To solve the performance bottleneck of algorithms such as these, the graphics processing unit (GPU) and recently field programmable gate arrays (FPGAs) have been incorporated to offload computations off of the CPU. The customizable and parallel nature of such accelerators allows developers the freedom to write device code that can result in immense performance speedups when working in unison with the CPU. Both the GPU and FPGA device codes can be written with little knowledge of low level circuit design by using languages such as CUDA or OpenCL, which are extensions to the C language. However, FPGA performance is best when written in a hardware description language such as VHDL, but VHDL has a steep learning curve because of the need to understand circuit design.
and negative muons in opposite directions. Such effects are present in prompt data reconstruction and cause a small mass shift for charge-asymmetric final states hard to see on the Z mass. It also slightly increases the width of the Z mass peak. These biases are studied and corrected in data by comparing the local inhomogeneities of the charge dependent dimuon mass to the mass of well-known neutral resonances. The correction improves the resolution of the dimuon invariant mass in Z-boson decays by 1% to 5%, depending on η and φ. The systematic uncertainty associated to this correction is estimated for each muon using simulation. In Fig. 3 we show the e ff ect of such a bias before (unfilled markers) and after (filled markers) the correction. After we correct positve and negative tracks show a better agreement. In Fig. 4 we show the residual e ff ect of this bias in the (η, φ)-map after we do correct. The residual biases are reduced to 0.2 per mille on the Z mass. In Fig. 5 and 6 we show the agreement between data and MC for the mass resolution and the dimuon mass scale of the pair which are directly related to the muon momentum resolution and scale. The dimuon mass resolution is obtained by fitting the width of the invariant mass peaks. The dimuon mass resolution is about 0.9% for p T ∼ 6 GeV in the barrel and 1.5% for p T ∼ 45 GeV. The agreement between data and MC for the
For the final validation of the Optimal Filtering method and the whole chain of the FATALIC readout system, the signal reconstructionperformance was tested with real data collected during a preliminary beam test at CERN . In Fig. 9 (a) the OF method is compared with the flat estimation method, which is the basic energy reconstruction corresponding to the simple summation of digitised samples. Clearly, the Optimal Filtering reconstruction provides a significant improvement of energy resolution compared to the simple summation approach. Fig. 9 (b) shows the two-dimensional plot of the energy reconstructed using the OF method for muon events recorded by two different channels of a single cell, and the expected correlation is observed accordingly.
together with results from performance evaluations and the determination of systematic uncertainties. This paper is organised as follows. The subsystems forming the ATLAS detector are described in Section 2. The E T miss reconstruction is discussed in Section 3. The extraction of the data samples and the generation of the Monte Carlo (MC) simulation samples are presented in Section 4. The event selection is outlined in Section 5, followed by results for E T miss performance in Section 6. Section 7 comprises a discussion of methods used to determine systematic uncertainties associated with the E T miss measurement, and the presentation of the corresponding results. Section 8 describes variations of the E T miss reconstruction using calorimeter signals for the soft hadronic event activity, or reconstructed charged-particle tracks only. The paper concludes with a summary and outlook in Section 9. The nomenclature and conventions used by ATLAS for E T miss -related variables and descriptors can be found in Appendix A, while the composition of E T miss reconstruction variants is presented in Appendix B. An evaluation of the effect of alternative jet selections on the E T miss reconstructionperformance is given in Appendix C.
In this section, we conduct extensive simulations in TOSSIM , a standard simulator for TinyOS programs, to reveal more system insights. Specifically, we evaluate the reconstructionperformance of iPath and three related works in networks with different configuration settings such as path length, routing dynamic, packet delivery ratio, and degree. A number of networks with up to 1000 nodes are used in the simulations. We also evaluate the impact of length of the hash value, which is the key parameter in the design of iPath. At the end of this section, we will show a visualization of the reconstruction process in a network with 400 nodes.
