Over the centuries the evolution of methods for the capture of human movement has been motivated by the need for new information on the characteristics of normal and pathological human movement. This study was motivated in part by the need of new clinical approaches for the treatment and prevention of diseases that are influenced by subtle changes in the patterns movement. These clinical approaches require new methods to measure accurately patterns of locomotion without the risk of artificial stimulus producing unwanted artifacts that could mask the natural patterns of motion. Most common methods for accurate capture of three-dimensional human movement require a laboratory environment and the attachment of markers or fixtures to the body's segments. These laboratory conditions can cause unknown experimental artifacts. Thus, our understanding of normal and pathological human movement would be enhanced by a method that allows the capture of human movement without the constraint of markers or fixtures placed on the body. In this paper, the need for markerless human motion capture methods is discussed and the advancement of markerless approaches is considered in view of accurate capture of three- dimensional human movement for biomechanical applications. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice. The feasibility of accurately and precisely measuring 3D human body kinematics for the lower limbs using a markerless motion capture system on the basis of visual hulls is demonstrated.
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(AWGN) channel from berths to the satellite. For frequency offsets estimation, there are many estimation methods[1-4] such as blind estimation, data aided estimation and so on. The research of blind estimation is less because its obvious SNR threshold and estimation precision is low under the low SNR environment. The common algorithms are Kay algorithm and Kim algorithm . As to data aided estimation, there are a lot of researches. In 1985, Steven A.Tretter proposed a approximate estimate algorithm  to change the frequency offsets estimation problem into frequency offsets estimation based on complex sequences phase, however it has the SNR threshold and it can only be applied to the high SNR conditions. In 1989, Kay raised a difference phase estimation method  on the basis of Steven A.Tretter, but the estimation accuracy is strongly influenced by noise and the algorithm is no longer applicable under the condition of low SNR with phase hopping. In 1995, M.Lulse and R.Reggiannini present a L&R algorithm  whose mean square error is close to Cramer Rao Bound (CRB) on Maximum Likelihood (ML) estimation basis, but its estimation range is limited. In 2007, Hua Fu put forward the ML algorithm [9-10] which joint amplitude and phase information. Because this algorithm can not only get the frequency estimated value, but also obtain the phase estimated value, it is the ideal algorithm and mean square error of frequency offset estimation is closed to CRB. However, fewer pilot symbols will lead to the estimation accuracy declining. When the Phase Locked Loop (PLL) is used for phase tracking process, large Doppler frequency shifts will reduce the capture speed and phase locked loop can not be finished capturing within a limited time. With the frequency lock loop help, phase locked loop has a faster capture speed, but it is not suitable in the low SNR because the frequency discriminator requires that input SNR cannot be less than 7dB.
The capture-recapture procedure in epidemiology con- sists in confronting data from at least two independent sources, collecting cases in the same area in order to estimate the total number of cases, and assessing the completeness of each data source . In brief, this method involves modelling the overlap between two or more lists of individuals (data “sources”) from the target population, and using this model to predict how many additional individuals were unseen, and hence the total population size. To avoid bias in the estimate, the sources of data collection must be independent and homogeneity of capture must be ensured [14,15] (i.e. the probability of capture does not depend on case characteristics). Capture heterogeneity can result in positive dependence (underestimation of the population) or negative dependence (over-estimation of the population) between sources.
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Capture-recapture methods can be used to estimate population size and other fundamental demographic variables. The most popular class of models that describe the behaviour of closed populations were first introduced as a set by Pollock(1974, 1976) and later more fully described in a wildlife monograph by Otis et al. (1978). Another important reference for this class of models is White et al. (1982). Each model within the class requires a sequence of t samples to be taken from the population. After each sample is taken animals within the sample not previously caught each receive a unique tag so that they can be recognised if recaptured in a later sample. After each sample is taken all animals are released. In each of their models Otis et al. (1978) allow the capture probabilities to vary due to time(t), due to heterogeneity (h) between the capture probabilities of the different animals, and due to a behavioural (b) response to the traps used. A total of eight possible models within this class results from the fact that each of these three factors can be present or absent. The sampling scheme considered within the Otis et al. (1978) monograph is referred to as discrete time sampling.
