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Electronic holography is an ideal 3-D display technique to ameliorate the vergence-accommodation conflict, which is a possible cause of visual fatigue and discomfort from viewing conventional stereoscopic 3-D displays. Previous studies have measured accommodative and vergence responses to holographic images and to real objects, and the results showed that these responses are in good agreement. To demonstrate the effectiveness of electronic holography as a possible solution to the vergence-accommodation conflict caused by viewing conventional stereoscopic 3-D displays, we measured both accommodative and vergence responses to reconstructed images of holograms and super multiview holographic stereograms. We also measured responses to real objects located at various distances and conventional two-view stereo images for comparison. The results indicate that the accommodative responses to the electro-holographic display and super multiview holographic stereogram change with the vergence response in a similar manner, as is the case with real objects as compared to conventional two-view stereo display. This suggests that the electronic holographic display and super multiview holographic stereograms are promising candidates for resolving the vergence-accommodation conflict. [ABSTRACT FROM AUTHOR]

Modeling 3D visual saliency has received great attention due to the development of emerging 3D display technologies. Traditional methods relying on low-level features may not be efficient in interpreting 3D visual content from high-level semantic perspective. Despite numerous efforts dedicated to this area, existing 3D visual saliency detection methods do not necessarily excel in exploring the stereoscopic image saliency driven by the intra-view and inter-view dependencies among left and right views. In this paper, we propose a visual saliency detection method for stereoscopic images grounded on adaptive viewpoint feature enhancement via binocular vision. More specifically, the correlation among left and right views is investigated through a delicately designed binocular stereoscopic saliency feature aggregation module, enabling the generation of more representative saliency features towards binocular vision. Subsequently, to further aggregate the saliency features in multiple scales, we design a progressive attention-based saliency feature pyramid extraction module to effectively integrate the features from top-level to down-level based on the network hierarchy mechanism. The saliency maps are ultimately produced for stereoscopic images by evaluating the obtained saliency features. In addition, we create a stereoscopic image saliency dataset (SIS-3D) that includes 1086 stereoscopic image pairs with various content and their corresponding human eye fixation annotations, aiming to further facilitate the research on visual saliency detection for stereoscopic images. Extensive experiments demonstrate that our proposed method improves CC by an average of 4.02% compared to representative counterparts on the newly built saliency dataset and another publicly available dataset. [ABSTRACT FROM AUTHOR]

Highly realistic 3-D displays that can reproduce object images to look like physical objects are utilized for natural and correct remote operation in industrial scenes. Therefore, we developed a visually equivalent light field 3-D (VELF3D) display that can produce highly realistic, accurate images with a high resolution and a smooth, accurate motion parallax. However, the observation distance is slightly long, and users cannot reach the displayed images. Therefore, we aim to develop a tablet-computer-type VELF3D display that enables users to touch the displayed objects. The display viewpoint density has been increased to achieve a shorter observation distance, while maintaining the display depth range. Because higher resolutions are required for a close observation distance and increased display viewpoints, we aimed to improve the effective resolution using almost the same pixel pitch display panel. Therefore, we built a prototype that combines a vertical red, green, blue stripe display panel and a parallax barrier with subpixel width slits. We confirmed effective resolution improvement by tiny subjective tests. This method also helps increase the depth range of the display when it is observed from a normal distance. [ABSTRACT FROM AUTHOR]

Conventional synthetic aperture radar (SAR) processing can be adapted to obtain 2-D high-resolution images of moving targets. The resulting images, however, suffer from layover effects, therefore making their interpretation more challenging. This problem can be dealt with by combining radar imaging with interferometry, which allows for 3-D target reconstruction. This article presents a 3-D imaging technique built upon the well-known global backprojection (GBP) algorithm, which has been adapted to focus moving targets rather than the stationary scene. The proposed algorithm is tested on real datasets acquired with a circular acquisition geometry. Using a nonlinear flight-path allows to monitor the target from multiple viewing angles, therefore overcoming the limitations that are common to stripmap SAR (e.g., shadowing effects, geometrically adverse cases, and so on). The obtained results, which were compared with independent ground measurements, demonstrate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Many stereoscopic image retargeting (SIR) methods have been developed for automatically and intelligently resizing stereoscopic images and we cannot always rely on time-consuming subjective user studies to validate the performance of different SIR methods. It is therefore required to design reliable objective metrics for SIR quality evaluation. This paper extends our previous 2D aspect ratio similarity (ARS) metric to a stereo 3D version termed as StereoARS where the key idea is to investigate into retargeting inconsistency between the original stereo correspondences. Our proposed StereoARS operates via two stages: monocular quality estimation and binocular inconsistency detection. In the first stage, monocular quality estimation is performed by applying a modified ARS measure on the left and right views separately to quantify the quality degradation within each monocular view. In the second stage, binocular inconsistency detection is performed in both pixel-level and grid-level to characterize the influence of binocular rivalry and stereo visual discomfort on SIR quality. In addition, we also measure to what extent the original pixel visibility relation is preserved after SIR as another binocular quality factor. Finally, these monocular and binocular quality estimates are fused to produce an overall SIR quality score. Extensive experiments have demonstrated that StereoARS achieves better alignment with human subjective ratings than the existing metrics by a large margin. [ABSTRACT FROM AUTHOR]

Device scaling of oxide-channel thin-film transistors (TFTs) toward sub-micrometer regime has been an urgent issue to realize higher-resolution displays and 3-D structured electronic devices. The channel-shortening effect is a serious problem in implementing the oxide TFTs with short channel lengths. In this work, the short-channel effects (SCEs) of the In-Ga-Zn-O (IGZO) TFTs were investigated with the variations in channel composition when the channel length was varied from $3~\mu \text{m}$ to 500 nm. Effective channel lengths were examined to decrease with increasing the In contents, in which the reduction of the effective channel length was estimated to be 240 and 720 nm when the In/Ga ratio increased 0.7 to 1.4. The drain bias-dependent roll-offs in turn-on voltages, which typically correspond to drain-induced barrier-lowering (DIBL), also showed significant channel composition dependence. When the In/Ga ratio within the channel increased from 0.2 to 1.4, the DIBL coefficients were estimated from 22 to 244 mV/V for the devices with a channel length of 1 $\mu \text{m}$. The channel composition-dependent variations in the effective channel length were suggested to result from the inter-diffusion and redox reactions at interfaces between the channel and electrode layers. Alternatively, the contact resistances were also examined to play major roles in determining the SCEs with scaling the devices, especially when the In contents decreased in the channel composition. Thus, properly controlling the channel composition can be concluded as such an important issue for compromising both technical strategies to reduce the channel resistance and to suppress the reduction of effective channel length. [ABSTRACT FROM AUTHOR]

