An Improved Chaos Driven Hybrid Differential Evolutionand Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements

Rosic, Maja; Sedak, Miloš; Simić, Mirjana; Pejović, Predrag

doi:10.3390/app13020684

Cited by 7 publications

(5 citation statements)

References 67 publications

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“…For enhancing the efficacy of the Inception v3 model, the CBO algorithm is used as a hyperparameter optimizer. BOA was a meta heuristic optimization algorithm that was derived from butterfly mating and butterfly foraging [20,21]. The mathematical representation of the BOA algorithm is based on three hypotheses (1) Each butterfly attracts the other by producing an odor fragrance.…”

Section: Feature Extraction Using Optimal Inception V3 Modelmentioning

confidence: 99%

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Thanarajan,

Alotaibi,

Rajendran

et al. 2023

Computers, Materials &Amp; Continua

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Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection. It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism. The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking. The Chaotic Butterfly Optimization technique is used to identify this specific disturbance. Therefore, this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning (ETASD-CBODL) technique. The presented ETASD-CBODL technique mainly focuses on the recognition of ASD via the ET and DL models. To accomplish this, the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS. In addition, the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization. Finally, the long-shortterm memory (LSTM) model is exploited for the recognition and classification of ASD. To assess the performance of the ETASD-CBODL technique, a series of simulations were performed on datasets from the figure-shared data repository. The experimental values of accuracy (99.29%), precision (98.78%), sensitivity (99.29%) and specificity (99.29%) showed a better performance in the ETASD-CBODL technique over recent approaches.

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Section: Feature Extraction Using Optimal Inception V3 Modelmentioning

confidence: 99%

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Thanarajan,

Alotaibi,

Rajendran

et al. 2023

Computers, Materials &Amp; Continua

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“…Subsequently, the second stage formulates a nonlinear optimization problem to determine the source position. Typical parameters for stationary source localization include Time Difference of Arrival (TDOA) [6][7][8][9], Time of Arrival (TOA) [10][11][12], Angle of Arrival (AOA) [13,14], and Received Signal Strength (RSS) [15,16]. Among them, TDOA stands out as one of the most popular choices.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Moving Source Localization with Time and Frequency Difference of Arrival: Motion-Assisted Method for Sub-Dimensional Sensor Networks

Yang

2024

Applied Sciences

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Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N+1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than 2N equations to solve for 2N unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than N+1, thereby corroborating the theoretical analysis.

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“…The results show that the algorithm combined with a logistic map has the best performance. In addition, the JAYA algorithm [15], differential evolution algorithm [16], particle swarm optimization algorithm [17], bird swarm algorithm [18], sunflower optimization algorithm [19], butterfly optimization algorithm [20], and other intelligent optimization algorithms are utilized to identify the parameters of nonlinear systems with chaotic behavior.…”

Section: Introductionmentioning

confidence: 99%

Research on Information Identification of Chaotic Map with Multi-Stability

Li,

Peng

2023

Fractal Fract

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Influenced by the rapid development of artificial intelligence, the identification of chaotic systems with intelligent optimization algorithms has received widespread attention in recent years. This paper focuses on the intelligent information identification of chaotic maps with multi-stability properties, and an improved sparrow search algorithm is proposed as the identification algorithm. Numerical simulations show that different initial values can lead to the same dynamic behavior, making it impossible to stably and accurately identify the initial values of multi-stability chaotic maps. An identification scheme without considering the initial values is proposed for solving this problem, and simulations demonstrate that the proposed method has the highest identification precision among seven existing intelligent algorithms and a certain degree of noise resistance. In addition, the above research reveals that chaotic systems with multi-stability may have more potential applications in fields such as secure communication.

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An Improved Chaos Driven Hybrid Differential Evolutionand Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements

Cited by 7 publications

References 67 publications

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Enhanced Moving Source Localization with Time and Frequency Difference of Arrival: Motion-Assisted Method for Sub-Dimensional Sensor Networks

Research on Information Identification of Chaotic Map with Multi-Stability

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