SbD-Driven Microwave Imaging for Biomedical Diagnosis

Practical applications of microwave imaging often require the solution of inverse scattering problems with inhomogeneous backgrounds. Toward this end, a novel inversion strategy, which combines the multiscaling (MS) regularization scheme and the difference contraction integral equation (DCIE) formulation, is proposed. Such an integrated approach mitigates the nonlinearity and the ill-posedness of the problem to obtain reliable high-resolution reconstructions of the unknown scattering profiles. The arising algorithmic implementation, denoted as MS-DCIE, does not require the computation of Green’s function of the inhomogeneous background, and thus, it provides an efficient and effective way to deal with complex scenarios. The performance of the MS-DCIE is assessed by means of numerical and experimental tests in comparison with competitive state-of-the-art inversion strategies as well.

This paper suggests a nature-inspired optimization technique, the Cheetah Optimizer (CO), to achieve optimal Directional Overcurrent Relays (DOCRs) coordination. The main contribution of this work is proposing a recent effective optimization technique (CO) for optimal coordination of DOCRs to clear the faults in the power system as soon as possible. CO strategy can solve nonlinear, non-convex complex coordination problems to achieve a fast and selective protection system. CO has advanced features overcoming other strategies, such as fast convergence, lower computational time, moderate exploration and exploitation modes, and attaining the optimal solution, avoiding stuck in local optima. Three test systems were used in this work. The standard IEEE-3, IEEE-9, and IEEE-30 bus test systems were used to confirm the performance of the proposed technique. The results yielded by the proposed algorithm were compared with other recently established counterparts, such as the Chimp Optimization Algorithm (ChOA), Osprey Optimization Algorithm (OOA), Coati Optimization Algorithm (COA), Seagull Optimization Algorithm (SOA), and Pelican Optimization Algorithm (POA). The proposed technique's reliability, stability, and consistency were recognized by inclusive statistical analysis. The proposed approach offered high-quality and robust solutions with lower computational processing times. In addition, it has a remarkable convergence, giving a benefit over adaptive coordination tendency by improving monitoring, communication, and grid control.

The solution of an inverse source problem usually embraces the microwave imaging context. Indeed, microwave imaging solutions require the incident field distribution within the imaging domain. In the field of microwave imaging with amplitude only data, a phaseless inverse source problem has been addressed, by combining a holography-based method, directly operating into the spatial domain, with a source reconstruction method. The combined method is tested for the field characterization of a series of source with different directivity levels. Then the field modeling strategy has been included within a phaseless inverse scattering solution and applied to a 3D in-silico test case.

Online monitoring of multimedia networks is required to ensure seamless and ubiquitous delivery of services to the end users. Quality of multimedia content, such as video streams, often gets degraded due to network losses such as packet loss. Frame freezing artifacts are introduced in a video stream when packet loss or packet delay takes place. Estimating the perceptual impact of these artifacts on quality of experience of end users helps service providers to maintain quality of service. In this paper, we have presented a novel no-reference video quality metric, which measures the impact of frame freezing due to packet loss and delay in video streaming networks. The proposed metric is based on several features that directly impact the quality of experience of end users. These features, including motion characteristics of videos, are calculated using the temporal information between video frames and then combined mathematically to form a video quality metric. Different weights are assigned to different features for better performance of the proposed metric. With detailed experiments, we have shown that our method outperforms other contemporary methods in terms of high accuracy and low computation time in frame freeze detection, low root mean square values, high coefficient of determination, and high correlation between subjective and objective measurements. We have used five video databases for our model's evaluation and validation. Furthermore, we have shown that our method is statistically superior to the other models in comparison.

This paper present an improved reconstruction algorithm of the multi-gray level images based on overlapping block method using exact continuous moments computation: Legendre , Zernike, Pseudo-Zernike and Gegenbauer moments . We solve the artifact issue caused by unitary block reconstruction which affects the visual image quality. This method aim to ensure high accuracy and low computation time, using only small finite number of moments. Our approaches aims to introduce these moments in the field of data compression and local feature extraction for pattern recognition. Experimental results show the superiority of our proposed approaches over the existing methods.

