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In response to the issues of relatively low image contrast, blurry boundaries of infrared small targets, and various environmental noise interferences in thermal imaging systems, resulting in reduced target recognition rates and tracking effectiveness. This paper proposes an infrared target image recognition algorithm based on fuzzy comprehensive assessment. Built upon infrared image sequences captured by an on-board thermal imager, the algorithm employs grayscale processing, neighborhood mean filtering, and second-order difference method to filter and enhance the infrared target images. This process improves the quality of infrared target images and enhances the contrast between targets and backgrounds. The algorithm utilizes the maximum interclass variance method for segmenting the infrared target images, combining it with the Canny operator to extract target edges, thereby obtaining clearer details of the infrared target edges. Finally, based on the extracted target feature information, the algorithm calculates the confidence of each marked region, establishes the membership function of target likelihood, employs a comprehensive weighted approach to construct the target confidence function, and compares the confidence of marked regions with that of the template image to achieve precise target recognition. Experimental validation against theoretical methods and comparisons with other approaches demonstrate the effectiveness and feasibility of the proposed method, providing theoretical support for reliable target tracking in electro-optical imaging systems.

This paper proposes a flexible-channel multiple-path delay feedback (FC-MDF) pipelined fast Fourier transform architecture, which is based on Radix-2 FFT algorithm. The FC-MDF can be dynamically configured to accommodate two levels of FFT size. Different from the bypass structure that prevents the sequence data from passing through the preceding stages when the FFT size is reduced, the FC-MDF can be dynamically configured into single-channel mode when calculating larger size sequences, and into dual-channel mode when calculating smaller size sequences by taking advantage of the first stage 0and sharing of processing elements in subsequent stages. The proposed architecture increases the utilization of computing units, including butterfly and complex multiplier, from 50% to 100%, and provides twice the throughput in dual-channel mode, compared with the traditional Radix-2 single-path delay feedback (R2SDF) architecture. We implemented this structure on Xilinx FPGAs. The experimental results show that, compared with current SDF, the proposed FC-MDF significantly optimizes the proportion of resources when the FFT size is reduced.

In big-data era, large amount of facial images could be used to breach the face identification system, which demands effective Face IDentification Differential Privacy (FaceIDP) of the facial images for widespread adoption of the face identification technique. In this paper, to our best knowledge, we take the first step to systematically study an effective important FaceIDP approach via the help of Dictionary Learning (DL) for secure releasing of facial images. First, a Dictionary Learning neural Network (DLNet) has been developed and trained with the facial images database, to learn the common dictionary basis of the facial image database. Then, the coding coefficients of the facial images are obtained. After that, the sanitizing noise is added to the coding coefficients, which obfuscates the facial feature vector that is used to identify a user’s identification. We have also proved that the FaceIDP is $\varepsilon $ -differentially private. More importantly, optimal noise scale parameters have been obtained via the Lagrange Multiplier (LM) method to achieve better data utility for a given privacy budget $\varepsilon $ . Finally, substantial experiments have been conducted to validate the efficiency of the FaceIDP with two real-life facial image databases.

The Corona Virus Disease 2019 (COVID-19) is highly infectious, has been spread worldwide, caused a global pandemic, and seriously endangered human health and life. The most effective methods for halting and stopping the transmission of the Corona Virus include early detection, quarantine, and successful treatment. Because it exhibits significant imaging characteristics for COVID-19 lesions in chest computed tomography (CT), it can be used to diagnose COVID-19. Aiming at the inaccuracies of uneven gray distribution, irregular regions, multi-scale, and multi-region segmentation in COVID-19 CT images. This paper proposed a novel Swin-Unet network to improve the accuracy of multi-scale lesion segmentation in COVID-19 CT images. First, in the double-layer Swin Transformer blocks of the Swin-Unet, a residual multi-layer perceptron (ResMLP) module was introduced and replaced the multi-layer perceptron (MLP) module to reduce the loss of features during the transmission process, thereby improving the segmentation precision of multi-scale lesion areas. Second, the uncertain region inpainting module (URIM) was added after Linear Projection, which can refine the uncertain regions in the segmentation features map, thereby improving the segmentation accuracy of different lesion regions. Third, a new loss function DF was designed. It can effectively improve the small target segmentation effect and thus improve the multi-scale segmentation result. Finally, the proposed method was compared to other methods on the public dataset. The Dice, Precision, Recall, and IOU of the proposed method are 0.812, 0.780, 0.848, and 0.683, respectively, which are better than the other models. Moreover, our model has fewer parameters and faster reasoning speed. The proposed method achieves excellent segmentation results for multi-scale and multi-region lesions, and it will be more beneficial in aiding COVID-19 diagnosis and treatment.