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Furthermore from the comments, the combined technique was said to be uncanny. The noise from the motion capture data and the exaggerated mouth shapes of the phonetic map technique were too much. The eyes were also stated to cause some distraction and dis- comfort. One participant noticed that, ”the eye motion was very distracting from the lip movement. With [the phonetic animation], there was no eye movement, so it was easier to focus on the lip movement”. The capture data for the upper region of the face was not smoothed, and should be filtered in the future to decrease the noise. The next iteration of this system should maybe avoid including the upper region of the face all together to avoid distraction until the combined lip-sync technique is improved enough to gain more viewer preference. It is also good to note that, while most who commented preferred the mo- tion capture animation, many mentioned that they wish there was some exaggeration in the mouth movements of that animation. One participant was tied between the motion capture animation and the combined animation saying that, ”[Sam] seemed the most natural [...] I liked Bob too - only slightly over the top and it was a hard choice between Sam and Bob”. Another participant remarked wanting some exaggeration in the motion capture animation but ”not to the extent or level as the other two”. This means that while this version of the system did not work, a combined motion capture and phonetic mapping technique could be liked in the future.
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Deployment of large scale biometric systems is already upon us. They are increasingly being adopted by border entry points and workplaces. Whilst they have been shown to be efficacious on small samples, they have yet to be demonstrated on large populations. One of the key challenges that needs to be solved in this scalability issue is to significantly increase the throughput of individuals. This can be achieved in two main fashions : faster biometric capture or less human intervention. One obvious way of increasing the capture rate of biometric information is to use non-contact methods such as face or gait. Face is a well known biometric that has been shown to be a rich discriminator of individuals , . Gait is a new biometric that has shown promising results whilst being detectable from a distance , . By making the system autonomous the requirement for a human operator can also be removed. Autonomous or smart rooms have been previously studied . They are typically concerned with tracking of individuals to customise their interaction with the environment. This paper aims to extend the smart room concept to bio- metric capture. However, instead of performing tracking, the environment will return biometric features. The environment, hereafter known as the biometric tunnel, will perform on- line capture of face and gait. Face will be found directly from images and gait information will be extracted via a 3D reconstruction. When the tunnel is fully automated, we shall develop identification results. Here we describe the underlying design and operation, especially with a view to a smart room or access control scenario. In biometric applications it is imperative that no information is lost as this may result in
Probability of infection status given observations. Notwithstanding the above, an individual’s true infection status is usually unknown. Rather, we have a set of diagnostic test results from which we wish to infer the true infection status of the individual. This can be achieved by combining the individual capture histories with the probabilities of capture, being infected at first capture, becoming infected (transitioning from unin- fected to infected) and of obtaining each combination of diagnostic test results, as estimated from the multi-event capture-recapture model. Figure 3 shows the capture and diagnostic test result histories for a selection of badgers, and illustrates the effect of sex, capture history and current and historical diagnostic test results on the probability of being truly infected given any diagnostic test result. For example, a female that was sampled only once in winter Figure 1. Temporal dynamics of the recapture probability for uninfected (solid lines) and infected (dotted lines) badgers at Woodchester Park from 2007 to 2012. Upper graph: males. Lower graph: females. Circles and bars indicate point estimates and 95% confidence intervals, respectively.
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Capture-C combines 3C, oligonucleotide capture tech- nology and NGS to generate genome-wide contact pro- files from hundreds of selected loci at a time . In this method, 3C DNAs are sonicated, and paired-end se- quencing adaptors are added. The resulting library is then enriched for junction fragments of interest by hybridization to biotinylated capture probes and strepta- vidin pull-down. Finally, the captured DNAs are ampli- fied and sequenced, and the interaction maps are produced by corresponding bioinformatics methods. The Capture-C strategy can be used to enrich Hi-C libraries, and accordingly a new technique, known as Capture Hi- C (CHi-C), was developed . This method enables deep sequencing of target fragments, excluding unin- formative background [115, 116].
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A) Model experiment of the OPAC purification of a yeast DNA fragment. The mixture of a PCR-amplified yeast DNA fragment (a target) and linearized pBR322 plasmid (a control) was targeted with PNAs 1 and 2. Then, the capture procedure was performed in the presence (+) or absence (-) of the biotinylated ODN probe. The captured yeast DNA fragment is marked with the white arrowhead. In the absence of an ODN this fragment was not captured and had less mobility. The latter was due to cationic PNAs non- specifically associated with the DNA fragment; these positively charged molecules dissociated from captured fragments during the washing procedure. M is for a λ DNA HindIII digest used as size marker, where the lower doublet corresponds to 2.0-2.3 kb.
