Reviewerly/exHarmony huggingface dataset:
3D measurement of large and complex parts based on phase matching and global marker points registration

Authors:
raw_author_name: Xudong Li
raw_affiliation_string: Beihang University (China)
raw_author_name: Yixuan Wang
raw_affiliation_string: Beihang University (China)
raw_author_name: Fei Ren
raw_affiliation_string: Shanghai Aerospace Equipments Manufacturer Co., Ltd. (China)
raw_author_name: Yong Tang
raw_affiliation_string: Beihang University (China)
raw_author_name: Huijie Zhao
raw_affiliation_string: Beihang University (China)

Abstract: Current requirements resulting from high quality standards and mass production call for an efficient and reliable manufacturing technology. With the aim of meeting the needs of accurate and efficient inspection of massive industrial parts, we propose a novel large-field measurement technique to measure weakly textured, complex reflective surface on large-size parts. We introduce a small-field-of-view, high-spatial-resolution binocular fringe projection local measurement system, which realizes high-precision point cloud data acquisition of industrial parts' surface based on the principle of heterodyne multi-frequency phase shift and high dynamic measurement. A new point cloud registration method based on phase matching and global marker points is proposed. A global measurement system with large field of view and low spatial resolution system is applied to track the local measurement system and realize the local area point cloud registration of large industrial components. The global marker points on the surface of the turntable jointly calibrated by the laser tracker and the vision system is presented to realize the registration of all areas of the component. A global optimization with trimmed ICP is applied for adaptive fine registration of multi-view point clouds with different overlap rates to eliminate accumulated errors. The proposed system can achieve a global measurement range of 4m×4m×2.5m, and the experimental results demonstrate the effectiveness of the proposed method for high-precision measurement of industrial complex parts with large field of view, which effectively avoid the transmission error and benefits the inspection and manufacturing of industrial parts.
Quantum transport on multilayer generalized scale-free networks

Authors:
raw_author_name: Mircea Galiceanu
raw_affiliation_string: Department of Physics, Federal University of Amazonas, 69077-000 Manaus, Brazil, Manaus, AM, 69077-000, BRAZIL
raw_author_name: Walter T Strunz
raw_affiliation_string: Physics, TU Dresden Faculty of Mathematics and Natural Sciences, 01062 Dresden, Germany, Dresden, 01062, GERMANY

Abstract: Abstract We study single-particle quantum transport on multilayer generalized scale-free networks using the continuous-time quantum walk model. Our focus is directed at the average return probability and its long-time average value as measures for the transport efficiency. In the continuous-time model these quantities are completely determined by all the eigenvalues and eigenvectors of the connectivity matrix. For all multilayer networks a nontrivial interplay between good spreading and localization effects is observed. The spreading is enhanced by increasing the number of layers L or the power-law exponent γ of the degree distribution. For our choice of the parameters, namely L (1 ≤ L ≤ 50) or γ (1 ≤ γ ≤ 4), the quantum efficiency is increased by at least one order of magnitude. The topological transition between networks without loops, which corresponds to a single scale-free network layer ( L = 1), and networks with loops ( L = 2) is the most impactful. Another important change occurs when L gets higher than the average diameter d of the layers, namely a new scaling behavior for random walks and lower fluctuations around the long-time average value for quantum walks. The quantum transport is more sensitive to changes of the minimum allowed degree, K min , than to the maximum allowed degree, K max . The same quantum efficiency is found by varying at least one of the parameters: L , γ , K min , or K max , although the network’s topology is different. The quantum efficiency of all multilayer scale-free networks shows a universal behavior for any size of the layers, more precise, is inversely proportional to the number of layers.
Uncertainty analysis and optimization design of long-distance laser triangulation displacement sensor applied to dynamic object

Authors:
raw_author_name: Zhuojiang Nan
raw_affiliation_string: Shanghai Jiao Tong University (China)
raw_author_name: Wei Tao
raw_affiliation_string: Shanghai Jiao Tong University (China)

Abstract: The principle of laser triangulation has been widely used in various high-precision measurement due to its advantages of high accuracy, fast response speed and simple structure. However, the laser triangulation method still has some limitations for the measurement of dynamic object, and the accuracy of laser location will be significantly reduced especially when the working distance becomes longer. We have carried out the development and performance optimization of a long-distance laser triangulation displacement sensor for the application of dynamic object. The measurement uncertainty is analyzed when the laser dithering, laser drifting and object motion are all taken into account. Then, reflectors are added to the optical path structure of traditional laser triangulation system with the aim to improving its sensitivity and expanding its working range. The experiment results show that the repeatability of our designed optical system is 2.9μm and the imaging uniformity is 86.23% when the working distance is of 1300mm.
Verification and identification of the speech signal using algebraic analysis and DWT

