Alignment Procedure Research Articles

Machine Learning-Assisted Beam Alignment for mmWave Systems

Beam alignment is a challenging and time-consuming process for millimeter wave (mmWave) systems, particularly as they trend towards higher carrier frequencies which require ever narrower beams. We propose a beam alignment method that is assisted by machine learning (ML), where we train ML models to predict the optimal access point (AP) and beam – or the best few candidates – for a user equipment (UE) given just its GPS coordinates, which can be fed back by the UE or estimated by the network using emerging localization techniques. We train and evaluate the models with data generated by a state-of-the-art commercial ray-tracing software in a realistic urban topology. Even with dynamic scatterers and imperfect UE coordinates, our proposed method can greatly reduce the search space (number of candidates) for finding the optimal AP and beam. For example, in a 28 GHz scenario with 64 beams, our method reduces the search space by approximately 4x for AP selection and over 10x for beam selection while achieving over 95% accuracy. We provide our dataset and models for ease of reproducing and extending our results, which suggest that UE localization coupled with suitably trained ML models can significantly speed up current beam alignment procedures.

Surrogate modeling of the CLIC final-focus system using artificial neural networks

Artificial neural networks can be used for creating surrogate models that can replace computationally expensive simulations. In this paper, a surrogate model was created for a subset of the Compact Linear Collider (CLIC) final-focus system. By training on simulation data, we created a model that maps sextupole offsets to luminosity and beam sizes, thus replacing computationally intensive tracking and beam-beam simulations. This model was then used for optimizing the parameters of a random walk procedure for sextupole alignment.

In situ evaluation of plasmonic enhancement of gold tips for plasmon-enhanced imaging techniques.

Plasmonic nanoantennas are at the core of various optical near-field scanning techniques such as tip-enhanced Raman spectroscopy as they provide the amplification and confinement of the electromagnetic field, which ultimately provides sensitivity and spatial resolution. With a cornucopia of different fabrication methods available, the actual performance of a nanoantenna is often only assessed by whether or not near-field imaging is possible, implying the complete alignment and landing procedure of the scanning probe. We present a semi-quantitative approach to assess the plasmonic enhancement of gold tips via localized surface plasmon resonance (LSPR) enhancement of intrinsic gold photoluminescence without the need for interaction with the sample. As the intensity of the plasmon at the apex decreases, a significant change in the shape of the tip signal spectrum is observed, reflecting itself as a decrease in the R2 value (fit quality) for numerical fitting with a Lorentzian, which also provides an approximation for the LSPR wavelength. Our findings suggest that the potential of a tip to perform well as an optical near field antenna may already be assessed in an early stage of the experiment.

In Situ Assembly of Ordered Hierarchical CuO Microhemisphere Nanowire Arrays for High-Performance Bifunctional Sensing Applications.

Seeking a facile approach to directly assemble bridged metal oxide nanowires on substrates with predefined electrodes without the need for complex postsynthesis alignment and/or device procedures will bridge the gap between fundamental research and practical applications for diverse biochemical sensing, electronic, optoelectronic, and energy storage devices. Herein, regularly bridged CuO microhemisphere nanowire arrays (RB-MNAs) are rationally designed on indium tin oxide electrodes via thermal oxidation of ordered Cu microhemisphere arrays obtained by solid-state dewetting of patterned Ag/Cu/Ag films. Both the position and spacing of CuO microhemisphere nanowires can be well controlled by as-used shadow mask and the thickness of Cu film, which allows homogeneous manipulation of the bridging of adjacent nanowires grown from neighboring CuO hemispheres, and thus benefits highly sensitive trimethylamine (TMA) sensors and broad band (UV-visible to infrared) photodetectors. The electrical response of 3.62 toward 100ppm TMA is comparable to that of state-of-the-art CuO-based sensors. Together with the feasibility of in situ assembly of RB-MNAs device arrays via common lithographic technologies, this work promises commercial device applications of CuO nanowires.

Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors.

The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or "anchors" as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.

