Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.
The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.
Light field imaging is an emerging technology in computational photography areas. Based on innovative designs of the imaging model and the optical path, light field cameras not only record the spatial intensity of threedimensional (3D) objects, but also capture the angular information of the physical world, which provides new ways to address various problems in computer vision, such as 3D reconstruction, saliency detection, and object recognition. In this paper, three key aspects of light field cameras, i.e., model, calibration, and reconstruction, are reviewed extensively. Furthermore, light field based applications on informatics, physics, medicine, and biology are exhibited. Finally, open issues in light field imaging and long-term application prospects in other natural sciences are discussed.
Compared with conventional cameras, spectral imagers provide many more features in the spectral domain. They have been used in various fields such as material identification, remote sensing, precision agriculture, and surveillance. Traditional imaging spectrometers use generally scanning systems. They cannot meet the demands of dynamic scenarios. This limits the practical applications for spectral imaging. Recently, with the rapid development in computational photography theory and semiconductor techniques, spectral video acquisition has become feasible. This paper aims to offer a review of the state-of-the-art spectral imaging technologies, especially those capable of capturing spectral videos. Finally, we evaluate the performances of the existing spectral acquisition systems and discuss the trends for future work.
Single-pixel imaging (SPI) technology has garnered great interest within the last decade because of its ability to record high-resolution images using a single-pixel detector. It has been applied to diverse fields, such as magnetic resonance imaging (MRI), aerospace remote sensing, terahertz photography, and hyperspectral imaging. Compared with conventional silicon-based cameras, single-pixel cameras (SPCs) can achieve image compression and operate over a much broader spectral range. However, the imaging speed of SPCs is governed by the response time of digital micromirror devices (DMDs) and the amount of compression of acquired images, leading to low (ms-level) temporal resolution. Consequently, it is particularly challenging for SPCs to investigate fast dynamic phenomena, which is required commonly in microscopy. Recently, a unique approach based on photonic time stretch (PTS) to achieve high-speed SPI has been reported. It achieves a frame rate far beyond that can be reached with conventional SPCs. In this paper, we first introduce the principles and applications of the PTS technique. Then the basic architecture of the high-speed SPI system is presented, and an imaging flow cytometer with high speed and high throughput is demonstrated experimentally. Finally, the limitations and potential applications of high-speed SPI are discussed.
Transient imaging is a technique in photography that records the process of light propagation before it reaches a stationary state such that events at the light speed level can be observed. In this review we introduce three main models for transient imaging with a time-of-flight (ToF) camera: correlation model, frequency-domain model, and compressive sensing model. Transient imaging applications usually involve resolving the problem of light transport and separating the light rays arriving along different paths. We discuss two of the applications: imaging objects inside scattering media and recovering both the shape and texture of an object around a corner.
Because the phase contains more information about the field compared to the amplitude, measurement of the phase is encountered in many branches of modern science and engineering. Direct measurement of the phase is difficult in the visible regime of the electromagnetic wave. One must employ computational techniques to calculate the phase from the captured intensity. In this paper, we provide a review of our recent work on iterative phase retrieval techniques and their applications in optical imaging.
We propose and fabricate a monolithic optical interconnect on a GaN-on-silicon platform using a wafer-level technique. Because the InGaN/GaN multiple-quantum-well diodes (MQWDs) can achieve light emission and detection simultaneously, the emitter and collector sharing identical MQW structure are produced using the same process. Suspended waveguides interconnect the emitter with the collector to form in-plane light coupling. Monolithic optical interconnect chip integrates the emitter, waveguide, base, and collector into a multi-component system with a common base. Output states superposition and 1×2 in-plane light communication are experimentally demonstrated. The proposed monolithic optical interconnect opens a promising way toward the diverse applications from in-plane visible light communication to light-induced artificial synaptic devices, intelligent display, on-chip imaging, and optical sensing.
We propose a method to establish a dynamic model for a wave glider, a wave-propelled sea surface vehicle that can make use of wave energy to obtain thrust. The vehicle, composed of a surface float and a submerged glider in sea water, is regarded as a two-particle system. Kane’s equations are used to establish the dynamic model. To verify the model, the design of a testing protot ype is proposed and pool trials are conducted. The speeds of the vehicle under different sea conditions can be computed using the model, which is verified by pool trials. The optimal structure parameters useful for vehicle designs can also be obtained from the model. We illustrate how to build an analytical dynamics model for the wave glider, which is a crucial basis for the vehicle’s motion control. The dynamics model also provides foundations for an off-line simulation of vehicle performance and the optimization of its mechanical designs.
We introduce a novel strategy of designing a chaotic coverage path planner for the mobile robot based on the Chebyshev map for achieving special missions. The designed chaotic path planner consists of a two-dimensional Chebyshev map which is constructed by two one-dimensional Chebyshev maps. The performance of the time sequences which are generated by the planner is improved by arcsine transformation to enhance the chaotic characteristics and uniform distribution. Then the coverage rate and randomness for achieving the special missions of the robot are enhanced. The chaotic Chebyshev system is mapped into the feasible region of the robot workplace by affine transformation. Then a universal algorithm of coverage path planning is designed for environments with obstacles. Simulation results show that the constructed chaotic path planner can avoid detection of the obstacles and the workplace boundaries, and runs safely in the feasible areas. The designed strategy is able to satisfy the requirements of randomness, coverage, and high efficiency for special missions.
The component-based business architecture integration of military information systems is a popular research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identification have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework (DoDAF) models. To quantify the indices of business components, we formulate a create, read, update, and delete (CRUD) matrix and use six metrics as criteria. We then formulate business component identification as a multi-objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ‘simulated annealing hybrid genetic algorithm (SHGA)’. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.
