False Position Research Articles

A Hybrid Method for Individual Tree Detection in Broadleaf Forests Based on UAV-LiDAR Data and Multistage 3D Structure Analysis

Individual tree detection and segmentation in broadleaf forests have always been great challenges due to the overlapping crowns, irregular crown shapes, and multiple peaks in large crowns. Unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) is a powerful tool for acquiring high-density point clouds that can be used for both trunk detection and crown segmentation. A hybrid method that combines trunk detection and crown segmentation is proposed to detect individual trees in broadleaf forests based on UAV-LiDAR data. A trunk point distribution indicator-based approach is first applied to detect potential trunk positions. The treetops extracted from a canopy height model (CHM) and the crown segments obtained by applying a marker-controlled watershed segmentation to the CHM are used to identify potentially false trunk positions. Finally, the three-dimensional structures of trunks and branches are analyzed at each potentially false trunk position to distinguish between true and false trunk positions. The method was evaluated on three plots in subtropical urban broadleaf forests with varying proportions of evergreen trees. The F-score in three plots ranged from 0.723 to 0.829, which are higher values than the F-scores derived by a treetop detection method (0.518–0.588) and a point cloud-based individual tree segmentation method (0.479–0.514). The influences of the CHM resolution (0.25 and 0.1 m) and the data acquisition season (leaf-off and leaf-on) on the final individual tree detection result were also evaluated. The results indicated that using the CHM with a 0.25 m resolution resulted in under-segmentation of crowns and higher F-scores. The data acquisition season had a small influence on the individual tree detection result when using the hybrid method. The proposed hybrid method needs to specify parameters based on prior knowledge of the forest. In addition, the hybrid method was evaluated in small-scale urban broadleaf forests. Further research should evaluate the hybrid method in natural forests over large areas, which differ in forest structures compared to urban forests.

Comparison of the Number of Iterations of Roots of Non-Linear Equations in the Regula Falsi Method, Secant Method, and Bisection Method using Python

Comparison of the Number of Iterations of Roots of Non-Linear Equations in the Regula Falsi Method, Secant Method, and Bisection Method using Python

Time-efficient detection of false position attack in 5G and beyond vehicular networks

5G-V2X-enabled transportation systems rely on seamless cooperation between vehicles, infrastructure, and pedestrians, facilitated by the exchange of real-time position and state information among these entities. Misbehaving vehicles try injecting bogus messages into the network, thereby compromising its reliability and security. This work proposes an efficient Deep Learning (DL)-based Misbehavior Detection System (MDS) that leverages the use of Recurrent Neural Networks (RNNs) to analyze message consistency and detect bogus information in 5G-V2X networks. We highlight the significance of incorporating historical data to analyze consistency. Additionally, we emphasize the importance of evaluating the computational overhead induced by MDSs in V2X networks, given the critical need for low latency communication. We validate our work through both experimental and theoretical studies and compare it to existing works. The obtained results show the effectiveness of our work, achieving an accuracy of 95% in detecting false information injection attacks. Additionally, we guarantee a low required time/computing complexity of O(1), thereby avoiding significant overhead impacting the end-to-end latency when exchanging CAMs.

Modified Bracketing Iterative Method for Solving Nonlinear Equations

Non-linear equations, depicted as curves in numerous everyday situations, have long piqued the curiosity of researchers and engineers due to their frequent occurrence in practical problems. Despite attempts to tackle these equations both analytically and numerically, analytical methods often fall short when the equation's degree exceeds five, prompting the adoption of numerical approaches to yield approximate solutions. Consequently, this study places emphasis on segmenting intervals into smaller sub-intervals, with a particular focus on employing the Regula-Falsi method to integrate these segmented intervals, thereby enhancing its convergence rate. Furthermore, by utilizing the Regula-Falsi formula for interval segmentation, the number of iterations and computational time required are minimized. Additionally, the effectiveness of the proposed method is verified through numerical experiments involving various equation types, including algebraic, trigonometric, exponential, logarithmic, and transcendental equations, comparing the outcomes with established methods. The findings demonstrate that the proposed algorithm not only efficiently segments intervals but also enhances accuracy and reduces errors when these segmented intervals are utilized in conventional bracketing methods.

