems to being able to render high-quality three-dimensional graphics at high frame rates. Computer graphics hardware acceleration and rendering techniques have improved significantly in recent years. This paper reviews the state of mobile 3Dgraphics, focusing on the research of raster system. It also discusses technical and practical challenges. Finally, implement the fixed pipeline raster system on the FPGA platform, the results show that the system is able to complete the rendering of complex graphics.

With the widely used of mobile devices, such as cellular phone, personal digital assistant (PDA), mobile navigator, personal multimedia player (PMP), 3D graphics are becoming key to mobile device experience. 3D graphics hardware acceleration has become more and more important in mobile devices. In the paper, we review some triangle rasterization algorithm previously. On that basis, we propose a novel triangle rasterization algorithm - midpoint traversal, which reduces the number of traversal points and improves the efficiency of graphic acceleration. At last, we use FPGA to implement the algorithm, drawing a conclusion: in the equality resource, the midpoint traversal can improve the efficiency.

In this paper, the author modifies the triangle rasterizationa and texture coordinate interpolation algorithm. The new algorithms reduce the time of triangle rasterization and texture coordinate interpolation. The proposed triangle rasterization based on edge function, and improved the efficiency with equal hardware resource. The proposed texture coordinate interpolation algorithm based the characteristics of lookup table. Compared with the previous texture coordinate interpolation algorithm, the proposed algorithm can reduce the execution time, and implement more easily. (4 pages)