Aviation promotes trade and tourism by connecting regions, people, and countries. Having a functional and efficient airport pavement network is important to improve aviation traffic and to provide safer mobility to almost 800 million passengers travelling in the U.S. per year. The Federal Aviation Administration has initiated and actively been participating in many projects to further advance pavement design and performance to meet user requirements. To accomplish that, quantitative data are needed; such data may be collected from the pavement response to gear and environment loading. In this study, responses from four instrumented taxiway concrete slabs at John F. Kennedy International Airport were analyzed. The collected data were used to develop machine-learning (ML) based prediction models to compute the temperature, curling and bending strains within pavement. The ML models were developed using the support vector machine (SVM) algorithm. The results showed that SVM based ML models can predict pavement responses with a high accuracy and low computation time. Furthermore, in the case of feeding more data from various airports, ML models have proven to be a promising technique for pavement analysis engine for future airport pavement design frameworks. This study also produces recommendations for future data collection projects to have well-designed databases for data-driven models development.

In this paper, we propose a novel method for reconstruction of the multi-gray level images using the exact computation of Legendre moments. The purpose of this method is to ensure high accuracy and low computation time, by dividing the image into a set of blocks instead of treating the whole image. For mitigating the blocking artifact involved in the reconstruction process, we propose a new approach based on block Reconstruction using a Global Overlapping Block and Exact Legendre Moments computation (GOBRELM). A typical comparison of the proposed method with the conventional ones concludes to very impressive and promising results concerning image quality and time consuming. The main motivation of the proposed approach is to allow fast and efficient reconstruction algorithm, with improvement of the reconstructed image quality and time consumption. This makes our method attractive for real time applications.

This work presents a novel inverse scattering (IS) methodology to deal wit.li the retrieval of the electromagnetic (EM) properties of unknown scat.t.erers. The proposed technique is based on the effective combination of a customized Bayesian compressive sensing (BCS) solver wit.li the iterative multi-scaling approach (IMSA). Accordingly, a-priori information on the class of imaged targets as well as progressively acquired information on their location and size is exploited to yield accurate and robust reconstructions. Moreover, a contrast source inversion (CSI) formulation is adopted in order to enable the retrieval of lion-Born scat.t.erers. Numerical results are shown to verify the effectiveness of the proposed IMSA-BCS-CSI method, as well as to compare it with state-of-the-art alternatives.

LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system

Active bending is an increasingly popular construction technique that uses elastically bent structural members to form complex curved shapes. The design and analysis of bending-active structures requires an accurate simulation of the bending process, which is often complicated by the occurrence of large displacements. In this article, we propose to combine a previously developed implicit dynamic relaxation method with co-rotational beam elements to obtain a fast and accurate method for form-finding and analysis of bending-active structures. This approach is applied to four test cases. Implicit dynamic relaxation is compared to the classic Newton–Raphson method and conventional dynamic relaxation. The results show that the proposed implicit dynamic relaxation approach can be stabilized intuitively by changing the time step and damping ratio, making it more stable than the classic Newton–Raphson method. Moreover, the proposed approach converges fast compared to the conventional dynamic relaxation: the total computation time is considerably lower, even though the computation time per iteration is higher. Finally, a high accuracy is achieved due to the use of co-rotational beam elements. The combination of high accuracy and low computation time makes this approach well-suited for both form-finding and analysis of bending-active structures.

In an inverse scattering problem, the constituent parameters of a scatterer are identified from the scattered field. In this paper, we use the real-coded clonal selection algorithm (RCSA) along with an iterative absorbing boundary condition (IABC) to control the computational cost of the analysis during the optimization process. A relatively less accurate boundary condition is used in the global search and a progressively improved boundary condition for the local search. Some results are presented to illustrate the accuracy and the significant reduction in the computation time obtained with our approach.

Evaluation of electromagnetic characteristics of 3D-printed radar absorbing structures based on three-dimensional period pattern surface

The paper presents a novel integrated system in which a number of image processing algorithm are embedded within a genetic algorithm (GA) based framework in order to provide an adaptation and better quality analysis with less computational complexity while maintaining flexibility to a broad range of defects. A specially tailored hybrid GA (HGA) is used to estimate geometric transformation of arbitrarily placed printed circuit boards (PCBs) on a conveyor belt without any prior information such as CAD data. A library of image processing functions is accessed by the HGA within an intelligent framework. These functions include operations such as fixed multi-thresholding, Sobel edge-detection, image subtraction and noise filters. The proposed framework allows novel composition of tasks such as edge-detection and thresholding in order to increase defect detection accuracy with low computational time. Our simulations on real PCB images demonstrate that the HGA is robust enough to detect any missing components and cut solder joint with any size and shape with significant reduction in computational time compared to conventional approaches