Machine learning non-isothermal study of the blade coating process (NIS-BCP) using non-Newtonian nanofluid with magnetohydrodynamic (MHD) and slip effects

The underlying investigation reports the effects of the magnetohydrodynamics (MHD) and non-linear slip coefficients on the coating thickness of the web/substrate during the blade coating process. Two dimensional analysis of the blade coating process is performed using non-Newtonian nanofluid model with the help of basic equations of fluid dynamics are performed. Firstly, the system of equations of motion (EOM) is transformed into the non-dimensional form by using the scaling factors. Secondly, the modeled equations are further simplified with the help of lubrication approximation theory (LAT). The resulting boundary value problem is transformed into stream function to eliminate the pressure gradient from the flow equation and then numerically tackled with the Matlab built-in function bvp4c with the method of false position (Regula-Falsi Method). A comparative study of numerical data with results simulated by artificial neural networks (ANN) found that results are in excellent agreement. The impact of sundry parameters on physical quantities is examined through graphical representation. Results indicate that the influence of magnetohydrodynamics (MHD), nanoparticle and slip effects cannot be ignored during the blade coating flow, as a significant impact of these parameters is observed on velocity, temperature, pressure, coating thickness, blade load, and streamlines.

Mathematical models for resolving the nonlinear formula for solar cell

<span>Accurate representation of a photovoltaic solar cell requires a comprehensive assessment of modeling factors that are unique to the individual device being studied. In the context of the single diode model, it is necessary to ascertain five distinct parameters, namely Rs, Rsh, Iph, Io, and n. In general, analytical or numerical methods may be used to calculate these values. In this paper, two alternative iterative approaches to solving nonlinear problems in solar cells without temperature are described and analyzed. The new iterative approach has several instances that have been quantitatively tested. This novel approach can be seen as a potential option for solving nonlinear equations. Additionally, a comparison between the suggested method, classic chord formula (CCM), and predictor-corrector type reveals that it is better and has the lowest evaluation. This is supported by an examination of accuracy and efficiency (as evaluated by function evaluations) false position method (FPM).</span>

AIS Data Manipulation in the Illicit Global Oil Trade

This article takes a close look at the landscape of global navigation satellite system (GNSS) spoofing. It is well known that automated identification system (AIS) spoofing can be used for electronic warfare to conceal military activities in sensitive sea areas; however, recent events suggest that there is a similar interest of spoofing AIS signals for commercial purposes. The shipping industry is currently experiencing an unprecedented period of deceptive practices by tanker operators seeking to evade sanctions. Last year’s announcement of a price cap on Russian crude oil and a new ban on Western companies insuring Russian cargoes is setting the stage for an increase in illegal activity. Our research team identified and documented the AIS position falsification by tankers transporting Russian crude oil in closed ship-to-ship (STS) oil transfers. The identification of the falsified positions is based on the repeated instances of discrepancies between AIS location suggestions and satellite radar imagery indications. Using the data methods at our disposal, we reconstructed the true movements of certain tankers and encountered some surprising behavior. These false ship positions make it clear that we need effective tools and strategies to ensure the reliability and robustness of AISs.