The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.
Because of the concise functionality of oblivious transfer (OT) protocols, they have been widely used as building blocks in secure multiparty computation and high-level protocols. The security of OT protocols built upon classical number theoretic problems, such as the discrete logarithm and factoring, however, is threatened as a result of the huge progress in quantum computing. Therefore, post-quantum cryptography is needed for protocols based on classical problems, and several proposals for post-quantum OT protocols exist. However, most post-quantum cryptosystems present their security proof only in the context of classical adversaries, not in the quantum setting. In this paper, we close this gap and prove the security of the lattice-based OT protocol proposed by Peikert et al. (CRYPTO, 2008), which is universally composably secure under the assumption of learning with errors hardness, in the quantum setting. We apply three general quantum security analysis frameworks. First, we apply the quantum lifting theorem proposed by Unruh (EUROCRYPT, 2010) to prove that the security of the lattice-based OT protocol can be lifted into the quantum world. Then, we apply two more security analysis frameworks specified for post-quantum cryptographic primitives, i.e., simple hybrid arguments (CRYPTO, 2011) and game-preserving reduction (PQCrypto, 2014).
In modern energy-saving replication storage systems, a primary group of disks is always powered up to serve incoming requests while other disks are often spun down to save energy during slack periods. However, since new writes cannot be immediately synchronized into all disks, system reliability is degraded. In this paper, we develop a high-reliability and energy-efficient replication storage system, named RERAID, based on RAID10. RERAID employs part of the free space in the primary disk group and uses erasure coding to construct a code cache at the front end to absorb new writes. Since code cache supports failure recovery of two or more disks by using erasure coding, RERAID guarantees a reliability comparable with that of the RAID10 storage system. In addition, we develop an algorithm, called erasure coding write (ECW), to buffer many small random writes into a few large writes, which are then written to the code cache in a parallel fashion sequentially to improve the write performance. Experimental results show that RERAID significantly improves write performance and saves more energy than existing solutions.
The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.
Complementing our previous publications, this paper presents the information schema constructs (ISCs) that underpin the programming of specific system manifestation feature (SMF) orientated information management and composing system models. First, we briefly present (1) the general process of pre-embodiment design with SMFs, (2) the procedures of creating genotypes and phenotypes of SMFs, (3) the specific procedure of instantiation of phenotypes of SMFs, and (4) the procedure of system model management and processing. Then, the chunks of information needed for instantiation of phenotypes of SMFs are discussed, and the ISCs designed for instantiation presented. Afterwards, the information management aspects of system modeling are addressed. Methodologically, system modeling involves (1) placement of phenotypes of SMF in the modeling space, (2) combining them towards the desired architecture and operation, (3) assigning values to the parameters and checking the satisfaction of constraints, and (4) storing the system model in the SMFs-based warehouse database. The final objective of the reported research is to develop an SMFs-based toolbox to support modeling of cyber-physical systems (CPSs).
In the field of nanotechnology, quantum dot-cellular automata (QCA) is the promising archetype that can provide an alternative solution to conventional complementary metal oxide semiconductor (CMOS) circuit. QCA has high device density, high operating speed, and extremely low power consumption. Reversible logic has widespread applications in QCA. Researchers have explored several designs of QCA-based reversible logic circuits, but still not much work has been reported on QCA-based reversible binary subtractors. The low power dissipation and high circuit density of QCA pledge the energy-efficient design of logic circuit at a nano-scale level. However, the necessity of too many logic gates and detrimental garbage outputs may limit the functionality of a QCA-based logic circuit. In this paper we describe the design and implementation of a DG gate in QCA. The universal nature of the DG gate has been established. The QCA building block of the DG gate is used to achieve new reversible binary subtractors. The proposed reversible subtractors have low quantum cost and garbage outputs compared to the existing reversible subtractors. The proposed circuits are designed and simulated using QCA Designer-2.0.3.
Rotating speed is a critical parameter affecting the performance of rotor gyroscopes. Rotor gyroscopes must operate at the rated rotating speed. To shorten the start time of the ball-disk rotor gyroscope, this paper presents a new design of the drive system for a ball-disk rotor gyroscope. The drive system is monitored by a microcontroller. First, the microcontroller generates a sine pulse width modulation signal to drive the permanent magnet rotor. Second, the position of the rotor is detected according to the back electromotive force in the non-energized coil. Third, a piecewise closed-loop control algorithm is implemented to keep the angular acceleration of the rotor within the safe range automatically during the acceleration process and when running at a constant speed. This control algorithm can avoid rotor stalling due to loss of steps. Experimental result shows that with the help of adaptive quick-start technique, the start time of the device can be shortened by up to 36.6%.
We propose a method to suppress deceptive jamming by frequency diverse array (FDA) in radar electronic countermeasure environments. FDA offers a new range-angle-dependent beam pattern through a small frequency increment across elements. Due to the coupling between the angle and range, a mismatch between the test angle and physical angle occurs when the slant range on which the beam focuses is not equal to the slant range of the real target. In addition, the range of the target can be extracted by sum-difference beam except for time-delay testing, because the beam provides a range resolution in the FDA that cannot be deceived by traditional deceptive jamming. A strategy of using FDA to transmit two pulses with zero and nonzero frequency increments, respectively, is proposed to ensure that the angle of a target can be obtained by FDA. Moreover, the localization performance is examined by analyzing the Cramer-Rao lower bound and detection probability. Effectiveness of the proposed method is confirmed by simulation results.