Accurate calculation of water/steam temperature and pressure in the solar field of direct steam generation (DSG) parabolic trough is essential to power dispatch and control. However, it is very difficult to achieve satisfied accuracy in limited time because of the existence of two-phase flow that has complex dynamic characteristics. In this work, a new hybrid model is proposed, which is based on the heat transfer mechanism enhanced by artificial neural network. This hybrid model has two characteristics as follows. (1) A heat-transfer and hydrodynamic coupling steady-state mechanism model as a prior model, which is used to quickly obtain the prior values including mechanism factors that are then used as input to an artificial neural network (ANN) model. (2) An ANN model consisting of two four-hidden-layer back propagation (BP) networks. The inlet pressure and outlet temperature of solar field are modelled in the first four-layer neural network, and then the outlet temperature of solar field is analyzed in the second four-layer neural network to improve the simulation accuracy. This model is verified by the data from DISS plant on 26 June 2001, and can be used for control studies and dispatching optimization. The calculation results show that the mean absolute percentage error (MAPE) of predicted inlet pressure and outlet temperature are 0.48% and 1.14%, respectively, which are better than the results of BP model, general regression neural network (GRNN) model, hybrid model with one BP network mentioned in this paper, and the dynamic simulation model mentioned in the literature.

Geometrical analysis of cone photoreceptor cells is important not only for ophthalmic diagnosis, but also for research on eye diseases. In this study, an automated cone photoreceptor cell segmentation and identification method based on morphological processing and watershed algorithm is presented for adaptive optics scanning laser ophthalmoscope images. Our method includes steps for image denoising, rough segmentation, fine segmentation, small region removal, and identification. The effectiveness of the proposed method was confirmed by comparing its results with those obtained manually yielding precision, recall, and F1-score values of 93.6%, 98.0% and 95.8%, respectively. The performance of our method is further verified by processing images with different cone photoreceptor cell densities from healthy retina and an image from an eye with diabetic retinopathy. The experimental results show that our algorithm achieved high accuracy in cone photoreceptor cell segmentation and identification in healthy retinas as well as in retina with diabetic retinopathy.

With the development of microelectronics and sensor technologies, implantable electronic devices are employed in many applications. These devices are distributed on or in the human bodies and are used to transmit signals wirelessly to external equipment. In conventional wireless communications, the antennas need a lot of space and power, and their strong electromagnetic interference limits the available locations for implantable devices. In the more recently developed galvanic coupling intra-body communication technology, human tissues are used as the media of signal transmission, and this method has therefore been applied to resolve the spatial limitations of conventional wireless communications methods. This paper presents a mathematical model of multi-layer galvanic coupling based on the volume conductor theory to analyze the transmission mechanism of these implantable intra-body communication devices. The proposed model is based on the quasi-static approximation conditions of Maxwell's equations, the field and potential are solved from Poisson's equation, and an equation was obtained to model the channel attenuation. The channel gain in a model of human limbs can be used to calculate within the frequency range of lesser than 1 MHz. To verify the accuracy and applicability of the model, the computed results were compared with the physiological saline and porcine tissue experimental results in the 100-kHz frequency.