Research on Location Privacy Protection Technology in Wireless Sensor Networks Based on Big Data

The digital footprint in wireless sensor networks can bring great academic and commercial value, but it will also bring the risk of privacy disclosure to users. This study discusses the location privacy protection methods in location based service under multiple scenarios. In the experiment, a false location filtering algorithm for real-time location request scenarios and a false path generation model for offline location release scenarios are proposed. The false position filtering algorithm is implemented based on the idea of a large top heap selection query. The algorithm can construct false position candidate sets and filter false positions. The false track generation model combines the false position technology and the generative adversarial networks model, which mainly protects the user’s track data by synthesizing tracks. In the attacker’s recognition experiment of a real location. The minimum distance between the false locations generated by the algorithm proposed in the study is above 400 m and the generation time does not exceed 5 ms, generating a better set of false locations in terms of both effectiveness and efficiency. Compared with several commonly used privacy-preserving algorithms, the proposed algorithm has the lowest probability of being identified with real locations, with no more than 21% overall, and is almost independent of the k value. the recognition accuracy of the trajectory user link task decreases from over 90% to about 34%, indicating that the proposed fake trajectory generation model can effectively protect users’ data privacy. The experimental results demonstrate that the algorithm and model proposed in the study can quickly generate physically dispersed and semantically diverse sets of fake locations and effectively protect users’ trajectory privacy, which is important for users’ digital footprint privacy protection.

Computation of Volterra and Fredholm Integro-Differential Nonlinear Boundary Value Problems by a Modified Regula Falsi-Bisection Shooting Approach

A modified Regula Falsi shooting method approach is deployed to solve a set of Volterra and Fredholm integro- nonlinear boundary value problems. Efforts to solve this class of problems with traditional shooting methods have generally failed and the outcomes from most domain based approaches are often plagued by ill conditioning. Our modification is based on an exponential series technique embedded in a shooting bracketing method. For the purposes of validation, we initially solved problems with known closed form solutions before considering those that do not come with this property but are singular, genuinely nonlinear, and are of practical interest. Although in most of these tests, convergence was found to be super linear, the errors decreased monotonically after few iterations. This suggests that the method is robust and can be trusted to yield faithful results and by far surpasses in simplicity various other techniques that have been applied to solve similar problems. In order to buttress the effectiveness and utility of this approach, we display both the graphical and error analysis outcomes. And for each test case, the method can be seen to demonstrate the closeness of numerically generated results to the analytical solutions.

Statistical Study of Bisection Method for Cubic Equations with Random Coefficients

The bisection method is an iterative approach used in numerical analysis to find solutions to nonlinear equations. The main purpose of this paper is to study how the parameters of a probability distribution characterizing the coefficients of a cubic polynomial can influence the convergence of the bisection method. The study covers discrete and continuous distributions, including discrete uniform, continuous uniform, and normal distributions. It was found that for both types of uniform distribution inputs, a second-degree polynomial equation can predict the average iteration for a given parameter r, where r indicates the distribution interval [−r, r]. Interestingly, the coefficients of the second-degree polynomial are nearly identical for discrete and continuous uniform distributions. For normal distribution input, the average iteration does not depend upon the standard deviation when the mean is fixed and the standard deviation is varying. But when the standard deviation is fixed and the mean is varying, the second-degree polynomial is still the best fit. This means the average iteration depends upon the mean of the normal distribution. Overall, our paper concludes that: I. For uniform distribution input, the average iteration does not depend on whether the distribution is discrete or continuous but rather depends on the range of the distribution which is its parameter. II. For non-uniform distribution input, the average iteration depends on the mean of the distribution (location parameter) but not on the standard deviation (scale parameter). Finally, a curtain is raised in the future direction of research in which we propose to combine the bisection method with the regula falsi and Newton-Raphson methods to increase the rate of convergence.

A superlinear Scaling Factor Regula Falsi root finder that detects the simple or multiple character of the root

In this work a new method of Scaling Factor Regula Falsi type is developed, by using parabolic interpolation. It has global convergence and a high computational efficiency for simple roots. The method, not being a hybrid one, allows changing the function used for calculating the scaling factor at every iteration, making possible to switch to other more adequate methods for multiple roots (e.g., a generalised Illinois one). As an important feature of the method, it allows determining with no additional calculations, whether the root is simple or multiple. The developed algorithm has been tested with numerous functions extracted from the bibliography, performing, in most cases, better than some routines (like brentq, brenth and toms748) found in common numerical libraries. Fully operative routines of the new method are provided as supplementary files in four different programming languages: python, lua, C and Fortran90.

