Reviewerly/exHarmony huggingface dataset:
A Convolutional Self-Attention Network for CSI Reconstruction in MIMO System

A convolutional self-attention network-based channel state information reconstruction method is presented to address the issue of low reconstruction accuracy of channel state information in Multiple-Input Multiple-Output (MIMO) at a high compression rate. First, an encoder-decoder structure-based channel state information reconstruction model is built. The feature is extracted by the encoder’s convolutional network, and the information is compressed by adding an attention block. At the same time, the compressed information is nonuniform quantized to prevent the transmission process from using up too much bandwidth. A dequantization module and an attention block are added to the decoder to reduce the impact of noise on the matrix, converting the continuous value into a discrete value to increase reconstruction accuracy and using the long-time cosine annealing training approach. According to the simulation results, when compared to CsiNet, Lightweight CNN, CRNet, and CLNet, convergence speed is improved by 17.64%, indoor reconstruction precision is improved by an average of 37.4%, and outside reconstruction accuracy is improved by an average of 32.5% under all compressions.
Efficient Identity-Based Multi-Cloud Security Access Control in Distributed Environments

Distributed computing is a forceful idea in disseminated registering which depicts versatile information to the executives for a minimal price dependent on client interest to various business associations. Because of multi-cloud identity-based encryption over distributed environment, in this document, the authors present and implement a novel identity-based multi-cloud security access control approach (NIMSACPA) for efficient security in multi data security and privacy based on three basic parametric concepts: 1) open minded security between autonomous user privacy using Byzantine protocol, 2) to classify the security privileges with respect to multi-cloud data sharing is described using DepSky Architecture, and 3) for identity-based information distribution between diverse users in CC described using Shamir secret key sharing procedure. This execution gives better and critical execution as far as data stockpiling and information investigation contrast and existing cryptographic techniques alongside practical multi-cloud information.
Effects of Computer-Based Training in Computer Hardware Servicing on Students' Academic Performance

This study determined the effects of computer-based training in computer hardware servicing with a pedagogical agent named “DAC: The Builder” on the academic performance of computing students. Fifty-six university students (30 students in the control group, 26 students in the experimental group) participated in a two-week experiment. The majority of the experimental group exhibited gaming behavior but subsequently reduced it after DAC intervention. The data collected in this study showed that the null hypothesis stating that there is no significant difference in the pretest and posttest scores of the experimental group can be rejected. Moreover, the hands-on posttest scores of both groups had significant differences. This study demonstrates that returning students to the lecture when they exhibit gaming the system behavior is an effective tool for discouraging this behavior. The use of the DAC is therefore recommended for students taking up computer hardware servicing. Implications and recommendations were also discussed.
Deep learning-based single-shot absolute phase retrieval using a triangular-wave embedded fringe coding strategy

In the field of 3D measurement, fringe projection profilometry attracts the most interest due to its high precision and convenience. However, it is still challenging to retrieve the unambiguous absolute phase from a single fringe image. In this paper, we propose a deep learning-based method for retrieving the absolute phase of triangular-wave embedded fringe images. Through the learning of a large amount of data, we use two neural networks to obtain high-precision wrapped phase and coarse absolute phase from the triangular-wave embedded fringe images respectively so as to obtain accurate fringe order. Combining the wrapped phase and fringe order, we can obtain high-precision absolute phases. The experimental results demonstrate that compared with our previous proposed composite dual-frequency fringe coding strategy, the fringe image of the new triangular-wave embedded fringe coding strategy as the input of the network can obtain the absolute phase with higher accuracy.
Accuracy analysis of fringe projection profilometry using raytracing algorithm and BP neural network

Fringe projection profilometry is widely used in manufacturing and the accuracy analysis is the key to promote this technology in engineering applications. Research analyzes influencing factors including gamma effect, intensity noise, defocus and methods are proposed to improve the measurement accuracy. However, an analytical study is difficult to perform, and the surface shape of the measuring objects influence the fringe images which needs to be considered. In this paper, raytracing algorithm and back-propagation network are used to study the relationship between the surface shape and measurement accuracy. The fringe projection profilometry system is simulated in computer using the raytracing algorithm and the light transport coefficients are measured to improve the accuracy of the camera defocus simulation. The impact of surface shape on fringe images is analyzed, and the projection and observation angles are used as the input of the network. The truth value of the surface is known in the simulation model thus the error of the coordinate can be obtained after simulation measurement and used as the output of the network. Experiment shows that, high correlation exists between the surface shape and the coordinate error.
Combined method of face super-resolution and rotation

