Miracle-dz/UniFAR-FTUs huggingface dataset:
Infrared polarization detection method for weak target in sky background

The false alarm generated by the background object in the complex sky background is a difficult problem in the detection of infrared small targets. The problem is particularly prominent when the target signal is weak. In order to effectively suppress the false alarm rate and improve the detection rate, a weak target detection algorithm based on infrared polarization map is proposed. Firstly, the data is collected by the infrared polarization detecting device, and then the polarization component map with the best image quality is analyzed. Finally, the filtering algorithm combined with Top- Hat morphological filtering and median filtering is used, and the global component threshold detection method is used to complete the polarization component map. Weak target detection. The experimental results show that the proposed method has higher detection rate and lower false alarm rate than the Top-Hat algorithm, maximum mean filtering algorithm, and LCM algorithm. An effective method for detecting weak targets in the background.
To estimation of the chlorophyll concentration from measurements of fluorescent characteristics at Lake Baikal

Starting from 2006, we conduct combined spring expeditions over the water area of Lake Baikal. These expeditions include measurements of fluorescent characteristics at different water levels from the surface down to the bottom. Direct methods for determination of biomass and chlorophyll concentration in algae are quite laborious and do not allow in situ analysis of the results. Fluorescent methods, which are fast enough, are generally indirect and should be specially justified to provide the quantitative information about biomass or chlorophyll concentration. In this study, the results of synchronous measurements are used to examine the feasibility of empirical calibration and quantitative reconstruction of the chlorophyll content from measured fluorescent characteristics.
Low dose x-ray CT image denoising via U-net in projection domain

Low dose computed tomography (LDCT) has attracted considerable interest in medical imaging fields. Reducing tube current intensity and decrease the exposure time are the two main ways in clinic applications. Nevertheless, the resulting statistical noise will seriously degrade CT image quality for diagnosis. To make full use of the original projection data as well as further improving the small dataset processing ability of U-net, this study aimed to investigate a low dose X-ray CT image denoising method via U-net in projection domain (PDI U-net). Meanwhile, in view of avoiding the excessive smoothing of the small structures, the inception module is introduced in the encoding stage of the network. And different convolution kernel operations of 1×1, 3×3, and 5×5 are used in parallel to obtain multi-scale image features, increasing the depth and width of the network while reducing the parameters. Furthermore, the shortcut connection is utilized to transfer the low-level area local detail information to the high-level area. By merging it with the global information of the high-level area, the proposed network can maintain the image details while de-noising. The experimental results show that the method proposed can significantly improve the image quality with clear feature edges and close visual appearance to the reference high dose CT images. Compared with LDCT and Residual Encoder-Decoder Convolutional Neural Network (RED-CNN), the peak signal to noise radio (PSNR) is improved by 9.02dB and 2.74dB, and the structural similarity (SSIM) is improved by 0.43 and 0.07, respectively.
Spoof detection for fake biometric images using feature-based techniques

Ensuring the actual presence of a genuine legitimate trait as opposed to a fake self-manufactured synthetic is a major problem in bio-metric authentication. The proposed system's objective is to improve the reliability of bio metric recognition systems through the use of image quality evaluation. The proposed technique uses general image quality features derived from a single image to distinguish between legitimate and impostor samples, making it optimal for applications with a very low degree of complexity. In the proposed method, we are using publicly available ATVSFir_ DB dataset of iris which makes it highly competitive. We have also tested the algorithm on self-generated dataset for authenticity and rigorous testing purposes. The results acquired from the experimental phase were satisfying and authentic. The proposed method is able to achieve an averaged accuracy of 99.1% for the ATVS-Fir_DB dataset and 99.9% for the self-generated dataset.
Generalists versus specialists in fluctuating environments: a bet-hedging perspective