Authors:
raw_author_name: Saadi Slami

Abstract: We presented a flowchart, of a new approach that relies on the mathematical operations of matrices and to verify and identifies the speaker, this is a new method difficult to detect by attackers, and this is due to the difficulty to determine its base matrix used in the speaker verification and identification process, Although there are many artificial intelligence programs that can imitate voices and generating false conversations, which poses security and hacking problems.
Phase shift error estimation based on deep learning

Authors:
raw_author_name: Xinhao Huang
raw_affiliation_string: Changzhou University (China)
raw_author_name: Jiaxing Shen
raw_affiliation_string: Changzhou University (China)
raw_author_name: Yiqing Cao
raw_affiliation_string: Putian University (China)
raw_author_name: Ketao Yan
raw_affiliation_string: Changzhou University (China)

Abstract: Phase-shifting interferometry is a high-precision and commonly used phase retrieval method. In practical applications, phase shift errors are usually introduced due to factors such as environmental disturbances and phase shifter error. In this paper, we propose a deep learning method for estimating phase shift error from phase-shifting interferograms. This method manages to process the three interferograms with phase shift <i>&pi;</i>/2, and uses neural network to extract phase shift errors from three interferograms. The analysis shows that the proposed method can effectively estimate the phase shift error under the noisy interferograms. This method can be used to correct phase-shift errors for phase retrieval (e.g., the least squares phase retrieval method) and calibrate phase shifters.
Active thermal marker using thermal images of heated areas with visible semiconductor laser

Authors:
raw_author_name: Tomohiko Hayakawa
raw_affiliation_string: Tokyo University of Science (Japan); University of Tokyo (Japan)
raw_author_name: Yuka Hiruma
raw_affiliation_string: University of Tokyo (Japan)
raw_author_name: Masatoshi Ishikawa
raw_affiliation_string: Tokyo University of Science (Japan)

Abstract: In situations where the object being captured or the camera itself is in motion, object tracking using conventional attached markers or detailed textures is useful to keep capture the object with high accuracy; however, tracking accuracy has been degraded in situations where texture features are scarce or where it is difficult to attach markers. Therefore, in this study, the temperature rise corresponding to the laser irradiation time to the object is detected by a thermal camera, and the points where the temperature reaches or exceeds a threshold value after the laser irradiation ends are used as markers. After the temperature drops, laser irradiation is performed again to repeatedly generate markers. Furthermore, by controlling the irradiation point with a 2-axis galvanometer mirror, a marker that can code arbitrary shapes and information can be generated on a 2-dimensional plane. In our experiments, we irradiated a red semiconductor laser onto black paper, and found that an irradiation time of 10 ms was the most efficient in terms of heat dissipation time, enabling the simultaneous generation of up to 33 markers. As a result, it was found that it is possible to draw distinguishable characters and graphics such as lines and circles by continuous drawing. Future plans include application to non-paper based on additional physical property investigations and actual tracking applications.
Bridge deflection measurement by drone aerial photography using the sampling moiré method

Authors:
raw_author_name: Shien Ri
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (Japan)
raw_author_name: Jiaxing Ye
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (Japan)
raw_author_name: Peng Xia
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (Japan)
raw_author_name: Nobuyuki Toyama
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (Japan)
raw_author_name: Tomoki Shiotani
raw_affiliation_string: Kyoto University (Japan)

Abstract: Accurate deflection measurement is vital in evaluating the structural integrity of transportation infrastructures, with bridges being fascinating. In this study, we propose a novel image stabilization technique integrated into the sampling moiré method, which leads to a dependable approach for measuring bridge deflection through drone aerial photography. Our experimental verification entailed conducting drone tests on an actual bridge, utilizing a passing test vehicle, and the results showcased deflection measurements comparable to those obtained through conventional methods. This newly developed technology eliminates the need for ground-fixed cameras mounted on tripods, thus enabling precise deflection measurement at the millimeter level for bridges in challenging environments, including marine and mountainous areas.
Whisper-Steg: Unveil the Hidden

