Micro Fourier Transform Profilometry (μ\muFTP): 3D shape measurement at 10,000 frames per second

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro Fourier Transform Profilometry ($\mu$FTP), which can capture 3D surfaces of transient events at up to 10,000 fps based on our newly developed high-speed fringe projection system. Compared with existing techniques, $\mu$FTP has the prominent advantage of recovering an accurate, unambiguous, and dense 3D point cloud with only two projected patterns. Furthermore, the phase information is encoded within a single high-frequency fringe image, thereby allowing motion-artifact-free reconstruction of transient events with temporal resolution of 50 microseconds. To show $\mu$FTP's broad utility, we use it to reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a flying dart, which were previously difficult or even unable to be captured with conventional approaches.Comment: This manuscript was originally submitted on 30th January 1

Temporal phase unwrapping using deep learning

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even in the presence of different types of error sources, e.g., intensity noise, low fringe modulation, and projector nonlinearity. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU

Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions

Domain invariant learning aims to learn models that extract invariant features over various training domains, resulting in better generalization to unseen target domains. Recently, Bayesian Neural Networks have achieved promising results in domain invariant learning, but most works concentrate on aligning features distributions rather than parameter distributions. Inspired by the principle of Bayesian Neural Network, we attempt to directly learn the domain invariant posterior distribution of network parameters. We first propose a theorem to show that the invariant posterior of parameters can be implicitly inferred by aggregating posteriors on different training domains. Our assumption is more relaxed and allows us to extract more domain invariant information. We also propose a simple yet effective method, named PosTerior Generalization (PTG), that can be used to estimate the invariant parameter distribution. PTG fully exploits variational inference to approximate parameter distributions, including the invariant posterior and the posteriors on training domains. Furthermore, we develop a lite version of PTG for widespread applications. PTG shows competitive performance on various domain generalization benchmarks on DomainBed. Additionally, PTG can use any existing domain generalization methods as its prior, and combined with previous state-of-the-art method the performance can be further improved. Code will be made public

Catch Me If You Can: A New Low-Rate DDoS Attack Strategy Disguised by Feint

While collaborative systems provide convenience to our lives, they also face many security threats. One of them is the Low-rate Distributed Denial-of-Service (LDDoS) attack, which is a worthy concern. Unlike volumetric DDoS attacks that continuously send large volumes of traffic, LDDoS attacks are more stealthy and difficult to be detected owing to their low-volume feature. Due to its stealthiness and harmfulness, LDDoS has become one of the most destructive attacks in cloud computing. Although a few LDDoS attack detection and defense methods have been proposed, we observe that sophisticated LDDoS attacks (being more stealthy) can bypass some of the existing LDDoS defense methods. To verify our security observation, we proposed a new Feint-based LDDoS (F-LDDoS) attack strategy. In this strategy, we divide a Pulse Interval into a Feinting Interval and an Attack Interval. Unlike the previous LDDoS attacks, the bots also send traffic randomly in the Feinting Interval, thus disguise themselves as benign users during the F-LDDoS attack. In this way, although the victim detects that it is under an LDDoS attack, it is difficult to locate the attack sources and apply mitigation solutions. Experimental results show that F-LDDoS attack can degrade TCP bandwidth 6.7%-14% more than the baseline LDDoS attack. Besides, F-LDDoS also reduces the similarities between bot traffic and aggregated attack traffic, and increases the uncertainty of packet arrival. These results mean that the proposed F-LDDoS is more effective and more stealthy than normal LDDoS attacks. Finally, we discuss the countermeasures of F-LDDoS to draw the attention of defenders and improve the defense methods

Adaptive CO2 emissions mitigation strategies of global oil refineries in all age groups

Continuous expansion of fossil fuel-based energy infrastructure can be one of the key obstacles in delivering the Paris Agreement goals. The oil refinery is the world's third-largest stationary emitter of greenhouse gases (GHGs), but the historical mapping of the regional-specific refining industry, their CO2 emission patterns, and mitigation potentials remain understudied. This study develops a plant-level, technical-specific, and time-series global refinery CO2 emission inventory, covering 1,056 refineries from 2000 to 2018. The CO2 emissions of the refinery industry were about 1.3 gigatonnes (Gt) in 2018, representing 4% of the total. If current technical specifications continue, the global refineries will cumulatively emit 16.5 Gt of CO2 during 2020–2030. The refineries vary in operation age, refining configuration structure, and geographical location, leading to the demand for specific mitigation strategies, such as improving refinery efficiency and upgrading heavy oil processing technologies, which could potentially reduce global cumulative emissions by 10% during 2020–2030

Discrete Dimers of Redox-Active and Fluorescent Perylene Diimide-Based Rigid Isosceles Triangles in the Solid State

The development of rigid covalent chiroptical organic materials, with multiple, readily available redox states, which exhibit high photoluminescence, is of particular importance in relation to both organic electronics and photonics. The chemically stable, thermally robust, and redox-active perylene diimide (PDI) fluorophores have received ever-increasing attention owing to their excellent fluorescence quantum yields in solution. Planar PDI derivatives, however, generally suffer from aggregation-caused emission quenching in the solid state. Herein, we report on the design and synthesis of two chiral isosceles triangles, wherein one PDI fluorophore and two pyromellitic diimide (PMDI) or naphthalene diimide (NDI) units are arranged in a rigid cyclic triangular geometry. The optical, electronic, and magnetic properties of the rigid isosceles triangles are fully characterized by a combination of optical spectroscopies, X-ray diffraction (XRD), cyclic voltammetry, and computational modeling techniques. Single-crystal XRD analysis shows that both isosceles triangles form discrete, nearly cofacial PDI–PDI π-dimers in the solid state. While the triangles exhibit fluorescence quantum yields of almost unity in solution, the dimers in the solid state exhibit very weak—yet at least an order of magnitude higher—excimer fluorescence yield in comparison with the almost completely quenched fluorescence of a reference PDI. The triangle containing both NDI and PDI subunits shows superior intramolecular energy transfer from the lowest excited singlet state of the NDI to that of the PDI subunit. Cyclic voltammetry suggests that both isosceles triangles exhibit multiple, easily accessible, and reversible redox states. Applications beckon in arenas related to molecular optoelectronic devices