As a typical cell structure, the cylindrical ferroelectric capacitor (CFE-cap) has been widely used in 3-D ferroelectric memories. Unlike planar ferroelectric capacitor, the CFE-cap has nonuniform electric field and polarization distribution. In this article, we investigate the impacts of the radius on the capacitance, field distribution, electrode charge densities of both the isotropic and anisotropic CFE-caps. As the inner radius increases, the CFE-cap behaves more like the planar capacitor with the same ferroelectric thickness. The results indicate that CFE-cap has an additional knob of inner radius Rin to tune its ferroelectric and electric characteristics. We have considered two kinds of CFE-caps, isotropic and anisotropic CFE-caps. The former is found to have stronger ferroelectric response than the latter, such as higher normalized capacitance and larger polarization. [ABSTRACT FROM AUTHOR]

We have proposed and developed an “edge-based depth-fused 3-D (DFD) display” in order to solve four problems in conventional DFD displays for many applications. 1) Obscure double imaging and an inappropriate luminance distribution may appear outside the overlap area due to adjusting the ratio of the luminances between the two overlapping images, which have the same shape, in order to control the perceived depth. 2) The viewing angle may be narrow. 3) The maximum perceived depth may be limited. 4) Moreover, the display system is rather thick. An “edge-based” DFD display, which projects a 2-D image of the desired 3-D image and a corresponding edge image with a gap, overcomes problem (1) by adjusting the perceived depth by changing the luminance of only the edge image so that the front or rear image is always a clear 2-D image. It also overcomes problems (2) and (3), respectively, by utilizing head movement and/or by blurring the edge image. The thickness problem (4) is overcome by fusing a DFD display and an arc 3-D display. [ABSTRACT FROM AUTHOR]

Objective: In minimally invasive surgery (MIS), in situ augmented reality (AR) navigation systems are usually implemented using a glasses-free 3D display to represent the preoperative tissue structure, and can provide intuitive see-through guidance information. However, due to changes in intraoperative tissue, the preoperative tissue structure is not able to exactly correspond to reality, which influences the precision of in situ AR navigation. To solve this problem, we propose a method to update the tissue structure for in situ AR navigation in such way to reflect changes in intraoperative tissue. Methods: The proposed method to update the tissue structure is based on the calibrated ultrasound and two-level surface warping technologies. Firstly, the particle filter-based calibration is implemented to perform ultrasound calibration and obtain intraoperative position of anatomical points. Secondly, intraoperative positions of anatomical points are inputted in the two-level surface warping method to update the preoperative tissue structure. Finally, the glasses-free real 3-D display of the updated tissue structure is finished, and is superimposed onto a patient by a translucent mirror for in situ AR navigation. Results: we validated the proposed method by simulating liver tissue intervention, and achieved the tissue updating accuracy of 92.86%. Furthermore, the targeting error of AR navigation based on the proposed method was also evaluated through minimally invasive liver surgery, and the acquired mean targeting error was 1.92 mm. Conclusion: The results demonstrate that the proposed AR navigation method is effective. Significance: The proposed method can facilitate MIS, as it provides accurate 3D navigation. [ABSTRACT FROM AUTHOR]

This article proposes a two-finger-based micrograsping system with high compliant borosilicate 3.3 glass probes and the corresponding sensing and control algorithms, which enables the orientation manipulation of microparts in three-dimensional (3-D) space. Compared with the existing research, the novelty of this article relies on three aspects: 1) the end-effector of the microgripper is designed to be with high compliance so that the squeeze force exerted on microparts can be more accurately regulated and the proposed microgripper is capable of manipulating fragile microparts; 2) the micrograsping system is endowed the capability to fully manipulate microparts’ orientation without recurring to auxiliary probes or rotary stages; and 3) the vibration characteristic of the grasping arm is investigated as cantilever beam for gasping stability analysis and squeeze force maintaining. In specific, taking spherical microparts with dimensions in the range from tens to hundreds of micrometers as target, the grasping system configuration, and the contact model between the probe and the microparts are first presented. Afterward, kinematics-based motion control strategy for position adjustment and orientation manipulation of microparts is clarified. Then, squeeze force regulation strategy is proposed, including adhesive force evaluation, vision-based squeeze force estimation, and the micropart releasing method. Finally, the vibration characteristic of the grasping arm is investigated as cantilever beam for gasping stability analysis and the squeeze force maintaining. The reliability and availability of the proposed micrograsping system is validated by well-designed experiments. Note to Practitioners—This article is motivated to develop a micrograsping system which enables dexterous rotating manipulation of microparts. The core of the proposed system is the utility of two fistulous tampered borosilicate 3.3 glass probes, which show high compliance in the microdomain. The manifested compliance enables the stably grasping of spherical objects during dexterous rotating manipulation and the vision-based contact force evaluation and regulation. The proposed system can rotate objects about two independent axes so that the orientation of a micropart can be adjusted to the desired one. This is critically important for many practical applications, such as the assembly of microballon and microneedle and the beamed cell penetration. [ABSTRACT FROM AUTHOR]

