Purpose: This study aims to investigate and compare three nonplanar (NP) slicing algorithms. The algorithms aim to control the layer thickness variation (LTV), which is a common issue in supportless fabrication of fre...
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Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of v...
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Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.
Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. Howeve...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.
The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals,i.e.,sharing disturbance mitigation among all controllable assets to even their ***,limited by neighboring communi...
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The droop-free control adopted in microgrids has been designed to cope with global power-sharing goals,i.e.,sharing disturbance mitigation among all controllable assets to even their ***,limited by neighboring communication,the time-consuming peer-to-peer coordination of the droopfree control slows down the nodal convergence to global consensus,reducing the power-sharing efficiency as the number of nodes *** this end,this paper first proposes a local power-sharing droop-free control scheme to contain disturbances within nearby nodes,in order to reduce the number of nodes involved in the coordination and accelerate the convergence speed.A hybrid local-global power-sharing scheme is then put forward to leverage the merits of both schemes,which also enables the autonomous switching between local and global power-sharing modes according to the system *** guidance for key control parameter designs is derived via the optimal control methods,by optimizing the power-sharing distributions at the steady-state consensus as well as along the dynamic trajectory to *** system stability of the hybrid scheme is proved by the eigenvalue analysis and Lyapunov direct ***,simulation results validate that the proposed hybrid local-global power-sharing scheme performs stably against disturbances and achieves the expected control performance in local and global power-sharing modes as well as mode ***,compared with the classical global power-sharing scheme,the proposed scheme presents promising benefits in convergence speed and scalability.
We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special...
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We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.
E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in ***,alternative approaches to conventional spin coating and developmen...
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E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in ***,alternative approaches to conventional spin coating and development processes are required to optimize the lithography procedure on irregular *** this review,we summarize the state of the art in nanofabrication on irregular substrates using e-beam *** overcome these challenges,unconventional methods have been *** instance,polymeric and nonpolymeric materials can be sprayed or evaporated to form uniform layers of electron-sensitive materials on irregular ***,chemical bonds can be applied to help form polymer brushes or self-assembled monolayers on these *** addition,thermal oxides can serve as resists,as the etching rate in solution changes after e-beam ***,e-beam lithography tools can be combined with cryostages,evaporation systems,and metal deposition chambers for sample development and lift-off while maintaining low *** nanopyramids can be fabricated on an AFM tip by utilizing ice as a positive ***,Ti/Au caps can be patterned around a carbon ***,3D nanostructures can be formed on irregular surfaces by exposing layers of anisole on organic ice surfaces with a focused *** advances in e-beam lithography on irregular substrates,including uniform film coating,instrumentation improvement,and new pattern transferring method development,substantially extend its capabilities in the fabrication and application of nanoscale structures.
Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by sol...
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Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by solving the inverse problem on the measured displacement field from the ultrasound images. The limitations of traditional inverse problem techniques in US elastography are either slow and computationally intensive (iterative techniques) or sensitive to measurement noise and dependent on full displacement field data (direct techniques). Thus, we develop and validate a deep learning approach for solving the inverse problem in US elastography. This involves recovering the spatial modulus distribution of the elastic modulus from one component of the US-measured displacement field. Approach: We present a U-Net-based deep learning neural network to address the inverse problem in ultrasound elastography. This approach diverges from traditional methods by focusing on a data-driven model. The neural network is trained using data generated from a forward finite element model. This simulation incorporates variations in the displacement fields that correspond to the elastic modulus distribution, allowing the network to learn without the need for extensive real-world measurement data. The inverse problem of predicting the modulus spatial distribution from ultrasound-measured displacement fields is addressed using a trained neural network. The neural network is evaluated with mean squared error (MSE) and mean absolute percentage error (MAPE) metrics. To extend our model to practical purposes, we conduct phantom experiments and also apply our model to clinical data. Results: Our simulated results indicate that our deep learning (DL) model effectively reconstructs modulus distributions, as evidenced by low MSE and MAPE evaluation metrics. We obtain a mean MAPE of 0.32% for a hard inclusion and 0.39% for a soft inclusion. Similarly, in our phantom studies, the predicted mo
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