Processing of face images is an important branch of machine vision. In real scenarios, the quality of the acquired images often does not reach the ideal condition therefore producing wrong results. While face super-resolution and rotation are two different ways to enhance the quality of face images, these two methods have always existed independently. The super resolution of face is to improve the image quality and get high resolution image from low resolution face image, and the rotation of face is to get the image under different view of the target and enrich the target person information. Both techniques have different approaches, but both are designed to improve the image quality for easier processing such as classification, detection and recognition. Therefore, can we combine the two methods, convert an image from a low-resolution image to a high-resolution image, and then use face rotation to obtain images of other views of the target face. We can obtain a higher quality image compared with the two independent methods. In this paper, we use a supervised learning method for face super-resolution and a self-supervised face rotation method for combining experiments, and the results show the reliability of combining the two methods.
An intensity-density associative clustering method for big millimeter-wave image data

The three-dimensional (3D) Millimeter-Wave (MMW) image is a big data with redundant information that not only blurs the image but also increases the computational load on the denoising procedure. To address this problem, we propose an intensity-density associative clustering method which mainly consists of two phases. Specifically, an intensity–based clustering algorithm, e.g., K-Means, is firstly applied on the amplitude of the image data and initially achieves denoising and data compression. Then, a density-based spatial clustering algorithm, e.g., DBSCAN, is used to further extract object information. Due to the label transferring from the retained amplitude, only valid image data can forward spatial information to DBSCAN, and as a result, the computational load on DBSCAN decreases. Also, a multi-threaded parallel computing framework is developed to exploit the distributed multicore processing for its implementation. Therefore, the proposed method can be well-adapted for 3D MMW image data in a computational efficient way. Simulations and experimental results confirm the effectiveness of our method that has good efficiency on big MMW image data with respect to the noise suppression and object information extraction.
An image stitching algorithm with improved Sigmoid function weights

Image stitching can combine multiple adjacent images with overlapping areas into a single high-resolution panoramic image that depicts the complete information about the target scene, this technique can be well applied to the visual measurements of optical profilometry instrument. However, there may still be relatively obvious splicing traces at the edges of the overlapping areas after image stitching with traditional weighted average fusion algorithm or linear weighted gradual fading-in and fading-out fusion algorithm. To address this issue, this paper proposes an image stitching algorithm that uses the improved Sigmoid function to calculate the non-linear weights. Firstly, the extended phase correlation operation and image resampling are used for image registration, and then the weight information of the target image is calculated by using the improved Sigmoid function, in other words the original Sigmod function is scaled, so that the function is shifted right and stretched as a whole, and the domain is set as the left boundary of the overlapping area to the right boundary of the overlapping area, the range corresponding to the domain is regarded as the weight information of the target image, the weight of the reference image and the weight of the target image satisfy the relationship of summing to one. Finally, the calculated two non-linear weights are used for image stitching. The experimental results show that the improved image fusion algorithm works better whether the camera is shooting with or without exposure differences.
Multi-point measurement using DIC based on UAV platform

Displacement measurement method provides quantitative information for structural safety evaluation and maintenance purposes. To overcome the difficulties in the outdoor measurement of large-span bridges, a high-resolution camera on an Unmanned Aerial Vehicle (UAV) was used to implement the deflect detection. First of all, the position of the UAV (including three displacements and three angles) was calibrated based on the speeded up robust features (SURF) algorithm. The SURF algorithm was used to extract the position information of the static areas in the image, and the calibration motion of the UAV was obtained by comparing the position changes of the static areas. The calibration used several self-designed marks with dots and circles to improve the accuracy. Then, the actual vibration of target was extracted from the raw data by subtracting the calibrated motion of the UAV. The numerical simulation indicates that the maximum error of displacement measurement can be controlled within 0.2 pixels. The calibrated experiments in the lab demonstrate that the overall correction accuracy of the displacement measurement is about 1.3 pixels. However, the maximum error of displacement measurement can be decreased to about 0.3 pixels if the rotation of UAV can be restrained by using damping mechanical devices.
A non-target calibration method of structured light system parameters against temperature variation

