Literature paper about volume holographic optical elements?5 answersVolume holographic optical elements (HOEs) have diverse practical applications, such as in holographic devices, optical beam shaping, diffractive optical elements, and compact optical systems. These elements offer advantages like high energy contrast, precise energy delivery, high angular selectivity, broadband operation, and compact size. The use of volume holograms allows for multiple optical functions in a common area, making them suitable for high-definition imaging and resolving two-point sources with short shift selectivity. Additionally, advancements in finite element methods enable accurate modeling of diffraction from holographic gratings with non-trivial profiles, leading to improved design of volume HOEs for better performance. Overall, volume holographic optical elements present a promising avenue for innovative optical and spectral device solutions.
How to calculate the distortion of point cloud map?5 answersDistortion of a point cloud map can be calculated by considering the position information and weight configuration of the points in the point cloud. Different weights can be assigned to points with different position information, allowing for a more accurate representation of the actual situation of the point cloud. One method for distortion quantification is the multiscale potential energy discrepancy (MPED), which measures both geometry and color differences in a point cloud by evaluating at various neighborhood sizes. Another approach is the use of lidar and IMU fusion to remove distortion caused by rapid motion, where the pose of the lidar is estimated and used to remove the distortion of the point cloud. Additionally, in video-based point cloud compression, considering only the rate instead of the rate distortion cost for unoccupied pixels can lead to bitrate savings without sacrificing the quality of the reconstructed point cloud.
What are the different types of fingerprint distortions?5 answersFingerprint distortions can be categorized into two types: linear and non-linear distortions. Linear distortions refer to the changes in the ridge and valley structure of fingerprints caused by non-uniform pressure and the elasticity of friction ridge skin. These distortions can be quantified and visualized using various metrics and software tools to assist examiners in fingerprint comparisons. Non-linear distortions, on the other hand, are induced by factors such as multipath fading channel, carrier frequency offset (CFO), and phase offset in radio frequency fingerprint identification (RFFI). These distortions can be mitigated through techniques like spectral quotient (SQ) representation and spectral circular shift division (SCSD). Additionally, distortion detection and rectification can be achieved through classification and regression algorithms, respectively, using features like ridge orientation maps and period maps.
What are the research challenges in holographic MIMO?4 answersHolographic MIMO (hMIMO) systems with a massive number of individually controlled antennas face several research challenges. One challenge is the computational complexity of minimum mean square error (MMSE) channel estimation, which scales as N^3. Antonio A. D'Amico et al. propose a low-complexity method based on the discrete Fourier transform (DFT) approximation to address this challenge, achieving the same performance as optimal MMSE with lower computational load that scales as N log N. Another challenge is the antenna efficiency, which affects the capacity improvement of holographic MIMO systems. Tengjiao Wang et al. propose an extended EM-compliant channel model that considers non-isotropic characteristics of the propagation environment, antenna pattern distortion, antenna efficiency, and polarization characteristics. Jiancheng An et al. discuss the open challenges and opportunities in holographic MIMO systems, particularly as transceiver array apertures become denser and electromagnetically larger.
How to cope with distortions when building a construction?3 answersTo cope with distortions when building a construction, it is important to consider the effects of various processes on the magnitude and distribution of stresses and distortions. Welding, grit blasting, and transporting can all cause changes in stress and distortion, but the distribution within a plate panel remains consistent. It is also crucial to accurately estimate the stresses and distortions during fabrication, as they can be larger than initially estimated. However, it is possible to keep distortions within tolerances by following established guidelines such as the Merrison Rules. By monitoring and predicting distortions, it is possible to make adjustments during construction to minimize their impact. Additionally, using multivariate discrete probability models and unbiased evaluations can help in understanding and managing the distribution of process distortions.
How can machine learning be used to improve holographic imaging?5 answersMachine learning can be used to improve holographic imaging by applying deep learning techniques to solve the phase retrieval problem for computer-generated holography (CGH). Capsule-based deep learning networks have been introduced to overcome information loss in convolutional neural networks (CNNs) and have shown promising results in holographic reconstruction. Deep learning algorithms have also been applied to various digital holography (DH) applications, improving performance and enabling new functionalities. Additionally, machine learning approaches have been used to improve the performance of holographic imaging tools, such as classification of biological samples and solving inverse problems in holographic microscopy. Deep neural networks have the potential to revolutionize holographic microscopy and sensing systems, as well as their biomedical applications.