Bet-hedging evolves in fluctuating environments because long-term genotype success is determined by geometric mean fitness across generations. However, specialist versus generalist strategies are usually considered in terms of arithmetic mean fitness benefits to individuals, as in habitat or foraging preferences. We model how environmental variability affects phenotypic variation within and among individuals to maximize either long-term arithmetic versus geometric mean fitness. For traits with additive fitness effects within lifetimes (e.g. foraging-related traits), genotypes of similar generalists or diversified specialists perform equally well. However, if fitness effects are multiplicative within lifetimes (e.g. sequential survival probabilities), generalist individuals are always favored, since geometric mean fitness favors greater within-individual phenotypic variation than arithmetic mean fitness does. Interestingly, this conservative bet-hedging effect outcompetes diversifying bet-hedging. These results link behavioral and ecological specialization and earlier models of bet-hedging, and thus apply to a range of natural phenomena from habitat choice to host specificity in parasites. Impact summary Which factors determine whether it is better to be a specialist or a generalist? Environmental fluctuations are becoming larger and more unpredictable across the globe as a result of human-induced rapid environmental change. A key challenge of evolutionary biology is therefore to understand how organisms adapt to such variation within and among generations, and currently represents a knowledge gap in evolutionary theory. Here we focus on how traits evolve when the (changing) environment determines the optimal value of a trait, so that the optimal trait value changes unpredictably over time. Our mathematical model investigates how much variation is optimal in a trait. We expect specialists (low within-individual trait variation) to be favored in stable environments, with generalists (high trait variation) favored in more variable environments. We show that the answer depends on whether we look from the point of view of the individual or all individuals of the same genotype. If an individual does well in the short term, but its offspring all experience a different environment and therefore do badly, the genotype as a whole is in trouble, and will not be favored in the long term. One solution to this problem could be to produce offspring with different trait values, to ensure that at least some of the offspring do well no matter the environmental conditions they grow up in. This “don’t put all your eggs in one basket” diversification strategy is well-known in some organisms, but how helpful is it if there is also some within-individual (i.e. generalist) trait variation? By answering these questions under various environmental scenarios, we link together many different concepts in evolutionary ecology and animal behavior, increasing our understanding about how organisms may cope with the current changes in environmental conditions around the world.
Distinction of bloods based on photoacoustic spectroscopy combined with PCA-LDA algorithm

To rapidly and accurately distinguish the real and fake bloods, a photoacoustic detection system was established based on an optical parameters oscillator (OPO) pulsed laser and a focused ultrasonic transducer in this paper. The time-resolved photoacoustic signals of ten cow bloods and ten props bloods were captured. The amplitudes and profiles of photoacoustic signals for real and fake bloods were different. In addition, the photoacoustic peak-to-peak values of all blood samples were obtained from 700nm to 1064nm, and used as the train samples. The photoacoustic peak-to-peak values were firstly standardized and the data dimensions were reduced by using principle component analysis (PCA) method. Different principle components were chosen according to the cumulative contribution rate of characteristic values for the standardized photoacoustic peak-to-peak data matrix. Then, the scores of different principle components for ten cow bloods and ten props bloods were obtained and used as the train data. Five cow bloods and five props bloods were test. The linear discrimination analysis (LDA) algorithm was used to distinguish the real and fake bloods. The test results show that, based on the score data for two principle components by using PCA, the correct rate (CR) of real and fake bloods was improved from 80% to 100%. Results show that the LDA coupled with PCA, i.e.,PCA-LDA, has good distinction effect for the real and fake bloods.
Denoising method for photoacoustic microscopy using deep learning

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.
G-CoReCCD: a GPU-based simulator of the charge transport in fully-depleted CCDs

We introduce a simulator of charge transport in fully-depleted, thick CCDs that include Coulomb repulsion between carriers. The calculation of this long-range interaction is highly intensive computationally, and only a few thousands of carriers can be simulated in reasonable times using regular CPUs. G-CoReCCD takes advantage of the high number of multiprocessors available in a graphical processing unit (GPU) to parallelize the operations and thus achieve a massive speedup. We can simulate the path inside the CCD bulk for up to hundreds of thousands of carriers in only a few hours using modern GPUs.
Los meniscos viables ¿pueden ser más fácilmente almacenados en un medio de cultivo alternativo? Estudio in vitro

Objetivo: El trasplante de aloinjertos meniscales (TAM) viables es complejo desde el punto de vista logístico. El objetivo fue comparar la estructura colágena y la viabilidad celular en dos medios de cultivo. Métodos: Se utilizaron 10 meniscos, que fueron divididos en 3 secciones. El metabolismo celular fue estudiado con la expresión génica de colágeno tipo I y II, y agrecano. La red colágena fue valorada con la escala Collagen Meniscal Architecture (CMA) tras su análisis con microscopia electrónica. Resultados: Colágeno I se expresó más en FBS que ITS (p = 0,036). Colágeno II se expresó más en el GC. No se observaron diferencias con la escala CMA. Conclusiones: ITS es tan efectivo como el FBS para preservar la red colágena. La expresión genética de las proteínas estudiadas fue similar en el grupo ITS que en el GC. ITS podría ser una mejor alternativa que FBS o suero del receptor como suplemento para preservar meniscos.
Dual-focal camera HDR imaging based on convolutional neural network