Facial scanning accuracy depending on the alignment algorithm and digitized surface area location: An in vitro study

ObjectivesTo measure the accuracy (trueness and precision) of a facial scanner depending on the alignment method and the digitized surface area location. MethodsFourteen markers were adhered on a head mannequin and digitized using an industrial scanner (GOM Atos Q 3D 12 M; Carl Zeiss Industrielle Messtechnik GmbH). A control mesh was acquired. Subsequently, the mannequin was digitized using a facial scanner (Arc4; Bellus3D) (n = 30). The control mesh was delineated into 10 areas. Based on the alignment procedures, two groups were created: reference best fit (RBF group) and landmark-based best fit (LA group). The root mean square was used to calculate the discrepancy between the control mesh and each facial scan. A 2-way ANOVA and Tukey pairwise comparison tests were used to compare trueness and precision between the 2 groups across 10 areas (α = .05). ResultsBoth alignment algorithms (P = .007) and digitized area (P < .001) were significant predictors of trueness with a significant interaction between the two predictors (F (9, 580) =25.13, P < .001). Tukey pairwise comparison showed that there was a significant difference between mean trueness values of RBF (mean=0.53 mm) and LA (mean=0.55 mm) groups. Moreover, a significant difference was detected among the trueness values across surface areas. The A9-area (left tragus area) had the highest and A5-area (right cheek area) had the lowest mean trueness. Both alignment algorithm (P < .001) and digitized surface area (P < .001) were significant predictors of precision with a significant interaction between the two predictors (F (9, 580) =14.34, P < .001). Tukey pairwise comparison showed that there was a significant difference between mean precision values of RBF (mean=0.38 mm) and LA (mean=0.35 mm) groups. Moreover, a significant difference was detected among the precision values across surface areas. Comparing the surface areas, A9-area had the highest and A10-area (forehead area) had the lowest mean precision. ConclusionsAlignment procedures influenced on the scanning trueness and precision mean values, but the facial scanner accuracy values obtained were within the clinically acceptable accuracy threshold of less or equal than 2 mm. Furthermore, the scanning accuracy (for both trueness and precision) depended on the location of the scanned surface area, being more accurate on the middle of the face than on the sides of the face.

Compact Robust Vector Bending Sensor Based on Single Stress-Applying Fiber

We propose a compact assemble-easy cost-effective vector bending sensor consisting of a short section of single stress-applying fiber (SSAF) spliced between two standard single-mode fibers (SMFs). The SSAF is specially designed by introducing a stress-applying part (SAP) into the cladding to achieve the asymmetrical geometry. Because of the mode field mismatch between the SMF and the SSAF, the cladding mode is effectively excited, which is sensitive to the bending and has orientation-dependence. Due to the large effective refractive index difference between the core and cladding mode, a compact dual-mode interferometer can be achieved with a thousands-micron-long structure. When the fiber is bent, the mode fields and the effective refractive indices will be modified, and accordingly, the direction-dependent interference spectral shift is observed. Owing to the well-designed SSAF structure, the cost-effective vector bending sensor can be easily realized by a simple sandwich structure without extra complex alignment procedures or fiber grating inscription. We experimentally verify the vector bending property. The maximum sensitivity of 2.04 nm/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> is obtained, and the experimental results match well with the simulations. Our proposed device is compact, easy to assemble, and cost-effective, which is promising in the field of vector bending measurement.

Quantitative analysis and benchmarking of positional accuracies of neutron strain scanners

Positional accuracy is an important parameter in residual stress investigations with neutron diffraction, considering that precise measurements of strains at the same localised position along a number of sample orientations are required, including investigations of complete complex shaped engineering components. This study reports the development of a standardised approach for quantitative analysis of positional accuracy on neutron strain scanners that builds on previous campaigns. The approach uses standardised sample sets with specific geometries that enable quantitative assessment of instrumental and sample alignment procedures and associated accuracies. This method has been implemented on four participating instruments: ENGIN-X (United Kingdom), MPISI (South Africa), SALSA (France) and STRESS-SPEC (Germany), to render results representative of monochromatic and time-of-flight strain scanners. The benchmarking results show comparable performance between the instruments with positional accuracies around 100 μm readily achieved. This standardised approach confirms the high positional precision attainable for non-destructive stress determination, to unequivocally benefit utilisation by academia and industry alike. It is envisaged that this common calibration protocol and reporting template that conforms to the newly developed Neutron Quality Label for Internal Stress Characterisation be adopted by other facilities to facilitate expansion of the supportive network.

Evaluation of an Ultrasound-Based Navigation System for Spine Neurosurgery: A Porcine Cadaver Study.