Retina images acquired by an adaptive optics confocal scanning laser ophthalmoscope (AOSLO) usually need to remove image warp to improve image quality. The most significant task of AOSLO image dewarping is image registration. Most traditional feature-based registration algorithms used for AOSLO images are based on Gaussian scale space. However, the homogeneous Gaussian blurring reduces the localization accuracy of feature points and the distinctiveness of feature descriptors. In this paper, the accelerated KAZE (AKAZE) feature based on nonlinear scale space was utilized to register AOSLO retinal images for the first time, and an efficient strategy based on matched feature points for frame selection was proposed to automatically accomplish AOSLO retinal image dewarping. Moreover, a flexible method based on power spectra analysis is proposed to study the minimum number of frames needed to accomplish image dewarping. The extensive experiments demonstrated that the AKAZE method is more suitable for AOSLO image dewarping, benefitted with better accuracy, robustness, and rapidity compared with several traditional registration methods based on Gaussian scale space.

To study the signal transmission mechanism in the human body, the channel characteristics are generally analyzed by modeling. In current modeling methods, the human body is considered quasi-static and the human tissues isotropic, for simplifying the model and its calculation; however, this does not consider the effect of the human tissues on electric signal transmission, resulting in considerable deviations between the calculated results and the measured values. To reduce model errors and improve precision, a channel modeling method with human muscular-tissue characteristics is proposed in this study. In this method, Maxwell's equations is used as the governing equation and a galvanic-coupling intra-body communication channel model with human-tissue characteristics is built in the cylindrical coordinate system. By building a numerical model with the same parameters as in the analytical model, the analytical solution is proved to be correct. By comparing the different-sample anisotropic models and the isotropic models with the experimental results, it is concluded that the anisotropic model with muscular-tissue characteristics is superior to the isotropic model without muscular-tissue characteristics, with respect to the curve variation tendency and error between the model calculations and the experimental results. The precision of this anisotropic model is enhanced by 200%; hence, it is more accurate. At last, in order to study the optimal communication frequency of the channel, we select 50 healthy persons as the subjects of this experiment, we find that the optimal communication frequency band of the human arm is 10 kHz to 50 kHz. Within this frequency band, the channel gain is the largest, and the mean deviation of samples is less than 2dB, which is very beneficial to signal transmission in human body.

A line-scanning ophthalmoscope (LSO) is a retinal imaging technique that has the characteristics of high imaging resolution, wide field of view, and high imaging speed. However, the high-speed imaging with rather short exposure time inevitably reduces the signal intensity, and many factors, such as speckle noise and intraocular scatter, further degrade the signal-to-noise ratio (SNR) of retinal images. To effectively improve the image quality without increasing the LSO system's complexity, the post-processing method of image super-resolution (SR) is adopted. In this paper, we propose a learning-based multi-frame retinal image SR method that directly learns an end-to-end mapping from low-resolution (LR) image sequences to high-resolution (HR) images. This network was validated on down-sampled and real LSO image sequences. We evaluated the method on a down-sampled dataset with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and perceptual distance. Moreover, the power spectra and full width at half maximum (FWHM) were used as the no-reference image quality assessment (NR-IQA) algorithms to evaluate the reconstruction results of the real LSO image sequences. The experimental results indicate that the proposed method can significantly enhance the SNR of LSO images and efficiently improve the resolution of LSO retinal images, which has great practical significance for clinical diagnosis and analysis.

In this paper, we firstly analyze the statistical distribution of simultaneous sparse coding errors (SSCE), which reflects the local correlation and non-local correlation characteristics of natural images. Based on the observation, we establish the optimal denoising problem which uses L1 norm to constrain the image prior. According to the close-form solution of the proposed problem, we find that the denoising limit is only determined by the patch number, patch size and the variances of different bands in SSCE. Then we exploit the relationships between the patch complexity, patch size, patch number and the denoising limit. The study shows that a content-adaptive strategy may be useful to obtain better denoising performance. We then design a content-adaptive method for image denoising in which the groups are adaptively determined according to the structural complexities of reference patches. Experimental results show that the proposed scheme achieves competitive performance with several state-of-the-art methods in both subjective and objective aspects.