The songs or speeches we listen from tape recorder or radio are in analogue form. So if it has to be kept part of the digital library system it should be first digitized. Digitization can be done by attaching an audio player to a system through an audio capture card and then sound can be recorded to the system. It depends on the choice of library, which format it stores voices for example .wav, .mp3, .midi etc. Recently developed MP3 format is very compact and takes less space while the quality of audio is also better compared to other formats.
There was a clear relationship between the presence of both wiggles and dashes, which have been used as proxies for prey encounters in diving animals (Simeone and Wilson, 2003; Zimmer et al., 2011a), and the presence of a prey capture identified by the SVM. Wiggles were a better indicator of prey capture than dashes, corresponding to 71% of dives in which the model identified a prey capture event compared with 61% for dashes. Studies using proxies for prey encounter such as wiggles, dashes and head movements have assumed that (a) all prey that is encountered is pursued (Ropert-Coudert et al., 2006) and (b) once prey is encountered, the likelihood of prey capture is high (Zimmer et al., 2011a). However, there are many factors that are likely to affect the rate of prey capture success in relation to the prey that is encountered. These include the effects of prey patch density on prey capture success (Draulans, 1987; Darby et al., 2012), the effects of light level on the foraging success of visual predators (Ropert-Coudert et al., 2006), the presence of competition from other predators (Minderman et al., 2006) and the effects of individual experience (Daunt et al., 2007). For these reasons, rates of prey capture cannot be inferred from prey encounter, and methods that focus on prey encounters or capture
Bubble } particle capture is the heart of froth S otation. For ef R cient capture to occur between a bubble and a hydrophobic particle, they must T rst undergo a suf R ciently close encounter, a process that is controlled by the hydrodynamics governing their ap- proach in the aqueous environment in which they are normally immersed. Should they approach quite closely, within the range of attractive surface forces, the intervening liquid R lm between the bubble and particle will drain, leading to a critical thickness at which rupture occurs. This is then followed by move- ment of the three-phase-line contact line (the bound- ary between the solid particle surface, receding liquid phase and advancing gas phase) until a stable wetting perimeter is established. This sequence of drainage, rupture and contact line movement constitutes the second process of attachment. A stable particle } bubble union is thus formed. The particle may only be dislodged from this state if it is supplied with suf R cient kinetic energy to equal or exceed the detachment energy, i.e. a third process of detach- ment can occur.
In Capture, you can organize your schematics into flat or heretical structures and interface to downstream EDA products using either flat (popular for PCB layout) or hierarchical (popular for synthesis and simulation) netlists. The schematic system also supports reuse of schematics within a hierarchy so you only need to draw a schematic once, then instance it in multiple places for a variety of applications. This guide uses the following nomenclature to describe the parts and part properties of these reused schematics:
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To identity enhancer targets, DNA fluorescence in situ hybridization (FISH) [59,60], as well as chromatin associ- ation methods (chromosome conformation capture (3C)) , can be employed. These are powerful approaches for evaluating whether a region of interest interacts with a specific genomic target, but they suffer from the limitation that the regions of interest must be pre-specified, that is, they are ‘one-by-one’ approaches. 4C (circular chromo- some conformation capture), an extension of 3C, can cap- ture all regions that physically contact a site of interest, without prior knowledge of the regions that contact that site being necessary  (that is, a ‘one-to-all’ approach). Higher-throughput methods include carbon-copy chro- mosome conformation capture (5C, many-to-many), a high-throughput expansion of 3C, Hi-C (all-to-all) and chromatin interaction analysis by paired-end tag se- quencing (ChIA-PET) (for detailed comparison of these methods, see reviews [63,64]). These global approaches can enable the identification of loci that directly and indir- ectly contact enhancers of interest, and can reveal com- plex interactions in which dozens to hundreds of loci aggregate, so-called transcriptional hubs or enhanceo- somes . These types of high-order interactions have been recently described by several studies [55,56,58]. The extent by which they overlap risk loci remains unexplored. Unfortunately, these approaches tend to be expensive and difficult for most labs to execute, and their resolution often prohibits their use for interrogating GWAS loci. Until recently, for example, the resolution of Hi-C was limited to capturing interactions separated by more than one megabase; 5 to 10 times greater than the distance by which most enhancer-gene interactions occur. Despite the limitations, ‘C’-based methods have been implemented to successfully identify targets of enhancer-risk variants and to quantify their functional effects. For example, Cowper- Sal lari and colleagues utilized 3C and allele-specific ex- pression to demonstrate the impact of the breast cancer risk SNP rs4784227 on expression of TOX3, thought to have a role in chromatin regulation . Bauer and co- workers utilized 3C to identify BCL11A as the gene target of an erythroid enhancer, and then further demonstrated the impact of enhancer variants on transcription factor binding and expression. Gene editing strategies have also been employed to demonstrate that this enhancer is essen- tial for erythroid gene expression . Finally, we highlight
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When port mirroring, be aware of the throughput of the ports you are mirroring. Some switch manufacturers allow you to mirror multiple ports to one individual port, which may be very useful when analyzing the communi- cation between two or more devices on a single switch. However, consider what will happen using some basic math. For instance, if you have a 24-port switch and you mirror 23 full-duplex 100Mbps ports to one port, you could potentially have 4,600Mbps flowing to that port. This is obviously well beyond the physical threshold of a single port and can cause packet loss or network slowdowns if the traffic reaches a certain level. In these situations switches have been known to completely drop excess packets or “pause” their back- plane, preventing communication altogether. Be sure that this type of situation doesn’t occur when you are when trying to perform your capture.