BackgroundWith the growing incidence of patients receiving surgical treatment for spinal metastatic tumours, there is a need for developing cost-efficient and radiation-free alternatives for spinal interventions. In this paper, we evaluate the capabilities and limitations of an image-guided neurosurgery (IGNS) system that uses intraoperative ultrasound (iUS) imaging for guidance.MethodsUsing a lumbosacral section of a porcine cadaver, we explored the impact of CT image resolution, ultrasound depth and ultrasound frequency on system accuracy, robustness and effectiveness. Preoperative CT images with an isotropic resolution of , and were acquired. During surgery, vertebrae L1 to L6 were exposed. For each vertebra, five iUS scans were acquired using two depth parameters (5 cm and 7 cm) and two frequencies (6 MHz and 12 MHz). A total of 120 acquisition trials were evaluated. Ultrasound-based registration performance is compared to the standard alignment procedure using intraoperative CT. We report target registration error (TRE) and computation time. In addition, the scans’ trajectories were analyzed to identify vertebral regions that provide the most relevant features for the alignment.ResultsFor all acquisitions, the median TRE ranged from 1.42 mm to 1.58 mm and the overall computation time was 9.04 s ± 1.58 s. Fourteen out of 120 iUS acquisitions (11.66%) yielded a level-to-level mismatch (and these are included in the accuracy measurements reported). No significant effect on accuracy was found with CT resolution (F (2,10) = 1.70, p = 0.232), depth (F (1,5) = 0.22, p= 0.659) nor frequency (F (1,5) = 1.02, p = 0.359). While misalignment increases linearly with the distance from the imaged vertebra, accuracy was satisfactory for directly adjacent levels. A significant relationship was found between iUS scan coverage of laminae and articular processes, and accuracy.ConclusionIntraoperative ultrasound can be used for spine surgery neuronavigation. We demonstrated that the IGNS system yield acceptable accuracy and high efficiency compared to the standard CT-based navigation procedure. The flexibility of the iUS acquisitions can have repercussions on the system performance, which are not fully identified. Further investigation is needed to understand the relationship between iUS acquisition and alignment performance.

Improvement of a computer-aided alignment algorithm for the nonsymmetric off-axis reflective telescope

Due to complications of the off-axis three-mirror anastigmat (TMA) telescope, each optical element in the off-axis TMA telescope is introduced with theoretical eccentricity and tilt. Moreover, the introduction of freeform surfaces and other optical elements with complex surface features generally causes the initial alignment accuracy of the optical path to be low. A large initial alignment error amplifies the sensitivity of the misalignment calculation accuracy to the measurement error of the Zernike coefficient, resulting in difficulty obtaining convergence results for a computer-aided alignment algorithm. Considering the above issues, the alignment sensitivity of each component in the optical path is analyzed in this. The large conditional number of the sensitivity matrix results in poor algorithm robustness. Thus, an adaptive damping factor least-squares algorithm model is proposed and derived to improve the efficiency of the classical least-squares algorithm. A method for piecewise optimization of the damping factor is also deduced. Experiments based on a 0.6 m off-axis TMA telescope verify the effectiveness of the algorithm. Simulation and integration experiments show that the proposed method can reduce the accuracy requirements of the initial alignment and improve the adaptability of Zernike coefficient measurement noise. The alignment procedure is carried out for three iterations, and the average of the five field-of-view wave aberration values is enhanced from 2.1λ (RMS; λ=632.8nm) to 0.09λ (average). The improved algorithm can solve the large initial alignment error of a nonsymmetrical off-axis reflective optical system with a freeform surface as well as the problem of the low success rate of the misalignment value due to low Zernike coefficient measurement accuracy.

A 1D Lyman-alpha profile camera for plasma edge neutral studies on the DIII-D tokamak.

A one dimensional, absolutely calibrated pinhole camera system was installed on the DIII-D tokamak to measure edge Lyman-alpha (Ly-α) emission from hydrogen isotopes, which can be used to infer neutral density and ionization rate profiles. The system is composed of two cameras, each providing a toroidal fan of 20 lines of sight, viewing the plasma edge on the inboard and outboard side of DIII-D. The cameras' views lie in a horizontal plane 77cm below the midplane. At its tangency radius, each channel provides a radial resolution of ∼2cm full width at half maximum (FWHM) with a total coverage of 22cm. Each camera consists of a rectangular pinhole, Ly-α reflective mirror, narrow-band Ly-α transmission filter, and a 20 channel AXUV photodetector. The combined mirror and transmission filter have a FWHM of 5nm, centered near the Ly-α wavelength of 121.6nm and is capable of rejecting significant, parasitic carbon-III (C-III) emission from intrinsic plasma impurities. To provide a high spatial resolution measurement in a compact footprint, the camera utilizes advanced engineering and manufacturing techniques including 3D printing, high stability mirror mounts, and a novel alignment procedure. Absolutely calibrated, spatially resolved Ly-α brightness measurements utilize a bright, isolated line with low parasitic surface reflections and enable quantitative comparison to modeling to study divertor neutral leakage, main chamber fueling, and radial particle transport.

