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To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

This article used an improved version of the Flow Direction Algorithm (FDA) with acceleration coefficients named Modified Flow Direction Algorithm (MFDA) for simultaneous Optimal Network Reconfiguration (ONR) and Optimal Distribution Generation's (ODG) installation to reduce the Active Power Losses (APL), maximize the Voltage Profile (VP) and stability of the Distribution System (DS). FDA is motivated by the simulation of the direction of water flow into a drainage basin at a lower elevation area. The neighboring flow and its slope also affect the direction of the water flow; its slope was obtained using the D8 method. FDA has a low convergence rate, stuck with local optima. This article proposed acceleration coefficients to acquire the proper balance between exploration and exploitation, accelerate the global convergence rate, and avoid getting stuck with local optima. The efficiency of the MFDA is evaluated based on its performance on 16 standard benchmark functions and is also used to solve the simultaneous ONR and ODG installation. Two DSs consisting of 33 and 69 bus test systems are considered to solve the problem. There are three methodologies to reduce the APL and maximize the VP and stability: ONR, ODG, and simultaneous ONR and ODG installation. This article uses four case studies with different load levels to show the proposed method's reliability. The analysis shows a better way to minimize the power loss by solving simultaneous ONR and ODG installation rather than only optimizing reconfiguration or installation of DG. It has been discovered that the results achieved are superior to those produced using the ISCA, FWA, HSA, etc. So, the MFDA shown can be a potentially useful way to solve 16 standard benchmark functions and simultaneous ONR and ODG installation. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. [ABSTRACT FROM AUTHOR]

As the popularity of wireless networks deepens, the diversity of device types and hardware environments makes network data take on heterogeneous forms while the threat of malicious attacks from outside can prevent ordinary methods from mining information from abnormal data. In view of this, the research will be devoted to the feature processing of the anomalous data itself, and the convolutional operation of the anomalous information by the convolutional neural network (CNN). This is to extract the internal information. In the first step of the cascaded CNNs, the dimensions of the anomaly data will be processed, the anomaly data will be sorted under the concept of relevance grouping, and then the sorted results will be added to the convolution and pooling. The performance test uses three datasets with different feature capacities as the attack sources, and the results show a 13.22% improvement in information mining performance compared to the standard CNN. The extended CNN step will perform feature identification for homologous or similar network threats, with feature expansion within the convolutional layer first, and then pooling to reduce the computational cost. The test results show that when the maximum value domain of linear expansion is 2, the model has the best feature recognition performance, fluctuating around 85%; The model comparison test results show that the accuracy of the extended CNN is higher than that of the standard CNN, and the model stability is better than that of the back propagation (BP) neural network. This indicates that the cascaded CNN dual module can mine for the data itself, thus ignoring the risk unknowns, and this connected CNN has some practical significance. The proposed cascaded CNN module applies advanced neural network technology to identify internal and external risk data. The research content has important reference value for the security management of IoT systems. [ABSTRACT FROM AUTHOR]

Hera mission is the European Space Agency's contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation. • The Image Processing of Hera is investigated using Convolutional Neural Networks. • The centroid regression of both Didymos and Dimorphos is solved. • The range and the Sun phase angle are estimated using the pinhole camera model. • The subsolar point position on the surface of Didymos is estimated. [ABSTRACT FROM AUTHOR]

Object detection from underwater sea images based on deep learning techniques provides preferable results in a controlled environment. Yet, these techniques experience some challenges in detecting underwater objects due to color distortion, noise, and scattering. Hence, enhancing the underwater imaginary is important for accurately determining the objects under water. This research presents a deep learning approach for perceiving underwater objects from enhanced underwater images. Very Deep Super-Resolution Network (VDSR), which exhibits a higher visual quality, is utilized for improving the underwater image quality, thereby it is sufficient for object detection. Then, the object is detected from the enhanced underwater image through the proposed Border Collie Flamingo optimization-based deep CNN classifier (BCFO-based deep CNN). The developed BCFO-based algorithm is the main highlight of the research, which effectively tunes the classifier's hyperparameter. The evaluation is established using the UIEB and DUO datasets on the basis of performance standards, such as specificity, accuracy, and sensitivity. When the training percentage is 80 and the K -fold is 10, the suggested model achieved accuracy results of 93.89% and 95.24%, sensitivity results of 95.93 and 97.29%, and specificity results of 98.64% and 99%, which is very efficient compared to some existing approaches. [ABSTRACT FROM AUTHOR]

