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Chebyshev moments were recently proposed to address problems of moments of continuous orthogonal basis. They are moments of a discrete basis and are orthogonal in the domain of the image coordinate space, a feature which presents a number of advantages over moments of continuous orthogonal basis. No effort has been devoted yet to the efficiency of their calculation. In this paper, we present a hardware architecture which enables real-time processing of binary and grayscale images of up to 4 Mpixels. This architecture is realized in a field-programmable gate array of typical clock frequency of 100 MHz. Index Terms--Chebyshev moments, field-programmable gate arrays (FPGAs), real-time image analysis.

Zernike moments have been proven to be very powerful image descriptors. However, their computational complexity makes them unsuitable for real-time applications. In this paper, a mathematical relationship between geometric and Zernike moments is extracted. In this way, the computation of geometric moments is adequate to derive Zernike moments. Since geometric moments can be efficiently implemented in hardware and their calculation can be performed in real-time, we propose here a new real-time hardware architecture for the computation of Zernike moments. This method outperforms existing software approaches, especially for large images, allowing real-time processing of images up to 4 Mpixels. Index Terms--FPGAs, image retrieval, real-time image analysis, Zernike moments.

In this paper, a hardware implementation of a fuzzy processor suitable for morphological color image processing applications is presented for the first time. From the hardware point of view, only a small number of algorithms for hardware implementation of soft gray-scale morphological filters have been reported in the literature, since research in mathematical morphology focuses mainly on possible extensions of the standard definitions (e.g., color and fuzzy mathematical morphology). The proposed digital hardware structure is based on a sequence of pipeline stages, and parallel processing is used in order to minimize computational times. It is capable of performing the basic morphological operations of standard and soft erosion/dilation for color images of 24-bit resolution. For the computation of morphological operations, a 3 x 3-pixel image neighborhood and the corresponding structuring element are used. However, the system can be easily expanded to accommodate windows of larger sizes. The architecture of the processor is generic; the units that perform the fuzzy inference can be utilized for other fuzzy applications. It was designed, compiled, and simulated using the MAX+PLUS II Programmable Logic Development System by Altera Corporation. The fuzzy processor exhibits a level of inference performance of 601 KFLIPS with 54 rules, and can be used for real-time applications where the need for short processing times is of the utmost importance. The selection of a latest technology computer system (Pentium 4/3 GHz with SSE-2) can speed up image processing applications, but the time required still cannot be compared to the corresponding time using this hardware structure. Index Terms--Color image processing, FPGAs, fuzzy systems, mathematical morphology.

This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

We construct recurrence plots (RPs) and conduct recurrence quantification analysis (RQA) to investigate the dynamic properties of the new Center for Financial Stability (CFS) Divisia monetary aggregates for the United States. In this study, we use the latest vintage of Divisia aggregates, maintained within CFS. We use monthly data, from January 1967 to December 2020, which is a sample period that includes the extreme economic events of the 2007-2009 global financial crisis. We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin. The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008. Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates, we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times., Competing Interests: Competing interestsThe authors declare that they have no conflictc of interest., (© The Author(s) 2023.)

The limited depth of field of optical lenses, makes multi-focus image fusion (MFIF) algorithms of vital importance. Lately, Convolutional Neural Networks (CNN) have been widely adopted in MFIF methods, however their predictions mostly lack structure and are limited by the size of the receptive field. Moreover, since images have noise due to various sources, the development of MFIF methods robust to image noise is required. A novel robust to noise Convolutional Neural Network-based Conditional Random Field (mf-CNNCRF) model is introduced. The model takes advantage of the powerful mapping between input and output of CNN networks and the long range interactions of the CRF models in order to reach structured inference. Rich priors for both unary and smoothness terms are learned by training CNN networks. The α -expansion graph-cut algorithm is used to reach structured inference for MFIF. A new dataset, which includes clean and noisy image pairs, is introduced and is used to train the networks of both CRF terms. A low-light MFIF dataset is also developed to demonstrate real-life noise introduced by the camera sensor. Qualitative and quantitative evaluation prove that mf-CNNCRF outperforms state-of-the-art MFIF methods for clean and noisy input images, while being more robust to different noise types without requiring prior knowledge of noise.

Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important. Transform-domain methods have been applied to image fusion, however, they are likely to produce artifacts. In order to cope with these issues, we introduce the Conditional Random Field (CRF) CRF-Guided fusion method. A novel Edge Aware Centering method is proposed and employed to extract the low and high frequencies of the input images. The Independent Component Analysis-ICA transform is applied to high-frequency components and a Conditional Random Field (CRF) model is created from the low frequency and the transform coefficients. The CRF model is solved efficiently with the α-expansion method. The estimated labels are used to guide the fusion of the low-frequency components and the transform coefficients. Inverse ICA is then applied to the fused transform coefficients. Finally, the fused image is the addition of the fused low frequency and the fused high frequency. CRF-Guided fusion does not introduce artifacts during fusion and supports image denoising during fusion by applying transform domain coefficient shrinkage. Quantitative and qualitative evaluation demonstrate the superior performance of CRF-Guided fusion compared to state-of-the-art multi-focus image fusion methods.

Asynchronous movement of the carotid atheromatous plaque from B-mode ultrasound has been previously reported, and associated with higher risk of stroke, but not quantitatively estimated. Based on the hypothesis that asynchronous plaque motion is associated with vulnerable plaque, in this study, synchronisation patterns of different tissue areas were estimated using cross-correlations of displacement waveforms. In 135 plaques (77 subjects), plaque radial deformation was synchronised by approximately 50% with the arterial diameter, and the mean phase shift was 0.4 s. Within the plaque, the mean phase shifts between the displacements of the top and bottom surfaces were 0.2 s and 0.3 s, in the radial and longitudinal directions, respectively, and the synchronisation about 80% in both directions. Classification of phase-shift-based features using Random Forests yielded Area-Under-the-Curve scores of 0.81, 0.79, 0.89 and 0.90 for echogenicity, symptomaticity, stenosis degree and plaque risk, respectively. Statistical analysis showed that echolucent, high-stenosis and high-risk plaques exhibited higher phase shifts between the radial displacements of their top and bottom surfaces. These findings are useful in the study of plaque kinematics.

In this paper, a novel multi-focus image fusion algorithm based on conditional random field optimization (mf-CRF) is proposed. It is based on an unary term that includes the combined activity estimation of both high and low frequencies of the input images, while a spatially varying smoothness term is introduced, in order to align the graph-cut solution with boundaries of focused and defocused pixels. The proposed model retains the advantages of both spatial-domain methods and multi-spectral methods and by solving an energy minimization problem and finds an optimal solution for the multi-focus image fusion problem. Experimental results demonstrate the effectiveness of the proposed method that outperforms current state-of-the-art multi-focus image fusion algorithms in both qualitative and quantitative comparisons. In this paper, the successful application of the mf-CRF model in multi-modal image fusion (visible-infrared and medical) is also presented.

Background: The diagnosis of bacterial infection in cirrhotic patients may be difficult, because of the absence of classical signs such as fever and raised white blood cell count. The role of C-reactive protein (CRP) in this context has not been clearly defined., Methods: Clinical and laboratory characteristics of 210 consecutive cirrhotic patients with (n=100) or without (n=110) bacterial infection were compared with a control group of non-cirrhotic patients with infection (n=106)., Results: Significantly fewer patients with cirrhosis had a body temperature ≥37°C when presenting with bacterial infection (56% cirrhotic vs. 85.5% non-cirrhotic patients, P=0.01). Mean leukocyte count was 6.92 × 10 3 /mm 3 in patients with cirrhosis and infection, 5.75 × 10 3 /mm 3 (P=0.02) in cirrhotic patients without infection, and 11.28 × 10 3 /mm 3 in non-cirrhotic patients with infection (P<0.001). Multivariate analysis revealed that CRP level and model for end-stage liver disease score were significantly associated with the presence of infection in patients with cirrhosis. A cutoff level of CRP>10 mg/L indicated the presence of infection with a sensitivity of 68%, a specificity of 84.5% and an area under the receiver operating characteristic curve of 0.8197. CRP cutoff level differed according to the severity of the liver disease: Child-Pugh score (CPS) A: 21.3 mg/L, B: 17 mg/L, and C: 5.78 mg/L., Conclusions: CRP at admission could help diagnose infection in cirrhotic patients. Since the severity of liver disease seems to affect the CRP values, lower CRP levels might indicate infection. Clinical suspicion is necessary to avoid delay in diagnosis and initiate antibiotic treatment., Competing Interests: Conflict of Interest: None