This communication presents a 3-D-printed wideband parallel-plate circularly polarized Luneburg lens (LL) antenna. The proposed structure of dielectric posts truncated by two metallic parallel plates serves not only as a LL, but also as a wideband linear-to-circular polarizer. The permittivity variation of the LL is realized by positioning dielectric posts with various radius (r) based on the optics theory and the effective medium theory (EMT). On the other hand, the desired circular polarization is realized by exciting two orthogonal propagating modes with different phase constants thanks to the integrated parallel plates. To verify our concept, a prototype was fabricated using 3-D-printing technology. Based on the measurement results, the proposed antenna achieves an overlapped bandwidth of 43% (21–32.5 GHz) in terms of the reflection coefficients lower than −10 dB and the axial ratios smaller than 3 dB. Good agreement between simulation and measurement is obtained. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of 3D motion recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. We propose a new 3D motion recovery method using spatio-temporal reconstruction, which uses joint low-rank and sparse priors to exploit temporal correlation and an isometric constraint for spatial correlation. The proposed model is formulated as a constrained optimization problem, which is efficiently solved by the augmented Lagrangian method with a Gauss–Newton solver for the subproblem of isometric optimization. The experimental results on the CMU motion capture dataset, Edinburgh dataset, and two Kinect datasets demonstrate that the proposed approach achieves better motion recovery than the state-of-the-art methods. The proposed method is applicable to Kinect-like skeleton tracking devices and pose estimation methods that cannot provide accurate estimation of complex motions, especially in the presence of occlusion. [ABSTRACT FROM AUTHOR]

Haptic shape displays provide compelling touch interactions by allowing users to freely explore a rendered surface. However, these displays are currently limited to 2.5-D surfaces due to the space requirements of their actuation. Building on previous work in haptic jamming, we developed a novel, soft 3-D shape display. A fully 3-D display that a user can grasp and hold allows for improved interactions for applications such as medical palpation training and virtual reality experiences. The shape display is implemented as an inflatable silicone membrane with embedded particle jamming cells that change stiffness and soft pneumatic actuators that control the distance between points on the surface. The device was modeled as a mass-spring system, and this model is used to develop a control sequence for a device to match a target shape. Due to constraints in actuation imposed by the 3-D geometry of the device, we developed an automatic design algorithm for the display, so that a display can be custom-designed to reach a set of target shapes using a relatively small number of actuators. [ABSTRACT FROM AUTHOR]

Owning the merits of flexible networking, rapid deployment, strong line-of-sight communications and so on, Unmanned Aerial Vehicle (UAV) has been regarded as a promising technology for the Fifth-Generation (5G) wireless network to realise its ambition of ubiquitous connectivity to support the various applications, e.g., broadcasting/multicasting services. However, the unique features of the UAV systems, e.g., highly agile mobility in the three-dimensional space, and the significant size, weight and power constraints, pose new challenges for 5G broadcasting communications. In order to fully harvest the benefits of UAV-aided 5G communications for broadcasting services, a new position based lightweight beamforming technology is proposed in this paper to enhance the capability of signal reception in 5G UAV broadcasting communications. Instead of relying on the channel estimation to form beams, which would cause significant broadband consumption, the position and mobility information of UAV system is exploited to predict UAV movement and form narrow beams to track UAV for signal enhancement and interference cancellation. To flight against the inherent position error, a new position correction method is designed to jointly utilise the DoA information of 5G system and the position information of the UAV system. Comprehensive simulation experiments are conducted based on 3GPP 5G specifications and the results show that the proposed algorithm outperforms the benchmark Zero-Forcing (ZF) without position prediction and position-based beamforming without position correction. [ABSTRACT FROM AUTHOR]

Passive operation and battery-charging of deep-body implants can be insured through wireless power transfer (WPT) technologies. However, the power transfer efficiency (PTE) is constrained by device miniaturization and implantation depth. This study proposes a complete WPT system consisting of a patterned WPT transmitter (Tx), an efficient rectifier, and an antenna integrated with the system. The WPT Tx had a size of 6 cm $\times$ 6 cm and was optimized to focus the power on the deep-tissue implants at 1470 MHz. The voltage doubler was optimized at 1470 MHz, had a small size of 5 mm $\times$ 10 mm, and exhibited a high radio frequency (RF)-to-direct current (dc) conversion efficiency of 90% at 2-dBm RF input power. Moreover, the implantable antenna occupies a small volume of 8.43 mm3 and supports quad-band operations: telemetry at 403 and 915 MHz, WPT at the midfield band of 1470 MHz, and control signaling at 2.4 GHz. First, the fabricated prototypes were measured individually in minced pork, in the American Society for Testing and Materials (ASTM) model, and in the saline-filled 3-D head phantom. While operating collectively as an integrated system, the PTE of the system was measured. Additionally, to enhance the PTE of the WPT system, a high-dielectric matching layer ($\varepsilon _{r} = 78$) was used between the WPT Tx and the phantom. Furthermore, to demonstrate the PTE of the WPT system, the voltage doubler was integrated with the implantable antenna, encapsulated in a 3-D-printed capsule endoscope, and its PTE was measured in a saline solution and minced pork. Finally, the compliance of the WPT system with the human safety standards was analyzed and found that the system solely satisfied the safety limits. It is evident from the experimental results that the system can transfer 6.7-mW power to millimeter-sized implants located 5-cm deep in tissues. [ABSTRACT FROM AUTHOR]

We propose a novel subsurface pipeline mapping and 3D reconstruction method by fusing ground-penetrating radar (GPR) scans and camera images. To facilitate the simultaneous detection of multiple pipelines, we model the GPR sensing process and prove hyperbola response for general scanning with nonperpendicular angles. Furthermore, we fuse visual simultaneous localization and mapping outputs, encoder readings with GPR scans to classify hyperbolas into different pipeline groups. We extensively apply the J-linkage method and maximum likelihood estimation with error analysis to improve algorithm robustness and accuracy. As a result, we optimally estimate the radii and locations of all pipelines. We have implemented our method and tested it in physical experiments with representative pipeline configurations. Two different kinds of 3-m-long pipes are used, with radii being 4.62 and 3.02 cm, respectively. The results show that our method successfully reconstructs all subsurface pipes. Moreover, the average estimation errors for two orientation angles of pipelines are 1.73° and 0.73°, respectively. The average localization error is 4.47 cm. Note to Practitioners—Automatic and accurate underground pipeline mapping technology is very important in civil construction projects. Lack of 3D utility pipeline maps may lead to accidental damage in civil construction and maintenance. Although ground-penetrating radar (GPR-based pipeline mapping methods have been studied for several years, these methods require the perpendicular scanning with respect to the pipe, which is impossible to guarantee in practice since the orientations of pipelines are unknown. Furthermore, these traditional methods can only estimate one pipeline at a time in a survey area and require prior knowledge of pipe diameter. We propose a robotic subsurface pipeline mapping method with a GPR and a camera to handle difficult factors such as multiple pipes, unknown pipeline orientation, and unknown pipeline diameters. Hence, we can perform GPR scanning along any generic linear trajectories. Our method has been tested in physical experiments with representative pipeline configurations. The results are sufficiently accurate, and it proves that our method can be an effective technology to reconstruct the underground pipelines. [ABSTRACT FROM AUTHOR]