This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail to satisfy constraints while achieving optimal performance. To address this issue, we propose a multi-objective artificial bee colony optimization algorithm with a classical multi-objective theme called fitness sharing. This approach helps the convergence of the Pareto solution set towards a single optimal solution that satisfies multiple objectives. This article introduces multi-objective optimization with an example of a non-dominated sequencing technique and fitness sharing approach. The experimentation is carried out in MATLAB 2018a. In addition, we applied the proposed algorithm to two different real-time datasets, namely the knapsack problem and the nurse scheduling problem (NSP). The outcome of the proposed MBABC-NM algorithm is evaluated using standard performance indicators such as average distance, number of reference solutions (NRS), overall count of attained solutions (TNS), and overall non-dominated generation volume (ONGV). The results show that it outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Jack Edward Volder had introduced the CORDIC (COordinate Rotation DIgital Computer) in 1959. This year, critical development and advancement of the CORDIC algorithm have reached 62 years. The VLSI implementation of the CORDIC requires simple hardware (of the form add–shift), which makes CORDIC the most suitable building block for many real-time applications. The sequential nature of the CORDIC computation and scale factor are crucial aspects to be considered as they limit the overall performance of the algorithm. In this work, we have studied the various CORDIC algorithms and their architectures which improve the performance of the standard radix-2 CORDIC algorithm. This comparative study aims to provide first-order information on CORDIC algorithm implementations, including their potential applications. In addition, the study also reflected the works done by numerous researchers and highlighted the limitations of the existing architectures and algorithms, and executed the assessment using various parameters such as convergence range, number of iterations required to achieve the complete rotation, and hardware resource complexity to implement X / Y rotator, Z rotator, and scale factor compensation. We have also carried out error analysis of various CORDIC algorithms in terms of root-mean-squared error (RMSE) and peak signal-to-noise ratio (PSNR). The number of iterations, scale factor, and the sequential computing of the micro-rotations can be identified as significant improvement areas of the CORDIC algorithm and modifications are required for real-time applications. [ABSTRACT FROM AUTHOR]

This paper discusses a convex optimisation problem with a common set of constraints in the framework of multi-agent systems. Each agent only exchanges information with its neighbours and collaboratively searches for the optimal solution of the global function. To this addressed problem, a distributed multi-step subgradient projection algorithm is developed, where an adaptive event-triggering protocol is designed to govern the information exchange. It is disclosed that the state of each agent representing the estimate of the optimal solution asymptotically converges to one of the optimal solutions under suitably chosen stepsizes and momentum parameters. Simulation results verify that the proposed algorithm has better convergence performance than the standard event-triggered subgradient projection algorithm. In addition, the communication frequency between agents can be effectively reduced to save communication resource consumption. [ABSTRACT FROM AUTHOR]

Three-phase unbalanced system can be represented mathematically in terms of positive, zero, and negative sequence components. These components are really useful to provide information regarding power quality disturbances in an unbalanced system. Thus effective estimation of sequence components using appropriate signal processing models helps to detect short-duration disturbances like sag, swell, etc. The use of fractional-order calculus-based signal processing models has given a new dimension to estimate parameters and track non-stationary power quality events. The input matrix and weight vector are generated using trigonometric expansion considering the voltages across all the three phases. The magnitude and phase of the sequence components can be estimated from the optimal weight vector which is recursively updated using the proposed algorithm. In this study, the strength of fractional-order calculus is exploited using least mean square/fourth (LMS/F) to estimate sequence components in an unbalanced three-phase power system. The proposed fractional LMS/F (FLMS/F)-based model is tested using signals generated from MATLAB 2020 and IEEE 1159 PQE database. All the comparisons of results are made using standard performance measures, and also the convergence analysis of the algorithm is presented. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments. [ABSTRACT FROM AUTHOR]