Authors:
raw_author_name: Prof. Laxman Pawar

Abstract: Whisper-Steg represents a ground- breaking project at the intersection of secure messaging and advanced information concealment techniques. This innovative chat application transcends traditional boundaries by seamlessly integrating the art of steganography, offering users a novel and secure platform for communication. The project not only facilitates text and image sharing but introduces an ingenious Image Steganography feature, allowing users to embed confidential information within images. Incorporating an acknowledgment mechanism akin to popular messaging applications like WhatsApp, Whisper- Steg prioritizes the reliability of communication. This feature ensures that users receive confirmation of message delivery, enhancing the overall user experience. The project dynamically generates cover images based on the size of hidden messages, employing AI algorithms to optimize concealment. This not only enhances the security of the stenographic process but also underscores the project's commitment to leveraging cutting-edge technologies for user benefit. In an era where privacy concerns are paramount, Whisper-Steg embraces steganography and AI-driven solutions and stands as a testament to technological innovation, reshaping the landscape of secure digital communication. Key Words: Whisper-Steg, Steganography, chat application
Phase retrieval by extracting Zernike coefficients from two random phase-shifting interferograms based on deep learning

Authors:
raw_author_name: Wenjun Yu
raw_affiliation_string: Shanghai University (China)
raw_author_name: Ketao Yan
raw_affiliation_string: Changzhou University (China)
raw_author_name: Xingyu Lin
raw_affiliation_string: Shanghai University (China)
raw_author_name: Yingjie Yu
raw_affiliation_string: Shanghai University (China)

Abstract: This paper presents a method of extracting Zernike coefficients and then retrieving the phase based on deep learning. In the normal interferometry, the phase extraction algorithm is utilized to calculate the wrapped phase according to the registering phase shifting interferograms, and then unwrap it to obtain the phase map. Further, Zernike polynomials are used to fit the phase. The method proposed in this paper uses neural networks to extract 35 terms of Zernike coefficients from two random phase-shifting interferograms, then retrieve the phase based on Zernike polynomials. The method only needs two random phase-shifting interferograms, does not require phase extraction and unwrapping processing and greatly simplifies the computation for phase reconstruction. The paper presents the training processing, and provides the experimental results. The results show that the proposed method can reach high precision and are more suitable for the quick testing for workshop environment.
Fast anti-turbidity underwater topography measurement based on rotating structured light

Authors:
raw_author_name: Yonghao Zhou
raw_affiliation_string: Shanghai University (China)
raw_author_name: Li Xu
raw_affiliation_string: North China University of Water Resources and Electric Power (China)
raw_author_name: Bin Shen
raw_affiliation_string: Shanghai University (China)
raw_author_name: Yingjie Yu
raw_affiliation_string: Shanghai University (China)
raw_author_name: Bo Jiao
raw_affiliation_string: North China University of Water Resources and Electric Power (China)

Abstract: The visual measurement techniques play an important role in underwater three-dimensional (3D) topography reconstruction. Line structured light measurement are widely used in visual 3D information acquisition. Aiming at the refractive nonlinear imaging effects and image degradation problems in underwater measurements, this paper proposes a physical enhancement-based underwater nonlinear refraction measurement model (peUNR). First, the underwater images are corrected by physical enhancement based on scattering imaging theory to improve the image recognition. Second, a refraction relationship for underwater visual measurements is developed by ray tracing. In this model, the light plane is unchanged before and after reaching the surface of the underwater target. As a result, the system parameters can be calibrated in air, allowing high-precision 3D reconstruction underwater without the need for underwater calibration. At last, in order to reduce the dependence of structured light stripe extraction on image quality, a multiline rotating structured light projection mode is designed by using binary coding, which fulfils the need for fast and portable measurements and improves the measurement efficiency and resolution simultaneously. The experimental results of underwater measurement indicate that the residual error of the fitted plane obtained by the peUNR model is 1.81 mm in turbid water with a sand content of 125 g/m<sup>3</sup>, which satisfy the requirements of underwater 3D high-precision measurement.
Blind multi-Poissonian image deconvolution with sparse log-step gradient prior

Authors:
raw_author_name: Dong Wende
raw_author_name: Shuyin Tao
raw_affiliation_string: Nanjing University of Science and Technology
raw_author_name: Chao Tian
raw_affiliation_string: Institute of Artificial Intelligence; University of Science and Technology of China