n industrial application of structured light systems (SLS), besides the measurement accuracy, the robustness in long-time working situations is also significant. This requirement is hard to be satisfied due to the drifting of system parameters caused by factors such as the generated heat of the devices and the temperature variation of the environment. To ameliorate this drawback, this paper proposes a re-calibration method of SLS parameters in temperature-varying situations based on utilizing a series of circle marks as control-points. In this method, after initial calibration, a measurement of the whole workspace including these control-points is carried out as soon as possible, in which a series of two-directions phase images and an extra white image are captured. Through phase calculation and reconstruction, the precise poses of the control-points can be obtained and saved. Then, cost function is constructed by utilizing these control-points based on bundle adjustment and the system parameters are optimized. Compared with previous re-calibration methods, since extra constraint of relative pose between the camera and the projector is provided by registered circle marks, a target object with ground truth used to calculate the scale information in previous methods is not necessary anymore, which improves the compatibility of SLS application. Experiments are also carried out to verify the performance of this method.
Measurement of transparent profile using multispectral 3D digital image correlation

Three-dimensional (3D) digital image correlation (DIC) based on fluorescent speckle pattern is becoming popular for measuring 3D profiles and deformations. However, the simultaneous monitoring of both the front and rear surfaces remains challenging due to the different refractive indexes of light in different media. In this study, we utilized a multispectral 3D DIC method using fluorescent speckle patterns and a 3CCD camera for the front and rear surfaces measurement of transparent objects. Fluorescent speckle patterns excited red or blue light were sprayed on the interested surfaces before measurement. Then, the red channel and blue channel sub-systems of a virtual 3D DIC system with a 3CCD camera were used to simultaneously capture both the front and back surfaces, respectively. To unify the sub coordinate systems of different channels, a calibration model of the 3CCD camera considering the sensor designed optical path difference (OPD) was proposed. Furthermore, profile reconstruction was corrected based on Snell's law to eliminate the refractive distortion caused by the different refractive indexes. The experimental results of transparent flat and curved objects showed that the accuracy of this method was significantly improved.
Real-time 3D face reconstruction based on geometry

We developed a novel method based on geometry to reconstruct a dynamic face solely by using the conventional structured light system, which does not need extra images, hardware components, or objects. In our method, the bridge of the nose is considered the feature region because its shape remains almost constant when a human is moving or making expressions. This three-dimensional (3D) face reconstruction method consists of the following steps: (1) spatially unwrap the phase and obtain the texture image for each frame; (2) locate the nose area for each frame with a feature detection algorithm; (3) determine the fringe order in the first frame with the cues given by traditional methods and acquire the recovered result of the feature region; (4) locate the feature region for each possible fringe order in one frame by finding the pixel nearest to the camera along the depth direction in the nose area; (5) register each 3D shape of the feature region with it in the former frame by the Iterative Closest Point (ICP) algorithm; (6) determine the fringe order by the minimal Hausdorff distance between two registered 3D shapes and then construct the face with the entire absolute phase map. Experimental results indicate that our proposed approach is capable of real-time reconstruction of a dynamic face and only three phase-shifted fringes are required per frame.
3D shape, deformation and strain measurement on complex structure using DIC-assisted fringe projection profilometry

Stereo digital image correlation (3D-DIC or Stereo-DIC) has advantages of high accuracy and flexibility and is widely used for 3D shape and deformation reconstruction. However, it is difficult to retrieve information of complex structure due to the severe perspective distortion in both views and area-based matching algorithm. And on the other hand, fringe projection profilometry (FPP) has pixelwise 3D shape reconstruction ability for complex structure but lacks accurate deformation and strain analyzing ability. In this work, we combine DIC with FPP to simultaneously obtain accurate 3D shape and deformation information and further perform strain analysis. First, the complete 3D shape of complex surface is reconstructed pixel by pixel using FPP. Next, the modulation of the phase-shifting fringes is extracted as texture maps to eliminate the interference of ambient light and for further DIC processing. DIC is only used to perform temporal matching for pixel-by-pixel tracking on reconstructed 3D shape. The in-plane and out-of-plane deformations are obtained simultaneously by directly comparing the complete 3D data for each corresponding pixel. Moreover, the strain in each direction is calculated by differencing the deformation data with chain rule. Experiments on the complex dynamic scene demonstrate the feasibility of the proposed method and show potential in analysis of specimen with complex structure.
Measurement viewpoint planning method for sheet metal parts with strong reflection