ON NEW HYBRID ROOT-FINDING ALGORITHMS FOR SOLVING TRANSCENDENTAL EQUATIONS USING EXPONENTIAL AND HALLEY'S METHODS

The objective of this paper is to propose two new hybrid root finding algorithms for solving transcendental equations. The proposed algorithms are based on the well-known root finding methods namely the Halley's method, regula-falsi method and exponential method. We show using numerical examples that the proposed algorithms converge faster than other related methods. The first hybrid algorithm consists of regula-falsi method and exponential method (RF-EXP). In the second hybrid algorithm, we use regula falsi method and Halley's method (RF-Halley). Several numerical examples are presented to illustrate the proposed algorithms, and comparison of these algorithms with other existing methods are presented to show the efficiency and accuracy. The implementation of the proposed algorithms is presented in Microsoft Excel (MS Excel) and the mathematical software tool Maple.

Newton’s forward interpolation method for solving nonlinear algebraic equation

As we know, the interpolation is one of the most basic and most useful numerical techniques in Mathematics. Newton’s forward interpolation method is one of most important of these methods. Its most important task in numerical analysis to find roots of nonlinear equations, several methods already exist to find roots. But in this paper, we introduce the interpolation technique for this purpose. The proposed method derived from the newton forward interpolation method and we compared the results with another existing method (Bisection Method (BM), Regula-Falsi Method (RFM), Secant Method (SM), Newton Raphson Method (NRM)) and the method proposed by J. Sanaullah (SJM). It’s observed that the proposed method has fast convergence but it has same order of convergence of the method (SJM). Maple software is used to solve problems by different methods.

Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1–L2 Norm and Total Variation

Infrared dim small target detection has received a lot of attention, because it is a crucial component of the IR search and track systems (IRST). The robust principal component analysis (RPCA) is a common detection framework, which works with poor performance with complex background edges and sparse clutters due to the inappropriate approximation of sparse items. A nonconvex constraint detection method based on the difference between the L1 and L2 (L1–L2) norm and total variation (TV) is presented. The L1–L2 norm is a more accurate sparse item approximation of L0 norm, which can achieve a better description of the sparse item to separate the target from the complex backgrounds. Then, the total variation norm is conducted on the target image to suppress the sparse clutters. The new model is solved using the alternating direction method of multipliers (ADMM) method. Then, the subproblems in the model are tackled by the difference of convex algorithm (DCA) and the Newton conjugate gradient (Newton-CG) solving L1–L2 norm and TV norm, respectively. In the experiment, we conducted experiments on multiple and single target datasets, and the proposed model outperforms the state-of-the-art (SOTA) methods in terms of background suppression and robustness to accurately detect the target. It can achieve a higher true position rate (TPR) with a low false position rate (FPR).

Electromagnetic Metasurface with Object Information and Position Illusion in Radar Vision at Microwave Frequencies

AbstractCamouflage skills are important for understanding the relationship between predators and prey in ecological systems. Mimicry is a common strategy used by creatures to alter their appearance in order to blend in with their surroundings and avoid detection. This paper unveils a previously unknown camouflage strategy that creates false object information and positions of an object in the visual range of radar sensor vision. A dispersion‐modulated illusionary metasurface to realize focal spot coding in a 3‐dimensional space to fit the radar imaging process is proposed. As the radar image indicates, an aircraft image is created by a flat metasurface plane with good polarization adaptation ability. Design, simulation, and experimental results validate the study. This camouflage strategy indicates the complexity of the food chain and can be used by both predators and prey, providing accessible insights for maintaining biological diversity. The metasurface‐based system can be potentially utilized in future radar markers for marine traffic control systems.