Abstract: Blind image deconvolution plays a very important role in the fields such as astronomical observation and fluorescence microscopy imaging, in which the noise follows Poisson distribution. However, due to the ill-posedness, it is a very challenging task to reach a satisfactory result from a single blurred image especially when the power of the Poisson noise is at a high level. Therefore, in this paper, we try to achieve high-quality restoration results with multi-blurred images which are contaminated by Poisson noise. Firstly, we design a novel sparse log-step gradient prior which adopts a mixture of logarithm and step functions to regularize the image gradients and combine it with the Poisson distribution to formulate the blind multi-image deconvolution problem. Secondly, we incorporate the methods of variable splitting and Lagrange multiplier to convert the original problem into sub-problems, then we alternately solve them to achieve the estimation of all the blur kernels of corresponding blurred images. Besides, we also design a non-blind multi-image deconvolution algorithm which is based on the log-step gradient prior to reach the final restored image. Experimental results on both synthetic and real-world blurred images show that the proposed prior is very capable of suppressing negative artifacts caused by ill-posedness. The algorithm can achieve restored image of very high quality which is competitive with some state-of-the-art methods.
Photonic time-delayed reservoir computing based on series-coupled microring resonators with high memory capacity

Authors:
raw_author_name: Hongliang Ren
raw_author_name: Yijia Li
raw_author_name: Ming Li
raw_author_name: Mingyi Gao
raw_author_name: Jin Lu
raw_author_name: Chang-Ling Zou
raw_author_name: Chunhua Dong
raw_author_name: Xiaoniu Yang
raw_author_name: Qi Xuan

Abstract: On-chip microring resonators (MRRs) have been proposed to construct time-delayed reservoir computing (RC) systems, which offer promising configurations available for computation with high scalability, high-density computing, and easy fabrication. A single MRR, however, is inadequate to provide enough memory for the computation task with diverse memory requirements. Large memory requirements are satisfied by the RC system based on the MRR with optical feedback, but at the expense of its ultralong feedback waveguide. In this paper, a time-delayed RC is proposed by utilizing a silicon-based nonlinear MRR in conjunction with an array of linear MRRs. These linear MRRs possess a high quality factor, providing enough memory capacity for the RC system. We quantitatively analyze and assess the proposed RC structure's performance on three classical tasks with diverse memory requirements, i.e., the Narma 10, Mackey-Glass, and Santa Fe chaotic timeseries prediction tasks. The proposed system exhibits comparable performance to the system based on the MRR with optical feedback, when it comes to handling the Narma 10 task, which requires a significant memory capacity. Nevertheless, the dimension of the former is at least 350 times smaller than the latter. The proposed system lays a good foundation for the scalability and seamless integration of photonic RC.
A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

Authors:
raw_author_name: Sundari . P
raw_affiliation_string: a:1:{s:5:"en_US"; s:30:"Govt. Arts College,Coimbatore "; }

Abstract: The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.
Applicability of education 4.0 in higher education: Engineering students’ survey

Authors:
raw_author_name: Andrea Tick

Abstract: The purpose of the study is to examine how Industry 4.0, and the digital environment, have together created a new situation for companies and universities in terms of soft skills for employability. While companies aim to introduce a new structure, universities try to align with these changes by developing new educational methods, curricula, and models. Many researches are focusing on - rightfully so - Industry 4.0 skills and competences to gain throughout higher education. However, we have shifted our research interest and asked students to what degree their university years added to their knowledge, self-management and skills. 147 engineering and technical manager students’ responses from two faculties at Óbuda University, Hungary were analyzed to see their evaluations of the role of the university in the framework of Education 4.0, and their chances in the job market. Quantitative and CHAID analyses were used. Students’ responses show significant differences between the two faculties regarding languages, teamwork, self-management and in the different order of the skills seen as most paramount to employability. Engineering students attach greater importance to field-relevant knowledge, while technical manager students find decision-making more useful. The research proved that Education 4.0 is here and Z generation studying at universities prefers problem-based learning, including creativity, analytical, and critical thinking while would like to have good communication skills. Universities need to adapt to these changes and integrate hard as well as soft skills development in tutoring. The importance of technical-IT knowledge is just as important as having interpersonal skills, excellence in problem-solving, so students need to be taught how to analyze problems, engage in scientific debate, or express themselves clearly as previous literature indicated.