ensure the TMUX system within desired crosstalk attenu- ation levels. Our solution is given in terms of linear ma- trix inequalities (LMIs) which can be solved easily by con- vex optimization . As illustrated later, compared with the existing TMUX design method via LMI technique , the proposed method embodies two obvious advantages. First, when the reconstructionperformance is concerned, the pro- posed mixed H 2 /H ∞ optimization method provides less con-
postoperatively at the end of first week, first month and 6 months. 10(50%) patients were able to take more than 80% of pre-illness single meal quantity, 9(45%) were able to take 50%- 80% and 1(5%) patient was able to take less than 50% of the pre-illness single meal volume, findings comparable to those reported by YNakane et al (1995), Michielsen Det al (1996),Iivonen MK et al (2000), Lehnert T, Buhl K. (2004), Liedman B.(1999), Fujiwara Y et al (2000), Kono K et al (2003). The mean operative time recorded was 346.25±20.0575 minutes as opposed to mean operative time of 275 minutes for the procedure repoted by Karl-Hermann Fuchs et al (1995) less possibly due to use of anastomotic GI staplers, fact supported by Chua L (1998) where he in his comparative study of reconstructive procedures after total gastrectomy found that Hunt Lawrence J pouch reconstruction extends the operative time but less so with the use of GI staplers. The mean hospital stay was 22.1±4.376 days which was comparable to the results by Iivonen MK et al. (2000), 19 days. The procedure related complications in these patients were divided into immediate and late. Immediate complications included anastomotic leak 9(45%) and consequent wound infection 11(55%) as opposed to Iivonen MK et al(2000) where in the clinical leakage rate of 4% in the control group correspondswell with that in previous studies (Inberg et al. 1981, Ovaska et al. 1989), butthe clinical anastomotic leakage rate (19%) in the pouch group was higher. Two patients the pouch group had only radiological leakage in theoesophagojejunal anastomosis but no signs of infection, and they recovered asquickly as the patients without leakage. A possible explanation for theincreased leakage rate in the pouch group is that pouch reconstruction maycompromise the intestinal wall circulation, resulting in impaired healing of the anastomosis. A less likely explanation is increased luminal tension in the pouch, this should could well enough be decompressed by thenaso-pouch tube routinely inserted at operation, respiratory tract infection 4(20%),intra-abdominal abscess 3(15%), ileus 3(15%), pulmonary thromboembolism 1(5%). The late complications were regurgitation 14(70%), dysphagia 4(20%), early satiety 5(25%), diarrhoea 3(15%), the findings were consistent with the post operative morbidity profile submitted by Iivonen MK et al(2000), Michielsen D et al (1996) however contrary toYNakane et al (1995) where in no differences in the incidence of post operative complications were reported.
The purpose of this study is to develop a fast and convergence proofed CBCT reconstruction framework based on the compressed sensing theory which not only lowers the imaging dose but also is computationally practicable in the busy clinic. We simplified the original mathematical formulation of gradient projection for sparse reconstruction (GPSR) to minimize the number of forward and backward projections for line search processes at each iteration. GPSR based algorithms generally showed improved image quality over the FDK algorithm especially when only a small number of projection data were available. When there were only 40 projections from 360 degree fan beam geometry, the quality of GPSR based algorithms surpassed FDK algorithm within 10 iterations in terms of the mean squared relative error. Our proposed GPSR algorithm converged as fast as the conventional GPSR with a reasonably low computational complexity. The outcomes demonstrate that the proposed GPSR algorithm is attractive for use in real time applications such as on-line IGRT.
Over the last years a variety of distributed methods have been proposed. Recent exam- ples include Consensus Monte Carlo (Scott et al. (2016)), WASP (Srivastava et al. (2015)), distributed GP’s (Deisenroth and Ng (2015)), and methods proposed in Shang and Cheng (2015), Jordan et al. (2016), Lee et al. (2015), Volgushev et al. (2017), to mention but a few. Most papers on distributed methods do extensive experiments on simulated, benchmark and real data to numerically assess and compare the performance of the various methods. Some papers also derive a number of theoretical properties. Theoretical results on the perfor- mance of distributed methods are not yet widely available however and there is certainly no common theoretical framework in place that allows a clear theoretical comparison of methods and the development of an understanding of fundamental performance guarantees and limitations. Clearly we can not consider the complete list of all existing methods in this paper. We limit ourselves to a number of representative methods that are Bayesian in nature, allowing for a meaningful comparison.
Engineering analysis and collision reconstruction of vehicle crashes, including passenger vehicles, heavy trucks, motorcycles, bicycles, etc. Air brake performance and operation. Skid testing of passenger cars and heavy trucks. Analysis of collisions, rollovers, tire disablements, vehicle crush, vehicle dynamics, restraint systems, and other issues.
On the other side, the lost samples of an over- sampled signal can be reconstructed by building some equations based on valid samples [13-16]. In these methods, if the out-of-band components of the signal are removed completely, the reconstruction procedure fails [15,16]. On the basis of [14-16], a clipping noise cancellation model using oversampled signal is unveiled in . In this study, rst, the oversampled signal is clipped; then, some out-of-band components of the clipped signal are saved during out-of-band ltering procedure. At the receiver, the clipped samples of oversampled signal, considered as lost samples, are reconstructed by employing the least square method. The reconstruction using this method is quite robust against additive channel noise, but the penalty is high complexity.
Generally, θ j+1 − θ j = π K is a constant, i.e., the projection angles distribute equally in the region [ 0, π]. However, the frequency samples (2π k L cos θ j , 2π k L sin θ j ) do not distribute equally in the 2-D regions, such as [ − π, π] 2 . Therefore, (9) and (11) form 2-D DFT and IDFT using the non-uniform frequency sampling, and the non-ignorable distortion will be brought into the reconstructed image. This problem is very similar with the 1-D exam- ple in the previous subsection. In order to reduce the distortion, an additional filter is necessary. It is similar with F(k) in Figure 3, and is shown in Figure 5. Further more, the idea behind the design of the additional filter for CT reconstruction is quite similar with that for 1-D signal reconstruction.