Quantifying steric hindrance and topological obstruction to protein structure superposition

BackgroundIn computational structural biology, structure comparison is fundamental for our understanding of proteins. Structure comparison is, e.g., algorithmically the starting point for computational studies of structural evolution and it guides our efforts to predict protein structures from their amino acid sequences. Most methods for structural alignment of protein structures optimize the distances between aligned and superimposed residue pairs, i.e., the distances traveled by the aligned and superimposed residues during linear interpolation. Considering such a linear interpolation, these methods do not differentiate if there is room for the interpolation, if it causes steric clashes, or more severely, if it changes the topology of the compared protein backbone curves.ResultsTo distinguish such cases, we analyze the linear interpolation between two aligned and superimposed backbones. We quantify the amount of steric clashes and find all self-intersections in a linear backbone interpolation. To determine if the self-intersections alter the protein’s backbone curve significantly or not, we present a path-finding algorithm that checks if there exists a self-avoiding path in a neighborhood of the linear interpolation. A new path is constructed by altering the linear interpolation using a novel interpretation of Reidemeister moves from knot theory working on three-dimensional curves rather than on knot diagrams. Either the algorithm finds a self-avoiding path or it returns a smallest set of essential self-intersections. Each of these indicates a significant difference between the folds of the aligned protein structures. As expected, we find at least one essential self-intersection separating most unknotted structures from a knotted structure, and we find even larger motions in proteins connected by obstruction free linear interpolations. We also find examples of homologous proteins that are differently threaded, and we find many distinct folds connected by longer but simple deformations. TM-align is one of the most restrictive alignment programs. With standard parameters, it only aligns residues superimposed within 5 Ångström distance. We find 42165 topological obstructions between aligned parts in 142068 TM-alignments. Thus, this restrictive alignment procedure still allows topological dissimilarity of the aligned parts.ConclusionsBased on the data we conclude that our program ProteinAlignmentObstruction provides significant additional information to alignment scores based solely on distances between aligned and superimposed residue pairs.

HR-MAS NMR Applications in Plant Metabolomics.

Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.

Alignment method for the accurate and precise quantification of tip-surface forces

We introduce a procedure to determine accurate and precise force-distance curves in dynamic force measurements utilizing a sharp tip. While single force curves are prone to systematic, often unnoticed errors, we present their self-consistent retrieval by an alignment procedure using repetitive measurements with the force probe oscillating at varied amplitude. By processing model data, we show that the procedure provides the valid force curve, the actual oscillation amplitude, and fully compensates thermal drift. The benefit of the method is demonstrated by application to experimental data.

Two-dimensional (2D) dynamic vibration optical coherence elastography (DV-OCE) for evaluating mechanical properties: a potential application in tissue engineering.

Mechanical properties in tissues are an important indicator because they are associated with disease states. One of the well-known excitation sources in optical coherence elastography (OCE) to determine mechanical properties is acoustic radiation force (ARF); however, a complicated focusing alignment cannot be avoided. Another excitation source is a piezoelectric (PZT) stack to obtain strain images via compression, which can affect the intrinsic mechanical properties of tissues in tissue engineering. In this study, we report a new technique called two-dimensional (2D) dynamic vibration OCE (DV-OCE) to evaluate 2D wave velocities without tedious focusing alignment procedures and is a non-contact method with respect to the samples. The three-dimensional (3D) Fourier transform was utilized to transfer the traveling waves (x, y, t) into 3D k-space (kx, ky, f). A spatial 2D wavenumber filter and multi-angle directional filter were employed to decompose the waves with omni-directional components into four individual traveling directions. The 2D local wave velocity algorithm was used to calculate a 2D wave velocity map. Six materials, two homogeneous phantoms with 10 mm thickness, two homogeneous phantoms with 2 mm thickness, one heterogeneous phantom with 2 mm diameter inclusion and an ex vivo porcine kidney, were examined in this study. In addition, the ARF-OCE was used to evaluate wave velocities for comparison. Numerical simulations were performed to validate the proposed 2D dynamic vibration OCE technique. We demonstrate that the experimental results were in a good agreement with the results from ARF-OCE (transient OCE) and numerical simulations. Our proposed 2D dynamic vibration OCE could potentially pave the way for mechanical evaluation in tissue engineering and for laboratory translation with easy-to-setup and contactless advantages.

Improvement of 3D Point Cloud Alignment for Super-Resolution Applications

With the prevalence of 3D scanners and 3D printers, manufacturing various 3D objects has become easier in recent years. When measuring the surface shape of an object using a 3D scanner, it is desirable to perform measurements at the highest possible resolution. However, there are many objects for which the resolution of commercial 3D scanners is insufficient. One solution to this problem is to apply super-resolution technology by measuring 3D data multiple times. It is crucial to align the 3D point clouds generated by multiple measurements accurately, but the conventional alignment methods are not accurate enough. This study aimed to improve the accuracy of the alignment process for 3D point clouds. The proposed method consists of the following four steps: (1) 3D point clouds are adaptively sampled. (2) A fast point feature histogram is used to extract features from the sampled point clouds. (3) The random sample consensus method is used to estimate an initial alignment. (4) The iterative closest point method is used to perform a precise alignment procedure. The feasibility of the proposed method is verified through experiments using real objects.