<p>In this abstract we propose the results of GPR measurements [1-2] performed in a site of cultural interest. In particular, the measurements were performed in a rectangular area inside the Messapic archaeological ancient settlement of Cavallino, close to Lecce (southern Italy) with a RIS-Hi model GPR system manufactured by IDSGeoradar s.r.l. and belonging to the Institute for the Electromagnetic Sensing of the Environment IREA-CNR. The data processing was performed according to a classical sequence of steps provided by zero timing, background removal, gain vs. depth, 1D filtering and time domain migration [3]. Afterwards, slicing was performed too and the results were georeferenced in QGIS thanks to the coordinatives of the four vertex of the rectangular area. The results indicate that there are some possible Messapic remains in the investigated area and suggest somehow the most promising point for a future localized excavation.  Future development will regard further processing of the data with an inverse scattering [4] algorithm accompanied with a shifting zoom procedure, that will make it possible to apply the inverse scattering approach to an electrically large domain [5].</p><p> </p><p><strong>Acknowledgments</strong></p><p>This work is supported by the project AMOR – Advanced Multimedia and Observation services for <br>the Rome cultural heritage ecosystem, financed within the call ESA 5G for L’ART (Business Applications programme).</p><p><strong>References</strong></p><p>[1] F. Gabellone, G. Leucci, N. Masini, R. Persico, G. Quarta, F. Grasso, “Nondestructive Prospecting and virtual reconstruction of the chapel of the Holy Spirit in Lecce, Italy”, Near Surface Geophysics, vol. 11, n. 2, pp. 231-238, April 2013.</p><p>[2] R. Persico, S. D'Amico, L. Matera, E. Colica, C. De, Giorgio, A. Alescio, C. Sammut and P. Galea, GPR Investigations at St John's Co‐Cathedral in Valletta. Near Surface Geophysics, vol. 17 n. 3, pp. 213-229. doi:10.1002/nsg.12046, 2019.</p><p>[3] G. Gennarelli, I. Catapano, F. Soldovieri, R. Persico, On the Achievable Imaging Performance in Full 3-D Linear Inverse Scattering, IEEE Trans. on Antennas and Propagation,  vol. 63, n. 3, pp. 1150-1155, March 2015.</p><p>[4] I. Catapano, G. Gennarelli, G. Ludeno and F. Soldovieri, "Applying Ground-Penetrating Radar and Microwave Tomography Data Processing in Cultural Heritage: State of the Art and Future Trends," in IEEE Signal Processing Magazine, vol. 36, no. 4, pp. 53-61, July 2019,.</p><p>[5] R. Persico, G. Ludeno, F. Soldovieri, A. De Coster, S. Lambot, 2D linear inversion of GPR data with a shifting zoom along the observation line, Remote Sensing, 9, 980; doi: 10.3390/rs9100980, open access, 2017.</p><div><br><div> <p> </p> </div> </div>

Salient object detection in a real-time environment demands high accuracy with less computation time. It is a changeling task to investigate a saliency model which improves saliency detection accuracy as well as reduces computation time. In this paper, we propose a hybrid model that improves detection accuracy with low computational time. The input image is simplified and clustered at multiple scales using SLIC and K-means clustering algorithms respectively. Gaussian Mixture Model (GMM) is developed on various color components of the digital image at multiple scales. The parameters of GMM are learnt using Expectation Maximization (EM) algorithm. The spatial variance of each color component is determined using GMM parameters and hence object position is estimated. Further, spatial variance of color components and object position is exploited to compute saliency map at a scale level. Afterwards, all the saliency maps generated across various scales are linearly combined to produce the final saliency map. The performance of the proposed model is compared in terms of Precision, Recall, F-Measure, Area under the Curve (AUC), Receiver Operating Characteristics (ROC) and Mean Absolute Error (MAE). Extensive experiments on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K show that the proposed model outperforms or comparable against 11 state-of-the-art methods of the last decade. The key features of the proposed method are object completeness and efficiency in terms of computational time.