A long range observing systems can be sturdily affected by scintillations. These scintillations are caused by changes in atmospheric conditions. In recent years, various turbulence mitigation approaches for turbulence mitigation have been exhibiting a promising nature. In this paper, we propose an effectual method to alleviate the effects of atmospheric distortion on observed images and video sequences. These sequences are mainly affected through floating air turbulence which can severely degrade the image quality. The existing algorithms primarily focus on the removal of turbulence and provides a solution only for static scenes, where there is no moving entity (real motion). As in the traditional SGL algorithm, the updated frame is iteratively used to correct the turbulence. This approach reduces the turbulence effect. However, it imposes some artifacts on the real motion that blurs the object. The proposed method is an alteration of the existing Sobolev Gradient and Laplacian (SGL) algorithm to eliminate turbulence. It eliminates the ghost artifact formed on moving object in the existing approach. The proposed method alleviates turbulence without harming the moving objects in the scene. The method is demonstrated on significantly distorted sequences provided by OTIS and compared with the SGL technique. The information conveyed in the scene becomes clearly visible through the method on exclusion of turbulence. The proposed approach is evaluated using standard performance measures such as MSE, PSNR and SSIM. The evaluation results depict that the proposed method outperforms the existing state-of-the-art approaches for all three standard performance measures. [ABSTRACT FROM AUTHOR]

As a novel Internet architecture focused on data contents, Named Data Networking (NDN) has been proven to be of great value in supporting the Internet of Things, Edge computing, Blockchain, and other popular topics. They can benefit mainly from NDN's intrinsic properties, such as flexible multicasting, in-network caching, among several others. However, once NDN's forwarding plane suffers Denial of Service (DoS) attacks, the overall system performance would be affected significantly. In NDN data transmission, interest forwarding is the most time-consuming operation and thus opens a possible vector of DoS attacks. It is proposed in this paper a fast name lookup algorithm for NDN interest forwarding, which selects feature prefixes instead of lengths to filter out interest packets in NDN interest forwarding. Due to the excellent filtration with feature prefixes, the algorithm accelerates NDN forwarding processes. Compared with other existing solutions, the proposed algorithm shows more than 70% of time improvement in forwarding malicious interests, while remaining at the same performance level in standard cases. [ABSTRACT FROM AUTHOR]

Purpose: To assess catheter reconstruction and error detection performance of an afterloader (Elekta Brachytherapy, Veenendaal, The Netherlands) equipped with electromagnetic (EM) tracking capabilities. Materials/Methods: The Flexitron research unit used was equipped with a special check cable integrating an EM sensor (NDI Aurora V3) that enables tracking and reconstruction capability. The reconstructions of a 24‐cm long catheter were performed using two methods: continuous fixed‐speed check cable backward stepping (at 1, 2.5, 5, 10, 25 and 50 cm/s) and stepping through each dwell position every 1 mm. The ability of the system to differentiate between two closely located (parallel) catheters was investigated by connecting catheters to the afterloader and moving it from its axis with an increment of 1 mm. A robotic arm (Meca500, Mecademic, Montreal) with an accuracy of 0.01 mm was used to move the catheter between each reconstruction. Reconstructions were obtained with a locally weighted scatterplot smoothing algorithm. To quantify the reconstruction accuracy, distances between two catheters were computed along the reconstruction track with a 5 mm step. The reconstructions of curve catheter paths were assessed through parallel and perpendicular phantom configuration to the EM field generator. Indexer length and lateral errors were simulated and a ROC analysis was made. Results: Using a 50 cm/s check cable speed does not allow for accurate reconstructions. A slower check cable speed results in better reconstruction performance and smaller standard deviations. At 1 cm/s, a catheter can be shifted laterally down to 1 mm and all paths can be uniquely identified. The optimum operating distance from the field generator (50 to 300 mm) resulted in a lower absolute mean deviation from the expected value (0.2 ± 0.1 mm) versus being positioned on the edge of the electromagnetic sensitive detection volume (0.6 ±0.3 mm). The reconstructions of curved catheters with a check cable speed under 5 cm/s gave a 0.8 mm ±0.3 mm error, or better. All indexer and lateral shifts of 1 mm were detected with a check cable speed of 2.5 cm/s or lower. Conclusions: The EM‐equipped Flexitron afterloader is able to track and reconstruct catheters with high accuracy. A speed under 5 cm/s is recommended for straight and curved catheter reconstructions. It allows catheter identification down to 1 mm inter‐catheter distance shift. The check cable can also be used to detect common shift errors. [ABSTRACT FROM AUTHOR]

Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in the proposed algorithm. For the said purpose, the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences, including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results. [ABSTRACT FROM AUTHOR]

PET scanners based on monolithic pieces of scintillator can potentially produce superior performance characteristics (high spatial resolution and detection sensitivity, for example) compared to conventional PET scanners. Consequently, we initiated development of a preclinical PET system based on a single 7.2 cm long annulus of LYSO, called AnnPET. While this system could facilitate creation of high-quality images, its unique geometry results in optics that can complicate estimation of event positioning in the detector. To address this challenge, we evaluated deep-residual convolutional neural networks (DR-CNN) to estimate the three-dimensional position of annihilation photon interactions. Monte Carlo simulations of the AnnPET scanner were used to replicate the physics, including optics, of the scanner. It was determined that a ten-layer-DR-CNN was most suited to application with AnnPET. The errors between known event positions, and those estimated by this network and those calculated with the commonly used center-of-mass algorithm (COM) were used to assess performance. The mean absolute errors (MAE) for the ten-layer-DR-CNN-based event positions were 0.54 mm, 0.42 mm and 0.45 mm along the x (axial)-, y (transaxial)- and z- (depth-of-interaction) axes, respectively. For COM estimates, the MAEs were 1.22 mm, 1.04 mm and 2.79 mm in the x-, y- and z-directions, respectively. Reconstruction of the network-estimated data with the 3D-FBP algorithm (5 mm source offset) yielded spatial resolutions (full-width-at-half-maximum (FWHM)) of 0.8 mm (radial), 0.7 mm (tangential) and 0.71 mm (axial). Reconstruction of the COM-derived data yielded spatial resolutions (FWHM) of 1.15 mm (radial), 0.96 mm (tangential) and 1.14 mm (axial). These findings demonstrated that use of a ten-layer-DR-CNN with a PET scanner based on a monolithic annulus of scintillator has the potential to produce excellent performance compared to standard analytical methods. [ABSTRACT FROM AUTHOR]

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy. [ABSTRACT FROM AUTHOR]

• A novel modified Jaya algorithm is developed to identify structural damage. • A multi-sample objective function embedding L 0.5 sparsity regularization is presented. • The coupling effects of three types of uncertainties are considered during the calculation. • Satisfactory identifications can be obtained by the proposed method. This study develops an improved Jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the Jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified Jaya algorithm is developed by integrating the one-step K-means clustering, Hooke–Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard Jaya algorithm, which is verified by a series of high-dimension test functions. An L 0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties. [ABSTRACT FROM AUTHOR]

• A discrete kernel estimator is developed to estimate unknown, time-varying target birth cardinality distribution. • Exponential weighted moving average is employed to address the time-varying characteristics of birth cardinality distribution. • An improved Gaussian Mixture CPHD filter with birth cardinality distribution and birth intensity estimated online is designed for multi-target tracking. Cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However, the performance of standard CPHD is degraded when the target birth process is unknown and dynamically changing. To remove this limitation, a discrete kernel estimator in conjunction with exponential weighted moving average scheme is introduced in this study to estimate the time-varying target birth cardinality distribution (i.e. , probability distribution on number of newborn targets appearing during one sampling time) at each processing step. The target birth intensity is then updated according to the resulting estimated birth cardinality distribution. The estimated birth intensity and cardinality distribution can be employed by a tracker based on Gaussian mixture CPHD (GMCPHD) to modulate its filtering strength for target tracking. The performance of the proposed framework is demonstrated by both numerical simulations and experiment on a video surveillance dataset. Results show that the proposed algorithm can reduce the delay of standard GMCPHD filter's response to the changes in the number of targets, and thus improve the accuracy of cardinality estimates. Satisfactory estimation of cardinality can be obtained even when there is a fast change in the rate of birth. [ABSTRACT FROM AUTHOR]

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity. [ABSTRACT FROM AUTHOR]