Bisection Method and Falsi Regulation Method to Determine The Roots of Polynomial Equations

Some simple polynomial equations can be solved by the remainder theorem, so there is no need for numerical methods to solve them, because the roots of equations are very easy to do using analytical methods, while there are some polynomial equations that are difficult and complex to find roots using analytical methods.
 In this literature review, researchers will use the bisection method and the false rule to find the roots of polynomial equations. Based on the steps or sequence of calculation of the polynomial roots of , using the bisection method, the author states that from the first step to the eleventh step, if the calculation continues then in the second step f(a)*f(c)>0 or away from zero as shown in table 1 above. The author states that if the twelfth step continues, then f(a)*f(c) will approach zero and it can be seen that there are looping process approaches resulting from f(a)*f(c). This research study concludes that the roots of the polynomial of , using the bisection method are 1.36474675. Based on the steps or sequence of calculating the roots of the polynomial of on, using the false position method (false rule), the author states that from the first step to the 366th step it turns out that f(c)=0.003195 when c=1,365423447. Thus the polynomial roots of using the false position method (regulation false) are 1.365423447.
 Keywords: Roots of polynomial equations.

GNSS Spoofer Localization for Vehicles Based on Doppler and Clock Drift Double Difference

Global navigation satellite systems (GNSS) have been widely used in our daily lives. Meanwhile, GNSS are facing a severe threat of spoofing attacks, which can mislead users by false position, velocity and time (PVT) solutions, resulting in serious consequences. Especially, with the development of vehicle navigation, autonomous driving and unmanned aerial vehicles (UAV), increasing spoofing attacks have emerged towards vehicles, where anti-spoofing techniques are desperately needed. Among these techniques, spoofer localization is a necessary step to fundamentally address the spoofing attacks. Most of the conventional spoofer localization methods are based on time of arrival (TOA) or time difference of arrival (TDOA). These methods require measurements observed by multiple receivers at the same time, where time synchronization is indispensable. However, it is hard to apply these methods to the existing vehicles equipped with independent commercial receivers. To cope with this problem, in this paper we develop a flexible method for vehicles to locate a stationary spoofer based on Doppler and clock drift double difference (DCDD). DCDD is calculated using Doppler measurements and clock drift solutions of one vehicular receiver and another reference receiver. Generally, Doppler frequency of GNSS signals changes slowly with time, which releases the restriction for high-precision time synchronization. DCDD represents the Doppler frequency caused by the vehicle motion, from which the spoofer position can be estimated. In addition, with the elimination of irrelevant components in Doppler measurements, DCDD minimizes the user complexity required for localization, where at least two receivers and one vehicle is needed. Utilizing DCDD, an iterative weighted least squares (WLS) algorithm is used to solve the spoofer position, and a closed-form initial value is given to improve the convergence rate. We evaluate the localization performance by the derived Cramér-Rao lower bound (CRLB) in the presence of vehicle position and velocity errors. Simulations and field vehicle experiments are conducted to verify the feasibility and effective of the proposed method. The experimental results show that with an average vehicle velocity of 8 m/s, the horizontal localization error can be less than 40 m and 20 m, using DCDD observations of 4 epochs and 8 epochs respectively.

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection.

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters' initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets-CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

Modified Anderson-Bjork’s method for solving non-linear equations in structural mechanics

One of the greatest problems in numerical methods is to provide high speed and stability at the same time. In this paper, we propose a new hybrid root finding algorithm, based on the appropriate combination of bisection, false position and the Anderson-Bjork’s method. In the general case, the achieved convergence is compatible to the fastest available methods, while stable performance in the worst case is provided. A comparative analysis versus popular root finding algorithms is performed as well. For that purpose, the respective procedures are implemented in the C# programming language. The proposed algorithm is included into the Calcpad engineering calculations platform.

Convergence of the Method of False Position

Convergence of the Method of False Position