Body composition can be assessed in many different ways. High-end medical equipment, such as Dual-energy X-ray Absorptiometry (DXA), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) offers high-fidelity pixel/voxel-level assessment, but is prohibitive in cost. In the case of DXA and CT, the approach exposes users to ionizing radiation. Whole-body air displacement plethysmography (BOD POD) can accurately estimate body density, but the assessment is limited to the whole-body fat percentage. Optical three-dimensional (3D) scan and reconstruction techniques, such as using depth cameras, have brought new opportunities for improving body composition assessment by intelligently analyzing body shape features. In this paper, we present a novel supervised inference model to predict pixel-level body composition and percentage of body fat using 3D geometry features and body density. First, we use body density to model a fat distribution base prediction. Then, we use a Bayesian network to infer the probability of the base prediction bias with 3D geometry features. Finally, we correct the bias using non-parametric regression. We use DXA assessment as the ground truth in model training and validation. We compare our method, in terms of pixel-level body composition assessment, with the current state-of-the-art prediction models. Our method outperforms those prediction models by 52.69% on average. We also compare our method, in terms of whole-body fat percentage assessment, with the medical-level equipment—BOD POD. Our method outperforms the BOD POD by 23.28%. [ABSTRACT FROM AUTHOR]

In this paper we propose an end-to-end swift 3D feature reductionist framework (3DFR) for scene independent change detection. The 3DFR framework consists of three feature streams: a swift 3D feature reductionist stream (AvFeat), a contemporary feature stream (ConFeat) and a temporal median feature map. These multilateral foreground/background features are further refined through an encoder-decoder network. As a result, the proposed framework not only detects temporal changes but also learns high-level appearance features. Thus, it incorporates the object semantics for effective change detection. Furthermore, the proposed framework is validated through a scene independent evaluation scheme in order to demonstrate the robustness and generalization capability of the network. The performance of the proposed method is evaluated on the benchmark CDnet 2014 dataset. The experimental results show that the proposed 3DFR network outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In recent years, the size of transistors has been scaled down to a few nanometers and further shrinking will eventually reach the atomic scale. Monolithic three-dimensional (M3D) ICs use the third dimension for placement and routing, which helps reduce footprint and improve power and performance of circuits without relying on technology shrinking. This article explores the benefits of M3D ICs using OpenPiton, a scalable open-source Reduced Instruction Set Computer (RISC)-V-based multicore SoC. With a logic-on-memory 3-D integration scheme, we analyze the power and performance benefits of two OpenPiton single-tile systems with smaller and larger memory architectures. The logic-on-memory M3D design shows 36.8% performance improvement compared to the corresponding tile design in 2-D. In addition, at isoperformance, M3D shows 13.5% total power saving. [ABSTRACT FROM AUTHOR]

Digital manufacturing technologies, especially three-dimensional (3-D) printing, are rapidly transforming the way we experience retail. Consumer products can now be deeply customized to an individual both in terms of fit, as well as style. However, consumers typically lack the design tools and expertise to express their design intent. In this article, we explore a human-centered approach for leveraging artificial intelligence (AI) to scaffold 3-D design tasks to make them accessible to more consumers. Our sunglasses kiosk prototype allows users to iteratively express their design intent at a high-level, using voice. The AI generates new 3-D designs of sunglasses that reflect the user's refined design intent. A user study examines the attitudes and behaviors toward this AI-powered kiosk approach. [ABSTRACT FROM AUTHOR]

Multiprocess Additive Manufacturing (AM) offers system designers new, exciting computational tools to rapidly realise smart wearable sensing devices in two-dimensional (2D) and 3D shapes. We guide readers through the novel development and fabrication process based on a digital co-design framework and highlight AM techniques, functional materials, and assembly procedures for designing wearables as flexible and stretchable on-skin patches, e-textiles, and smart accessories for everyday use. [ABSTRACT FROM AUTHOR]

Feature learning technologies using convolutional neural networks (CNNs) have shown superior performance over traditional hand-crafted feature extraction algorithms. However, a large number of labeled samples are generally required for CNN to learn effective features under classification task, which are hard to be obtained for hyperspectral remote sensing images. Therefore, in this paper, an unsupervised spatial–spectral feature learning strategy is proposed for hyperspectral images using 3-Dimensional (3D) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial–spectral structure information for feature extraction. A companion 3D convolutional decoder network is also designed to reconstruct the input patterns to the proposed 3D-CAE, by which all the parameters involved in the network can be trained without labeled training samples. As a result, effective features are learned in an unsupervised mode that label information of pixels is not required. Experimental results on several benchmark hyperspectral data sets have demonstrated that our proposed 3D-CAE is very effective in extracting spatial–spectral features and outperforms not only traditional unsupervised feature extraction algorithms but also many supervised feature extraction algorithms in classification application. [ABSTRACT FROM AUTHOR]

We demonstrate new fabrication technologies for 3D microelectrode platforms, fully realized for several 3D multi-functional biosystems. The microfabrication technology involves 3D metallized microtowers realized by 3D printing, metal evaporation, and coarse biocompatible lamination to insulate the traces. Electrospun 3D nanofiber scaffolds (NFSs) are coupled to the microelectrodes to provide additional functionality. The scaffolds were formed via electrospinning two types of nanofibers: ~200-500 nm PET, a hydrophobic polymer, and ~100 nm PVA/PAA, a hydrophilic co-polymer. PVA/PAA nanofibers had consistent diameters without beading and were used in subsequent experiments. Impedance measurements before, 651.3 $\text{k}\Omega $ , and after, 659.4 $\text{k}\Omega $ , deposition of PVA/PAA remains unchanged, indicating enhanced functionality without interfering with the electrical characteristics of the 3D MEAs. Silver nanoparticles (Ag NP) were embedded as model drug compounds in the PVA/PAA-NFS to demonstrate the potential of the 3D MEA as a biosensor and drug delivery system. TEM and antimicrobial studies demonstrated ~5-15 nm Ag NP within the PVA/PAA-NFS, which was potent to Acinetobacter baumannii and Escherichia coli. Fine 3D insulation atop the microtowers is achieved using a drop-casted/spin-coated 3D layer of Polystyrene (PS), which is laser micromachined to realize $50\times 50\,\,\mu \text{m}^{{2}}\,\,3\text{D}$ microelectrodes with impedance properties similar to other reported approaches. [2019-0043] [ABSTRACT FROM AUTHOR]

