In this paper, we present a simple and effective scene-based nonuniformity correction (NUC) method for infrared focal plane arrays based on interframe registration. This method estimates the global translation between two adjacent frames and minimizes the mean square error between the two properly registered images to make any two detectors with the same scene produce the same output value. In this way, the accumulation of the registration error can be avoided and the NUC can be achieved. The advantages of the proposed algorithm lie in its low computational complexity and storage requirements and ability to capture temporal drifts in the nonuniformity parameters. The performance of the proposed technique is thoroughly studied with infrared image sequences with simulated nonuniformity and infrared imagery with real nonuniformity. It shows a significantly fast and reliable fixed-pattern noise reduction and obtains an effective frame-by-frame adaptive estimation of each detector's gain and offset.

With the advent of the era of big data, artificial intelligence has attracted continuous attention from all walks of life, and has been widely used in medical image analysis, molecular and material science, language recognition and other fields. As the basis of artificial intelligence, the research results of neural network are remarkable. However, due to the inherent defect that electrical signal is easily interfered and the processing speed is proportional to the energy loss, researchers have turned their attention to light, trying to build neural networks in the field of optics, making full use of the parallel processing ability of light to solve the problems of electronic neural networks. After continuous research and development, optical neural network has become the forefront of the world. Here, we mainly introduce the development of this field, summarize and compare some classical researches and algorithm theories, and look forward to the future of optical neural network.

This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.

Purpose To develop a software package for automated classification of anterior chamber angle of the eye by using ultrasound biomicroscopy. Methods Ultrasound biomicroscopy images were collected, and the trabecular-iris angle was manually measured and classified into three categories: open angle, narrow angle, and angle closure. Inception v3 was used as the classifying convolutional neural network and the algorithm was trained. Results With a recall rate of 97% in the test set, the neural network's classification accuracy can reach 97.2% and the overall area under the curve was 0.988. The sensitivity and specificity were 98.04% and 99.09% for the open angle, 96.30% and 98.13% for the narrow angle, and 98.21% and 99.05% for the angle closure categories, respectively. Conclusions Preliminary results show that an automated classification of the anterior chamber angle achieved satisfying sensitivity and specificity and could be helpful in clinical practice. Translational Relevance The present work suggests that the algorithm described here could be useful in the categorizing of anterior chamber angle and screening for subjects who are at high risk of angle closure.

This article introduces a framework to monitor complex dynamic and mildly nonstationary processes that are driven by a set of latent factors that can have different integration orders. The framework (i) relies on a novel deflation-based stationary subspace analysis that extracts latent source variables from recorded data sets in an iterative manner and (ii) utilizes the exact local Whittle estimator to calculate the fractional integration orders of the extracted source variables. The framework is embedded within a multivariate time-series structure to model the dynamic characteristics of the latent factors and to remove serial correlation in order to construct univariate monitoring statistics. A numerical and an industrial case study show that this framework is capable of modeling dynamic and mildly nonstationary variable inter-relationships that can have different integration orders.

In scene-based nonuniformity correction (NUC) methods for infrared focal plane array cameras, the statistical approaches have been well studied because of their lower computational complexity. However, when the assumptions imposed by statistical algorithms are violated, their performance is poor. Moreover, many of these techniques, like the global constant statistics method, usually need tens of thousands of image frames to obtain a good NUC result. In this paper, we introduce a new statistical NUC method called the multiscale constant statistics (MSCS). The MSCS statically considers that the spatial scale of the temporal constant distribution expands over time. Under the assumption that the nonuniformity is distributed in a higher spatial frequency domain, the spatial range for gain and offset estimates gradually expands to guarantee fast compensation for nonuniformity. Furthermore, an exponential window and a tolerance interval for the acquired data are introduced to capture the drift in nonuniformity and eliminate the ghosting artifacts. The strength of the proposed method lies in its simplicity, low computational complexity, and its good trade-off between convergence rate and correction precision. The NUC ability of the proposed method is demonstrated by using infrared video sequences with both synthetic and real nonuniformity. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). IntroductionThermal array detectors, also known as infrared focal plane arrays (IRFPA), are a rapidly developing technology and are used in a wide range of industry, medical, and military applications. However, the nonuniformity in IRFPAs, which is due to pixel-to-pixel variation in the detectors' response, can considerably degrade the quality of IR images since it results in a fixed-pattern noise (FPN) that is superimposed on the true image. 1 Therefore, nonuniformity correction (NUC), being an indispensable key step, is applied to nearly all of the IRFPA-based engineering applications. Further, what makes the problem worse is that the nonuniformity varies over time and is closely related to external conditions, 2, 3 which results in the failure of traditional reference-based NUC methods. In order to solve this problem, several scene-based nonuniformity correction (SBNUC) techniques have been recently developed.There are two main categories of SBNUC: statistical methods 4-7 and registration-based methods. 8-10 Compared with registration-based methods, statistical approaches have been well studied because of their relatively lower computational complexity, smaller storage demands, and better realtime performance. The most well-known statistical method relies on the global constant statistic (GCS) assumption, 4, 5 which states that the statistics of the observed scene become constant over time. This assumption requires that each detector in the array spend an equal amount of time observing a wide range of irradiance values. So it usually needs

For the study of propagation characteristics in underwater acoustic channels, a geometrybased model is introduced to represent the multipath scattering environments between a transmitter (Tx) and a mobile receiver (MR). To consider the impact of the scattering environments on the propagation characteristics with low complexity, we adopt a rectangle to describe the communication environments of the vertical cross section of the ocean, where the scatterers are assumed to be randomly distributed on the surface and bottom of the sea. In the model, we first derive the closed-form expressions for the probability density functions (PDFs) of the angle of departure (AoD) and angle of arrival (AoA) statistics. Then, we investigate the spatial and frequency correlation functions of two different propagation paths. The numerical simulation results fit the conventional results very well, which demonstrate that the proposed model has the ability to describe the underwater acoustic communication environments. INDEX TERMS Underwater acoustic channels, geometry-based channel model, angle of departure AoD and angle of arrival AoA statistics, spatial and frequency correlation functions. I. INTRODUCTION A. MOTIVATION Underwater wireless networks, which have the advantages of scientific exploration, tactical surveillance, and offshore exploration, have been widely researched in wireless communication systems [1]. To make these applications feasible, it is important to design and analyse wireless underwater acoustic communication systems. In light of this, statistical propagation characteristics of underwater communication channel are necessary to assess system performance to improve the quality of a communication system [2]. B. RELATED WORKS The existing literatures have done a variety of studies on the simulation of underwater acoustic wireless channels, which are usually based on the experimental data obtained in some The associate editor coordinating the review of this manuscript and approving it for publication was Guan Gui.

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