In this paper, we propose a method which is based on the dual-focal camera facing the same target to expanse the dynamic range of images. Since the spatial resolution of dual-focal camera in this paper is different, down-sampling, up-sampling, and multi-resolution fusion are required in image fusion processing to obtain an ideal high dynamic range image. The current multi-frame high dynamic range algorithm is mainly for similar resolution images. When there are two images with large resolution differences, The effect of ordinary registration algorithms (For example, optical flow registration algorithm) are limited, and the image may appear ghost and color artifacts after registration. Our method uses a convolutional neural network, which composed of two subnets. An image fusion subnet and a style transfer subnet. Because there is only one exposure image in the surrounding field of view, the central field of view is processed separately from the surrounding field of view. In the central field of view, U-Net is used to register the images layer by layer to increase the registration speed and registration accuracy. After the high dynamic range image in the central field of view, the style transfer network is used to transfer the color distribution of the high dynamic range image to the surrounding field of view. As for result, we performed extensive qualitative and quantitative comparisons to show that our method produces excellent results where ghost and color artifacts are significantly reduced compared to existing general multi-frame high dynamic range methods, and is robust across various inputs.
Network training parameters exploration for generating starting points of freeform reflective imaging system design based on machine learning

Using freeform optical surface in an imaging optical system is a revolution in the field of optical design. Introducing machine learning into freeform imaging optical design will significantly reduce the human effort and even beginners in optical design will be able to perform difficult design tasks. Machine learning has been successfully applied to the immediate generation of starting points with various system specifications for the design of freeform reflective imaging systems. However, the parameters used in the network training, which are the key points in the whole design framework, are determined without proper guidance, which may significantly affect the actual performance of the networks. In this paper, a comprehensive exploration of the training parameters of the neural network used for starting points generation of freeform reflective systems is conducted. The parameters include the number of layers, the number of nodes in the layers, the type of activation function, the type of loss function, the type of optimization algorithm, and the value of learning rate. A detailed comparison and analysis of different training parameters are demonstrated on the training results and the imaging performance of validation output systems with large amount of random system specifications input. Using the obtained results designers can choose proper parameters accordingly and get desired neural networks with shorter training time and better performance. The results also offer insight in the design of imaging systems with other system configurations and more advanced system specifications.
Crop seed classification based on a real-time convolutional neural network

The yield of agricultural crops is directly related to the quality of the seed. One of the ways to improve the quality is to sort seeds by physical and optical properties (color, shade of color, shape, size and structure) using grain cleaning units (photoseparators or color sorters). Currently, two main approaches are used to create sorting algorithms: generalized statistical and neural networks. The benefits of a neural-based sorting approach include the absence of any restrictions on training series; there is no need to pre-examine the nature of the data and adjust sorting algorithm. Nowadays, neural networks are better than statistical methods, especially in the case of deep neural networks. The disadvantages of neural networks are in the high computational complexity of obtaining results and huge size of the training series, sufficient to achieve high accuracy in making reliable decisions. This work discusses the creation of a convolutional neural network architecture that allows dividing the input flow of wheat seeds into two classes: "good" and "bad" (with flaws in shape and color). The implementation of convolutional neural network on FPGA using the Xilinx System Generator for DSP and Matlab / Simulink package is also considered. The results of a convolutional neural network training, assessment of the accuracy of seed correct classification, and verification of the convolutional neural network hardware FPGAs implementation are presented.
Research on visualization method of high dynamic range infrared image based on M-CLAHE