Limited by the imaging dynamic range of the camera, the phenomenon of over-exposure and over-dark often occurs in the 3D measurement of strong reflective sheet metal parts, resulting in incomplete measurement result. One of existing methods such as multiple exposure can measure most of the visible area under a single viewpoint, but the visible area with too small or too large incidence angle still cannot be measured. To solve this problem, in this paper, a method of viewpoint planning for sheet metal parts with strong reflection is proposed. The method introduces the surface reflection model of reflective sheet metal parts into viewpoint planning to achieve the synchronous optimum of measurement efficiency and data integrity. Firstly, according to the measurable region of the surface structured light 3D measurement system and CAD model, the candidate viewpoint set is randomly generated in the sampling space, and the visibility matrix is constructed by analyzing whether each candidate viewpoint is visible to each patch of the model. Then, the surface reflection model of sheet metal parts with strong reflection is constructed, and the reflection coefficient of the visible patches under each viewpoint is calculated according to the reflection model. Based on this, the measurability of the visible patches of the viewpoint under multiple exposures is calculated, and the visibility matrix is updated. Lastly, through the viewpoint quality evaluation function constructed based on the data coverage increment and multiple-exposure time, the viewpoint with the highest quality is selected heuristically until the coverage requirement is met. Experiments show that the algorithm can improve the measurement efficiency and ensure the integrity of the measurement data.
Cylindrical CGH wavefront calibration method based on transmission holography

Computer-generated holograms (CGH) can generate the ideal wavefront required for any aspheric surface to be measured for phase compensation. It is usually used to create a cylindrical wavefront to replace the null compensator in cylindrical interferometry. Compared with the null compensator, the error components introduced by CGH are more complex, including substrate error, etching error, coding error, etc. The error calibration of the wavefront generated by the CGH can effectively improve the accuracy of the measurement results. Since CGH will generate multi-order diffraction wavefronts, there is frequency aliasing in multiple groups of fringes, so its wavefront cannot be measured directly. A physical model of the multi-order diffracted wavefronts emitted by CGH is established, and the interference of multiple sets of wavefronts is simulated numerically. To solve the frequency aliasing problem, a tilted carrier beam is introduced by using transmission holography to separate the frequency of the target wavefront from those of the other orders. Thus, the frequency of the target order is extracted, and the cylindrical wavefront is recovered according to its frequency. In this paper, the distribution of experimental fringes is consistent with the simulated fringes, which verifies the accuracy of the physical model. The simulation results show that the proposed method can theoretically separate and recover the cylindrical wavefront well, proving the method's theoretical feasibility.
An automated 3D shape measurement method for turbine blade using a 6-axis robot

High-accuracy 3-D shape measurement of turbine blade is important for manufacturing engine and aircraft’s safety. At present, the most commonly used three-coordinate measuring machine in the industrial field may cause scratches on the surface of turbine blade. Optical 3-D measurement methods, such as fringe projection profilometry, are unable to carry out targeted measurement of geometric structures with different levels of precision. This paper presents an automatic measurement method based on pneumatic gripper and 6-axis robot. The proposed composite system includes global sensor with large scanning range and local sensor with high accuracy, which can perform 3-D reconstruction with specific resolution for different positions of turbine blade. We use a 6-axis robot and pneumatic gripper to grasp the turbine blade automatically. The 3-D scanning is completed according to the viewpoints planned in advance. The point cloud data of different angles are aligned to obtain a high precision 3-D model of turbine blade surface. The experimental results show that the proposed automatic scanning method can effectively avoid the measurement error caused by manual grasping operation. On the basis of complete 3-D measurement of the whole surface, more dense point cloud data are obtained for fine structures such as blade edge. Our result is of great significance to the field of industrial measurement.
Combined line laser 3D measurement method based on binocular vision

With the continuous development of domestic and foreign manufacturing industries, there are limitations in the omnidirectional three-dimensional measurement of large parts. In order to improve the 3D measurement range, speed, and accuracy of single-line lasers, a combined measurement method based on binocular vision positioning technology is proposed. First, the circular markers are pasted around the line laser sensor, and a virtual intermediate coordinate system is constructed based on the centers of the marked points. Secondly, a feature point energy matching algorithm is used to complete the corresponding points matching of the circular markers, and the position of the circular markers is solved based on the SVD method. Third, the relative pose relationship between the virtual intermediate coordinate system and the line laser sensor coordinate system is completed based on the hand-eye calibration model. Fourth, combined with the circular markers pose and relative pose, the local point cloud data of the line laser is unified into the global coordinate system to realize point cloud splicing. The experimental results show that the average measurement error of the measurement system is less than 0.23 mm, which basically meets the requirements of general shape measurement.