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Immediately related to the previous theme, this one regards the usage of information which would be provided by a more comprehensive capture of patient data. It was thought that this would enable better understanding of the quality of treatments and provide new insight by cross-referencing the new data with that already captured by existing hospital formation systems, as suggested by E1: "Actually, we can link that [data recorded by proposed system] by date by looking at their other stuff [existing patient data]- which is actually probably better…yeah so in terms of… - it's about being able to put them in the same place or link them at some point."
It is important to note that the auxiliary information z is associated with a contrast r = x − y , so it typically comprises a couplet of information about the original offspring points x and y. It might therefore be tempting to assume that z is independent of the contrast distance r , in other words, that f (z|r) = f (z). However, care is needed with this assumption. If the observations in z reflect some property of the parents of the two points x and y, then z is likely to depend upon r due to the influence of r on the probability that x and y share a common parent. If x and y are close (small r ), they are more likely to have the same parent than if they are distant (large r ). For the purpose of capture–recapture studies, the “parent” of a point x corresponds to the animal who deposited the sample at x, so auxiliary in- formation will typically be connected with the parent and we need to derive the influence of r on f (z | r).
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Serum samples and viruses. A total of 153 acute-phase (i.e., days 1 to 7 after onset) serum samples from 108 DENV1-infected patients (65 patients provided a single serum sample, 41 patients provided two serum samples, 2 patients provided three serum samples) were collected during the DENV1 epidemic in Guangzhou, China, in 2006 (2, 22). Another 30 acute-phase serum samples from DENV2-infected patients were collected in Guangdong Province, China, in 2001, as described in our previous report (12). Seven acute-phase serum samples from DENV3-infected patients were collected in Guangzhou in 2009. Laboratory diagnosis of dengue virus infection was performed at the Center for Disease Control and Prevention of Guangzhou, Guangzhou, China, with virus isolation, RT-PCR, and/or serological tests by the Dengue IgM capture ELISA (catalogue number EDEN01M; Panbio Diagnostics, Brisbane, Australia) and Dengue IgG capture ELISA (catalogue number E-DEN02G; Panbio Diagnostics). Infection status (primary or secondary) was classified as follows: a serum sample with a positive result for IgM antibody and a negative result for IgG antibody or a negative IgG test result for a serum sample collected at 3 to 4 days after disease onset, followed by seroconversion in the convalescent-phase serum sample, was considered to be from a primary infection; a serum sample positive or negative for IgM antibody but positive for IgG antibody was considered to be from a secondary infection, according to the criteria of Vazquez and colleagues (16). Acute-phase serum specimens obtained from nondengue febrile patients with other flavivirus or nonflavivirus infections confirmed by RT-PCR, IgM detection, or seroconversion of IgG were also used in the study. Control serum specimens were obtained from 500 healthy humans. The flaviviruses used in this study included strains of each of the four DENV serotypes (DENV1, Hawaii; DENV2, New Guinea-C; DENV3, Guanxi-80-2; DENV4, H241) and the attenuated live vaccine
This is a tool which comes along with the Android Software Development Kit (SDK) Dalvik Debug Monitor Server (DDMS) is also integrated in eclipse. It provides screen capture service, thread information of device, logcat process, radio information, call and short message service spoofing and so many other functions. DDMS works with both emulator and a connected device. If both are connected and running simultaneously, DDMS defaults to emulator. In Android, each application runs its own process on its own virtual machine (VM).