Image recapture is one of the simplest methods of content leakage, but is very difficult to address with the rapid development and dissemination of digital devices. This paper proposes novel secure display schemes to prevent image recapture on a polarized 3D system without additional hardware. Using commercial polarized 3D characteristics, the secret image to be protected from recapturing is encrypted on a pixel-by-pixel basis and decrypted with polarized glasses (PGs). Moreover, the proposed secure display schemes allow for the restricted viewing of secret images that only authorized people may see or the selective viewing of secret images and public images that anyone may see. Quantitative and qualitative analyses of the experimental results show that the proposed schemes can prevent content leakage from recaptured still or time axis images via a digital camera because the secret image is statistically encrypted, similar to random noise. As such, the restricted viewing or the selective viewing is possible depending on the presence of PGs. Therefore, the proposed schemes not only prevent image recapture using digital devices but also implement a secure display using PGs. [ABSTRACT FROM AUTHOR]

This paper deals with precise absolute geolocation of point targets by means of a pair of high-resolution synthetic aperture radar (SAR) acquisitions, acquired from a satellite. Even though a single SAR image is a 2-D projection of the backscatter, some 3-D information can be extracted from a defocussing analysis, depending on the resolution, thanks to orbital curvature. A second acquisition, observing the same scene under a different look angle, adds stereogrammetric capability and can achieve geolocation accuracy at decimeter level. However, for the stereogrammetric analysis to work, it is necessary to match targets correctly in the two images. This task is particularly difficult if it has to be automatic and targets are dense. Unfortunately, the defocussing-based geolocation is not sufficient for reliable target matching: the limiting factor is the unknown tropospheric delay that can cause geolocation errors of several meters in the elevation direction. However, observing that the tropospheric phase screen displays a low-pass character, this paper shows how to identify statistically the local atmospheric disturbances, therefore dramatically improving the score of successful matching. All steps involved exploit peculiar radar image characteristics and, thanks to this, avoid generic point cloud matching algorithms. The proposed algorithm is shown at work on a pair of TerraSAR-X staring spotlight images. [ABSTRACT FROM AUTHOR]

Viewing the communication system as three-dimensional (3-D) with various tiers cooperating among each other is a new trend to present 5G heterogeneous networks (HetNets). Base stations (BS) in each tier operate with different power levels, access methods, and unique topologies. A proper user (UE) association algorithm for HetNets is a great challenge. We develop a new real-time dynamic user (UE) association algorithm for multi-tier cooperating systems that considers users’ mobility and traffic dynamics considering both overall network load and received signal to interference and noise ratio (SINR). Despite that our proposed UE association algorithm does not depend on an interference mitigation algorithm to improve its performance, we develop a location-based interference mitigation algorithm to mitigate co-tier and cross-tier interferences in the worst case scenario of spectrum sharing among various tier BSs to overcome some of the drawbacks of spectrum partitioning algorithms. Our new algorithms are studied and analyzed through simulation and they are proved to provide the best performance compared to other algorithms. [ABSTRACT FROM AUTHOR]

Efficient algorithms for 3D character control in continuous control setting remain an open problem in spite of the remarkable recent advances in the field. We present a sampling-based model-predictive controller that comes in the form of a Monte Carlo tree search (MCTS). The tree search utilizes information from multiple sources including two machine learning models. This allows rapid development of complex skills such as 3D humanoid locomotion with less than a million simulation steps, in less than a minute of computing on a modest personal computer. We demonstrate locomotion of 3D characters with varying topologies under disturbances such as heavy projectile hits and abruptly changing target direction. In this paper we also present a new way to combine information from the various sources such that minimal amount of information is lost. We furthermore extend the neural network, involved in the algorithm, to represent stochastic policies. Our approach yields a robust control algorithm that is easy to use. While learning, the algorithm runs in near real-time, and after learning the sampling budget can be reduced for real-time operation. [ABSTRACT FROM AUTHOR]

Real-time rendering of large-scale Web3D scenes was difficult to implement in virtual reality systems and geographic information system (GIS) in the past because of the technical constraints in CPU, memory, and network bandwidth. In this paper, a model management strategy was proposed based on triangular meshes, in which neighborhood buildings are considered as nodes and connected. Each node in the mesh has a set of level of detail (LOD) models, including high, medium, and low precision models. Besides a model file, the high precision LOD of the node can be a subtriangular mesh as well. The three-dimensional (3-D) models in a complex scene can be flexibly managed with some nested triangular meshes. The memory management strategy and the display management strategy were discussed in this paper. According to the experimental results, the proposed method effectively achieves the progressive downloading, dynamic loading, and real-time display for a large-scale 3-D scene. Its performance is better than the traditional methods. [ABSTRACT FROM AUTHOR]

Provision of adequate task-oriented training can be difficult for stroke survivors with limited hand movement. The current passive devices are mainly intended for gross grasp and release training. Additional assistive devices are required to improve functional opposition. This paper investigated the functional recovery of chronic stroke patients after using a three-dimensional (3D) printed dynamic hand device (3D-DHD) as an adjunct to conducting a task-oriented approach (TOA). Ten participants were randomly assigned to either the 3D-DHD group (n = 5) or the control group (n = 5). The TOA was used for the 3D-DHD group by using the 3D-DHD twice a week for four weeks, followed by a two-week home program. Only the TOA was used for the control group. The outcome measures, including the box and blocks test (BBT) of manual dexterity and prehensile strength, were conducted at baseline and at follow-up at four and six weeks later. The 3D-DHD group exhibited significantly superior improvements to the control group in the BBT and the palmar pinch force test. Both the groups had significant within-group improvements in the BBT and in all strength measures compared with baseline measurements. The use of 3D-DHD could position stroke-affected hands in coordinated functional opposition and had the potential to facilitate manual dexterity and advanced prehensile movement. [ABSTRACT FROM AUTHOR]