Automatic 3-D Measurement Method for Nonuniform Moving Objects

Phase-shifting profilometry (PSP) via sequential fringe projection is applicable for high-accuracy 3-D measurements of static objects. However, moving objects cannot be static or quasi-static during one projection cycle, thereby causing surface misalignment and phase errors in 3-D measurements. This article proposes a motion-estimated PSP based on the fringe-oriented synthetic phase correlation algorithm (FO-SPCA), which achieves automatic 3-D measurements of objects in nonuniform 2-D motion. To robustly estimate the motion of objects among fringe images, the proposed fringe-oriented feature extraction technique obtains the surface features by eliminating the modulation influence of sinusoidal illumination. Furthermore, the FO-SPCA is developed to accurately detect the subpixel displacements of moving objects according to object surface features. With the subpixel displacements, the alignment procedure is completed through a reverse 2-D transformation and then the phase errors are compensated accordingly. The simulation and experimental results verify the feasibility and robustness of the motion-estimated PSP for the in situ 3-D measurements of nonuniform moving objects.

Alignment and parameterization of single cycle motion data

Abstract Motion capturing systems produce a large amount of information on the motion of individuals. A growing number of data reduction techniques have been developed to reduce the amount of data while keeping relevant information. An overview that compares and identifies the advantages and disadvantages of these methods on cyclic motion data is, however, lacking. Therefore, this study aims to assess the features of different data reduction techniques by applying them to a large public gait data set. Due to the periodicity of cyclic data, an individual cycle can be isolated and analyzed. The analysis of single cycles requires preprocessing steps to segment and align the individual cycles. The latter is needed to isolate the amplitude variability. Three alignment procedures with different complexity, namely Linear Length Normalization (LLN), Piecewise LNN (PLLN) and Continuous Registration (CR), are assessed based on the amount of resulting variation. Subsequently three data reduction techniques (i.e. Principal Component Analysis (PCA), Principal Polynomial Analysis (PPA) and Multivariate Functional PCA (MFPCA)) are applied to the aligned single gait cycles. The data reduction techniques are evaluated based on the in-sample error, the out-of-sample error, the compactness and the computation time to produce a model. The curves aligned with CR have the lowest remaining variation and thus the lowest amount of remaining phase variation. The differences between the different data reduction techniques appear to be minimal. PPA shows to be the most compact and is therefore recommended when compactness is crucial and out-of-sample performance is less essential. The use of MFPCA is advised when one wants to include data from different sources. PCA is suggested when computation time is key.

Channel Estimation for 6G V2X Hybrid Systems Using Multi-Vehicular Learning

Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable spectral efficiency and mean square error compared to the unconstrained maximum likelihood estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal fully digital systems.

Keyword Search Using Attention-Based End-to-End ASR and Frame-Synchronous Phoneme Alignments

Attention-based end-to-end (E2E) automatic speech recognition (ASR) architectures are now the state-of-the-art in terms of recognition performance. However, despite their effectiveness, they have not been widely applied in keyword search (KWS) tasks yet. In this paper, we propose the Att-E2E-KWS architecture, an attention-based E2E ASR framework for KWS that can afford accurate and reliable keyword retrieval results. First, we design a basic framework to carry out KWS based on attention-based E2E ASR. We adopt the connectionist temporal classification and attention (CTC/Att) joint E2E ASR architecture and exploit the spike posterior property of CTC to provide the keywords time stamps. Second, we introduce the frame-synchronous phonemes modeling and use the dynamic programming (DP) algorithm to provide alignments between E2E grapheme outputs and phoneme outputs. We call this alignment procedure dynamic time alignment (DTA), which can provide the proposed Att-E2E-KWS system with more accurate time stamps and reliable confidence scores. Third, we use the Transformer, a self-attention-based encoder-decoder neural network, in place of conventional recurrent neural networks in order to yield more parallelizable models and increased training speed. We conduct comprehensive experiments on English and Mandarin Chinese. To the best of our knowledge, this is the first practical Att-E2E-KWS framework, and experimental results on Switchboard and HKUST corpora show that our proposed Att-E2E-KWS systems significantly outperform the CTC E2E ASR based KWS baselines.