The analog signal output by the infrared detector is usually sampled as a 14-bit high-dynamic-range digital infrared image, which is not compatible with most 8-bit display systems and cannot be directly used for imaging display. Therefore, image compression quality is very important for image display . This paper studies and analyzes the mapping technology of the base layer, focusing on the traditional limited contrast histogram equalization method (CLAHE). Considering that the conventional CLAHE method is applied to infrared images, there will be a problem of excessive stretching of some uniform areas. a new algorithm for Contrasted Limited Adaptive Histogram Equalization based on uniform region test (M-CLAHE) is proposed. Based on the traditional CLAHE method, this method introduces a gradient operator to extract the contour boundary of the sub-region image, and quantifies the complexity of the image region to solve the above problems. The experimental results show that compared with the traditional CLAHE infrared image compression method, this method can more than double the visual indicators such as image contrast and average gradient compared with the traditional visualization method. This method has engineering portability, and it can be transplanted to the infrared movement to increase the image quality of most scenes by about twice.
ResDeep convolutional networks for terahertz image semantic segmentation

In recent years, terahertz imaging system has become more and more mature. There are good detectability for hidden objects on the human body surface, because terahertz waves has certain penetrating characteristics and low electromagnetic radiation in terahertz band. Using terahertz imaging technology has the advantages of high efficiency and non-radiation to carry out human body safety inspection, that makes it more and more widely used in the security security field. In order to achieve the purpose of detecting the hidden objects on the human body surface using terahertz technology, a convolution neural network named ResDeepNet is proposed in this paper. This network is different from the convolution neural networks structure of traditional optical images. According to the terahertz imaging characteristics of human body image, the semantic segmentation network is designed. Through the convolution network, the segmentation algorithm of terahertz image is realized, the target information is enhanced, and the noise is effectively suppressed in the terahertz image. The segmentation network and algorithm achieved the best results in test and practical application.
Solutions on global synthesis to improve multiple configurations

Multiple configurations system and its influence and control on aberration are the basis for the design of such systems and have practical value in engineering. The principle, modeling and error analysis are analyzed. Based on the system configuration of the compound zoom system, moreover, this paper advances the algorithm analysis. With an interval which between the elements as the initial amount, the form of motion of the elements is used as the free amount, and the distribution of the optical focal length and the form of the element motion can be obtained by calculating the equation sets. Compared with the traditional method, the global synthesis can significantly improve the design in effect and efficiency.
Surface impedance of the structure: thick ice-sea in VLF-VHF range of radio waves

For calculations of the electromagnetic field over the two-layer structure “thick ice-sea”, the areas of applicability of the impedance boundary conditions for a vertically polarized wave in the range of 0.01 ˗ 120 MHz are determined. Seven models of ice cover are considered. The ice thickness varied from 2 to 9 m. Results are needed to calculate the attenuation function W and the field level in the LF-MF-HF range of radio waves.
Color-guided depth map super resolution using joint convolutional neural network

As an expression of three-dimensional information, depth map provides more possibilities for many computer vision applications, which puts forward higher quality requirements for depth maps. However, the spatial resolution of depth map is always low, which limits its potential. In this paper, we present a depth map super-resolution based on joint convolutional neural network (J-CNN). The network combines RGB image to guide the reconstruction of low-resolution depth map. Our model consists of three subnets. Subnet 1 uses simple convolutional layers to extract rough features of depth maps. Subnet 2 and 3 use a progressive up and progressive down sampling network structure. This structure can greatly expand the receptive fields and help to extract more fine features of the images at different scales. We use it to extract the complex features in the depth map and RGB image. Finally, convolutional layers connect three subnets to transfer useful information from RGB image to depth map. The final up-sampling reconstructed operation is realized by sub-pixel convolution, effectively avoiding the “checkerboard effect”. Our J-CNN is evaluated on Middlebury datasets which shows improved performance compared with six advanced methods. Our model also shows strong robustness under large sampling factor (16 times).
Optimization and application of the SVM analysis model parameters for mixed gas

Combining support vector machine with infrared spectrum analysis, the infrared spectra analysis model of mixed gas is established. Taken the mixed gas containing hydrocarbon as an example, the selection and optimization of model parameters is researched through experiment. Beginning with the SVM analysis model, infrared spectrometer and spectra data, spectra analysis band and spectrometer scanning interval, the detailed study of how the parameters like the types of kernel function for the SVM analysis model, penalty factor C, method of spectra data preprocessing, spectrometer scanning interval, spectra analysis band, etc. Affect the analysis result are carried out. The experimental results show that under the condition of the SVM analysis model is determined, a reasonable selection and optimization of the SVM analysis model parameter can improve the accuracy of the analysis results, and it has a practical application value.