We propose the first real-time system for the egocentric estimation of 3D human body pose in a wide range of unconstrained everyday activities. This setting has a unique set of challenges, such as mobility of the hardware setup, and robustness to long capture sessions with fast recovery from tracking failures. We tackle these challenges based on a novel lightweight setup that converts a standard baseball cap to a device for high-quality pose estimation based on a single cap-mounted fisheye camera. From the captured egocentric live stream, our CNN based 3D pose estimation approach runs at 60 Hz on a consumer-level GPU. In addition to the lightweight hardware setup, our other main contributions are: 1) a large ground truth training corpus of top-down fisheye images and 2) a disentangled 3D pose estimation approach that takes the unique properties of the egocentric viewpoint into account. As shown by our evaluation, we achieve lower 3D joint error as well as better 2D overlay than the existing baselines. [ABSTRACT FROM AUTHOR]

Fish Tank Virtual Reality (FTVR) displays create a compelling 3D spatial effect by rendering to the perspective of the viewer with head-tracking. Combining FTVR with a spherical display enhances the 3D experience with unique properties of the spherical screen such as the enclosing shape, consistent curved surface, and borderless views from all angles around the display. The ability to generate a strong 3D effect on a spherical display with head-tracked rendering is promising for increasing user's performance in 3D tasks. An unanswered question is whether these natural affordances of spherical FTVR displays can improve spatial perception in comparison to traditional flat FTVR displays. To investigate this question, we conducted an experiment to see whether users can perceive the depth and size of virtual objects better on a spherical FTVR display compared to a flat FTVR display on two tasks. Using the spherical display, we found significantly that users had 1cm depth accuracy compared to 6.5cm accuracy using the flat display on a depth-ranking task. Likewise, their performance on a size-matching task was also significantly better with the size error of 2.3mm on the spherical display compared to 3.1mm on the flat display. Furthermore, the perception of size-constancy is stronger on the spherical display than the flat display. This study indicates that the natural affordances provided by the spherical form factor improve depth and size perception in 3D compared to a flat display. We believe that spherical FTVR displays have potential as a 3D virtual environment to provide better task performance for various 3D applications such as 3D designs, scientific visualizations, and virtual surgery. [ABSTRACT FROM AUTHOR]

The radar echoes of space debris sometimes have drawbacks of inadequate pulse samples, self-occlusion, and data missing, while the state-of-art radar imaging algorithms become unproductive. To tackle this problem, this paper proposes a novel method for three-dimensional (3-D) imaging of space debris. This iterative algorithm terminates automatically and can recover the intensity of space debris radar cross section, making it practical in real-world applications. Unlike the 3-D imaging methods based on multistatic inverse synthetic aperture radar, the proposed method is applicable to monostatic ISAR. [ABSTRACT FROM AUTHOR]

Research works in novel viewpoint synthesis are based mainly on multiview input images. In this paper, we focus on a more challenging and ill-posed problem that is to synthesize surrounding novel viewpoints from a single image. To achieve this goal, we design a full resolution network to extract fine-scale image features, which contributes to prevent blurry artifacts. We also involve a pretrained relative depth estimation network, thus three-dimensional information is utilized to infer the flow field between the input and the target image. Since the depth network is trained by depth order between any pair of objects, large-scale image features are also involved in our system. Finally, a synthesis layer is used to not only warp the observed pixels to the desired positions but also hallucinate the missing pixels from other recorded pixels. Experiments show that our technique successfully synthesizes reasonable novel viewpoints surrounding the input, while other state-of-the-art techniques fail. [ABSTRACT FROM AUTHOR]

Compared to the conventional planar flash memory, advanced 3-D flash memory adopts charge-trap technology that can significantly enhance cell density and storage capacity. Despite these advantages, 3-D charge-trap flash memory brings several new challenges. First, charge-trap flash is sensitive to temperature. Recent studies demonstrate that, the high temperature will incur both charge loss and retention degradation. This issue does not happen in 2-D flash memory which adopts floating gate technology. Second, current 3-D charge-trap flash integrates the extra large capacity physical block, and each block contains over 1024 physical pages. The large-capacity block infrastructure will cause extra garbage collection overhead, which makes the thermal issue more complicated. This paper presents TempCure, a temperature-aware reliability enhancement strategy for 3-D charge-trap flash memory. TempCure is a novel hardware and file system interface that can transparently allocate physical space based on the temperature status. TempCure adopts two reliability enhancement strategies, temperature mining and block allotment, to prevent the generation of hotspots and enhance the data integrity of 3-D flash memory. We conduct a set of experiments using standard benchmarks. Experimental results show that TempCure can effectively reduce the peak temperature and block erase counts with negligible timing overhead in comparison with representative schemes. [ABSTRACT FROM AUTHOR]

We propose a method designed to push the frontiers of unconstrained face recognition in the wild with an emphasis on extreme out-of-plane pose variations. Existing methods either expect a single model to learn pose invariance by training on massive amounts of data or else normalize images by aligning faces to a single frontal pose. Contrary to these, our method is designed to explicitly tackle pose variations. Our proposed Pose-Aware Models (PAM) process a face image using several pose-specific, deep convolutional neural networks (CNN). 3D rendering is used to synthesize multiple face poses from input images to both train these models and to provide additional robustness to pose variations at test time. Our paper presents an extensive analysis of the IARPA Janus Benchmark A (IJB-A), evaluating the effects that landmark detection accuracy, CNN layer selection, and pose model selection all have on the performance of the recognition pipeline. It further provides comparative evaluations on IJB-A and the PIPA dataset. These tests show that our approach outperforms existing methods, even surprisingly matching the accuracy of methods that were specifically fine-tuned to the target dataset. Parts of this work previously appeared in  and . [ABSTRACT FROM AUTHOR]

Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder–decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel integrated vehicular system using collaborative unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for autonomous exploration, mapping, and navigation in GPS-denied 3-dimensional (3-D) unknown environments. The system implements a novel two-layered exploration strategy to decompose the perception task into a coarse exploration layer and a fine mapping layer. The coarse exploration makes use of a UGV to carry out fast autonomous exploration and active 2.5D simultaneous localization and mapping (SLAM) to generate a coarse environment model, which serves as a navigation reference for subsequent complementary 3-D fine mapping conducted by a UAV. The two layers share a novel optimized exploration path planning and navigation framework, which provides optimal exploration paths and integrates the collaborative exploration and mapping efforts through an OctoMap-based volumetric motion planning interface. The proposed system provides an efficient pipeline of fast environment perception taking advantages of the agility of the UAVs as well as the powerful computation resource aboard UGVs, also allowing assistive local perception with augmented object information when necessary. The effectiveness of our system is verified by both simulations and experiments, which demonstrate its capability of implementing heterogeneous UAV and UGV collaborative exploration and structural reconstruction of the environments through active SLAM, providing optimized perception for navigation tasks. [ABSTRACT FROM AUTHOR]

This paper describes a 3-D bifocal design technique for offset dual-reflectarray antennas (DRAAs) with application to multibeam satellite antennas in Ka-band. The bifocal technique has been used to obtain beams closer than those obtained with a single-focus antenna and, at the same time, to improve the performance of the extreme beams. The technique has been applied to design a multibeam DRAA in an offset compact range configuration to provide 1.12° separation between adjacent beams at 19.7 GHz. A small-scale bifocal DRAA demonstrator has been designed, fabricated, and tested. The demonstrator generates 10 beams alternating in dual-linear polarization when the antenna is illuminated by five adjacent feeds, but the technique can be extended to circular polarization. The results are in a good agreement with the design requirements in the frequency band from 19.2 to 20.2 GHz. [ABSTRACT FROM AUTHOR]

A highly efficient wireless power transfer (WPT) system is required in many applications to replace the conventional wired system. The high temperature superconducting (HTS) wires are examined in a WPT system to increase the power-transfer efficiency (PTE) as compared with the conventional copper/Litz conductor. The HTS conductors are naturally can produce higher amount of magnetic field with high induced voltage to the receiving coil. Moreover, the WPT systems are prone to misalignment, which can cause sudden variation in the induced voltage and lead to rapid damage of the resonant capacitors connected in the circuit. Hence, the protection or elimination of resonant capacitor is required to increase the longevity of WPT system, but both the adoptions will operate the system in nonresonance mode. The absence of resonance phenomena in the WPT system will drastically reduce the PTE and correspondingly the future commercialization. This paper proposes an open bifilar spiral coils based self-resonant WPT method without using resonant capacitors at both the sides. The mathematical modeling and circuit simulation of the proposed system is performed by designing the transmitter coil using HTS wire and the receiver with copper coil. The three-dimensional modeling and finite element simulation of the proposed system is performed to analyze the current density at different coupling distances between the coil. Furthermore, the experimental results show the PTE of 49.8% under critical coupling with the resonant frequency of 25 kHz. [ABSTRACT FROM AUTHOR]

This paper describes two important improvements to a 3-D shooting-and-bouncing-polygon (SBP) ray tracer for radio propagation modeling. It introduces a way to trace diffracted–reflected (D–R) rays in the SBP framework and improves SBP accuracy when the D–R rays play an important role. It also introduces a better space partitioning scheme for the use with SBP and improves the SBP’s computation efficiency by more than $10\times $ in some applications. The numerical results are compared against published measurements and 3-D full-wave solutions. They show that the improved SBP ray tracer is a good multipurpose 3-D ray tracer which can be used to simulate urban street canyon (without double over-rooftop diffraction), indoor, and tunnel propagation environments. [ABSTRACT FROM AUTHOR]

This paper presents the first demonstrator of a portable, multiview, high-resolution, 3-D, and real-time microwave imaging system. The system is based on a recently developed real-time 3-D microwave camera, which performs quasi-monostatic acquisitions, equipped with an optical depth camera, providing target surface profile information. In addition, the entire system can be arbitrarily moved along the target performing microwave and depth camera synchronized acquisitions from different views with a twofold purpose, namely: 1) enabling a coverage area much larger than that possible with a static imaging system and b) allowing for incorporation of several tilt angles (or views) to enhance capturing specular reflection imaging data to improve the overall image quality. At each scanning position, the imaging data from the microwave camera are processed to build a local 3-D microwave image. The information is then merged, using recently proposed techniques for multiview synthetic aperture imaging, to compose the global image. The synchronized optical camera depth acquisitions enable tracking the entire imager movements so that the position and attitude are known. Moreover, the data acquired by the depth camera are also use to build a complementary 3-D outer surface profile model of the target, producing a combined and realistic image of the internal and external geometries of the target. Finally, the performance of the combined system is evaluated using several examples related to hidden contraband covered by clothing (i.e., people screening). [ABSTRACT FROM AUTHOR]

Immersive navigation in virtual reality (VR) and augmented reality (AR) leverages physical locomotion through pose tracking of the head-mounted display. While this navigation modality is intuitive, regions of interest in the scene may suffer from occlusion and require significant viewpoint translation. Moreover, limited physical space and user mobility need to be taken into consideration. Some regions of interest may require viewpoints that are physically unreachable without less intuitive methods such as walking in-place or redirected walking. We propose a novel approach for increasing navigation efficiency in VR and AR using multiperspective visualization. Our approach samples occluded regions of interest from additional perspectives, which are integrated seamlessly into the user's perspective. This approach improves navigation efficiency by bringing simultaneously into view multiple regions of interest, allowing the user to explore more while moving less. We have conducted a user study that shows that our method brings significant performance improvement in VR and AR environments, on tasks that include tracking, matching, searching, and ambushing objects of interest. [ABSTRACT FROM AUTHOR]