An image acquisition system composed of an array of sensors, where each sensor has a subarray of sensing elements of suitable size, has recently been popular for increasing the spatial resolution with high signal-to-noise ratio beyond the performance bound of technologies that constrain the man- ufacture of imaging devices. The technique for reconstruct- ing a high-resolution from data acquired by a prefabricated array of multisensors was advanced by Bose and Boo , and this work was further developed by applying total least squares to account for error not only in observation but also due to error in estimation of parameters modeling the data . The method of projection onto convex sets (POCS) has been applied to the problem of reconstruction of a high- resolution image from a sequence of undersampled degraded frames. Sauer and Allebach applied the POCS algorithm to this problem subject to the blur-free assumption . Stark and Oskoui  applied POCS in the blurred but noise-free case. Patti et al.  formulated a POCS algorithm to com- pute an estimate from low-resolution images obtained by ei- ther scanning or rotating an image with respect to the CCD image acquisition sensor array or mounting the image on a moving platform .
ory, some state reconstruction schemes are derived for complex dynamical networks including state coupling and outputs coupling. For suppressing noise in the chan- nel, the integral observers [22,23] are applied and the estimation errors are bounded with H ∞ performance.
The empirical mode decomposition (EMD) was recently proposed as a new time-frequency analysis tool for nonstationary and nonlinear signals. Although the EMD is able to find the intrinsic modes of a signal and is completely self-adaptive, it does not have any implication on reconstruction optimality. In some situations, when a specified optimality is desired for signal reconstruction, a more flexible scheme is required. We propose a modified method for signal reconstruction based on the EMD that enhances the capability of the EMD to meet a specified optimality criterion. The proposed reconstruction algorithm gives the best estimate of a given signal in the minimum mean square error sense. Two diﬀerent formulations are proposed. The first formulation utilizes a linear weighting for the intrinsic mode functions (IMF). The second algorithm adopts a bidirectional weighting, namely, it not only uses weighting for IMF modes, but also exploits the correlations between samples in a specific window and carries out filtering of these samples. These two new EMD reconstruction methods enhance the capability of the traditional EMD reconstruction and are well suited for optimal signal recovery. Examples are given to show the applications of the proposed optimal EMD algorithms to simulated and real signals.
The performance of a super-resolution (SR) reconstruction method on real-world data is not easy to measure, especially as a ground-truth (GT) is often not available. In this paper, a quantitative performance measure is used, based on triangle orientation discrimination (TOD). The TOD measure, simulating a real-observer task, is capable of determining the performance of a specific SR reconstruction method under varying conditions of the input data. It is shown that the performance of an SR reconstruction method on real-world data can be predicted accurately by measuring its performance on simulated data. This prediction of the performance on real-world data enables the optimization of the complete chain of a vision system; from camera setup and SR reconstruction up to image detection/recognition/identification. Furthermore, diﬀerent SR reconstruction methods are compared to show that the TOD method is a useful tool to select a specific SR reconstruction method according to the imaging conditions (camera’s fill-factor, optical point-spread-function (PSF), signal-to-noise ratio (SNR)).
This article provides for the reconstructions of ATK-735 compressors, which is used in the production of weak-nitric acid to produce ammonia sletry. The purpose of the reconstruction of compressors, snow production costs, energy and manpower. This reconstruction allows stable work and reducing the unplanned shutdown of the machine, maintain cleanliness in the places where repair personnel work and preserve the global problem cleanliness of nature and people's health in the region.
Figure 3 shows a simpliﬁed sketch of the Belle II Data Acquisition (DAQ) system. The data of the SVD, the drift chamber and the sub-detectors for particle identiﬁcation (PID), calorimetry and muon detection are sent to the Higher Level Trigger (HLT) . The data from the SVD are additionally sent to the DATCON, which performs online track reconstruction, extrapolation to the PXD, and calculation of ROI’s in the PXD. The ROIs are then sent to the Online Selector Node (ONSEN) . Besides DATCON, the HTL is the second system that performs a calculation of ROI’s in the PXD. For this task the HLT not only can use data from the SVD, but also from the drift chamber and the other sub detectors. In addition, HLT provides the trigger signal for the complete detector and pipelines the data of the sub-detectors, except PXD, to the storage. The PXD data are only sent to ONSEN, which merges the ROI’s of HLT and DATCON and performs the overall PXD data reduction by rejecting hits outside the ROIs. The HLT uses a computing farm with 6400 cores in total and runs sophisticated track ﬁnding and ﬁtting algorithms. These HLT algorithms will also be used in the later oﬄine track reconstruction. DATCON, on the other hand, runs a fast FPGA-based track reconstruction.