Real-time indoor scene reconstruction aims to recover the 3D geometry of an indoor scene in real time with a sensor scanning the scene. Previous works of this topic consider pure static scenes, but in this paper, we focus on more challenging cases that the scene contains dynamic objects, for example, moving people and floating curtains, which are quite common in reality and thus are eagerly required to be handled. We develop an end-to-end system using a depth sensor to scan a scene on the fly. By proposing a Sigmoid-based Iterative Closest Point (S-ICP) method, we decouple the camera motion and the scene motion from the input sequence and segment the scene into static and dynamic parts accordingly. The static part is used to estimate the camera rigid motion, while for the dynamic part, graph node-based motion representation and model-to-depth fitting are applied to reconstruct the scene motions. With the camera and scene motions reconstructed, we further propose a novel mixed voxel allocation scheme to handle static and dynamic scene parts with different mechanisms, which helps to gradually fuse a large scene with both static and dynamic objects. Experiments show that our technique successfully fuses the geometry of both the static and dynamic objects in a scene in real time, which extends the usage of the current techniques for indoor scene reconstruction. [ABSTRACT FROM AUTHOR]

Rendering techniques that increase realism in volume visualization help enhance perception of the 3D features in the volume data. While techniques focusing on high-quality global illumination have been extensively studied, few works handle the interaction of light with materials in the volume. Existing techniques for light-material interaction are limited in their ability to handle high-frequency real-world material data, and the current treatment of volume data poorly supports the correct integration of surface materials. In this paper, we introduce an alternative definition for the transfer function which supports surface-like behavior at the boundaries between volume components and volume-like behavior within. We show that this definition enables multi-material rendering with high-quality, real-world material data. We also show that this approach offers an efficient alternative to pre-integrated rendering through isosurface techniques. We introduce arbitrary spatially-varying materials to achieve better multi-material support for scanned volume data. Finally, we show that it is possible to map an arbitrary set of parameters directly to a material representation for the more intuitive creation of novel materials. [ABSTRACT FROM AUTHOR]

Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g., clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enables the segmentation and annotation of massive 3D data. Our tool works by coupling 2D and 3D information via an interactive framework, through which users can provide high-level semantic annotation for objects. We have experimented our tool and found that a typical indoor scene could be well segmented and annotated in less than 30 minutes by using the tool, as opposed to a few hours if done manually. Along with the tool, we created a dataset of over a hundred 3D scenes associated with complete annotations using our tool. Both the tool and dataset are available at http://scenenn.net. [ABSTRACT FROM AUTHOR]

Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heterogeneous graph. We assign each object a set of attributes, based on which a semantic abstraction of the heterogeneous graph is generated. This semantic abstraction is SFG. We design a suite of operations to explore the underlying flow fields based on this graph representation and abstraction mechanism. Users can flexibly reconfigure SFG to examine the relationships among groups of objects at different abstraction levels. Three linked views are developed to display SFG, its node split criteria and history, and the objects in the spatial volume. For simplicity, we introduce SFG construction and exploration for steady flow fields with critical points being the only features. Then we demonstrate that SFG can be naturally extended to deal with unsteady flow fields and multiple types of features. We experiment with multiple data sets and conduct an expert evaluation to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Two approaches are proposed for cross-pose face recognition, one is built on the handcrafted features extracted from the 3D reconstruction of facial components and the other is built on the learned features from a deep convolutional neural network (CNN). As both approaches rely on facial landmarks for alignment across large poses, we propose the Fast Hierarchical Model (FHM) for locating cross-pose facial landmarks in real time. Unlike most 3D approaches that consider holistic faces, the first proposed approach considers 3D facial components. It segments each 2D face in the gallery into components, reconstructs the 3D surface for each component, and recognizes a query face by component features. The core part of the CNN-based approach is a modified VGG network. We study the performance with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. The two recognition approaches and the FHM are evaluated in extensive experiments and compared with state-of-the-art methods to demonstrate their efficacy. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of human action recognition, in which each action is captured by multiple sensors and represented by multisets. We propose two novel information fusion techniques for fusing the information from multisets. The first technique is biset globality locality preserving canonical correlation analysis (BGLPCCA), which aims to learn the common feature subspace between two sets. The second technique is multiset globality locality preserving canonical correlation analysis (MGLPCCA), which aims to deal with three or more sets. The proposed BGLPCCA and MGLPCCA are able to learn a low-dimensional common subspace that preserves the local and global structures of data samples. Moreover, two novel descriptors are presented for both depth and skeleton. We then propose a new human action recognition framework employing the proposed BGLPCCA or MGLPCCA to learn the shared subspace from multiple sets of features including skeleton, depth, and optical flow. Extensive experiments on five publicly available datasets (MSR Action3D, UTD multimodal human action dataset, multimodal action database, Kinect activity recognition dataset, and SBU Kinect interaction dataset) demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In this correspondence paper, we consider an unmanned aerial vehicle (UAV) equipped with an air-to-ground friendly jammer assisting secure communications between a legitimate transmitter–receiver pair for unknown eavesdropper location. To enhance the reliability and security of this system, we examine the impact of the UAV jamming power and its three-dimensional (3-D) spatial deployment on the outage probability (OP) of the legitimate receiver and the intercept probability (IP) of the eavesdropper. Based on the IP, we develop a new security measure, which we refer to as the intercept probability security region (IPSR). The IPSR defines the region within a target area where the IP is less than a given threshold. We design a low-complexity iterative algorithm to maximize the IPSR subject to an OP constraint by jointly optimizing the 3-D deployment and jamming power of the UAV jammer. Numerical results show that our proposed iterative algorithm performs close to an exhaustive search with significantly reduced complexity. [ABSTRACT FROM AUTHOR]

We introduce an optical design and a rendering pipeline for a full-color volumetric near-eye display which simultaneously presents imagery with near-accurate per-pixel focus across an extended volume ranging from 15cm (6.7 diopters) to 4M (0.25 diopters), allowing the viewer to accommodate freely across this entire depth range. This is achieved using a focus-tunable lens that continuously sweeps a sequence of 280 synchronized binary images from a high-speed, Digital Micromirror Device (DMD) projector and a high-speed, high dynamic range (HDR) light source that illuminates the DMD images with a distinct color and brightness at each binary frame. Our rendering pipeline converts 3-D scene information into a 2-D surface of color voxels, which are decomposed into 280 binary images in a voxel-oriented manner, such that 280 distinct depth positions for full-color voxels can be displayed. [ABSTRACT FROM AUTHOR]