RESUMO
Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions. This paper presents a novel approach to employing RNNs to increase the accuracy of underwater target localization by exploiting the temporal dynamics of proximity-informed data. This method uses an RNN architecture to track changes in audio emissions from underwater targets sensed by a microphone network. Using the temporal correlations represented in the data, the RNN learns patterns indicative of target localization quickly and correctly. Furthermore, the addition of proximity-based features increases the model's ability to understand the relative distances between hydrophone nodes and the target, resulting in more accurate localization estimates. To evaluate the suggested methodology, thorough simulations and practical experiments were carried out in a variety of underwater environments. The results show that the RNN-based strategy beats conventional methods and works effectively even in difficult settings. The utility of the proximity-aware RNN model is demonstrated, in particular, by considerable reductions in the mean estimate error (MEE), an important performance measure.
RESUMO
Microstrip antennas usually suffer from high losses, gain, and efficiency degradation. It is a challenging task to miniaturize the patch antenna without degrading the performance parameters. To mitigate the above problems, a microstrip patch antenna loaded with stubs and printed on the ground plane loaded with dumbbell meta-atom is presented in this paper. The proposed double dumbbell meta-atom consists of two complementary split ring resonator (CSRR) cells loaded with rectangular rings. This exhibits the Double Negative (DNG) characteristic at 2.45 GHz. The devised meta-atom possesses dimensions of 0.05λ x 0.03λ at lower giga-hertz range. The meta-atom is further analyzed in CST-Microwave Studio and the corresponding S-parameters are extracted in MATLAB using the Nicolson Ross Weir (NRW) method. The electrical model of the meta-atom is also analyzed using Agilent ADS simulator. Further, two models of the proposed antenna with FR-4 and RT/Duroid-5880 are designed and compared. The proposed patch antenna resonates at three different frequency bands i.e. 2.445 GHz with a 3-dB bandwidth of 110 MHz (2.4 GHz-2.51 GHz), at 5.85 GHz with a 3-dB bandwidth of 730 MHz (5.13 GHz-5.86 GHz), and at 8.83 GHz with a 3-dB bandwidth of 1.83 GHz (7.7 GHz-9.53 GHz). This exhibit peak gains of 2.75dBi, 3.53dBc and 4.36dBi with low cross polarization levels at the said frequencies of operation. Further, the antenna possesses circular polarization in the frequency band (5.15 GHz-5.63 GHz). This antenna is used for Wi-Fi, ISM and X-band communications. The designed prototype is fabricated and tested and bears resemblance to the simulated results.
RESUMO
Management of vehicle traffic is a challenging task as it is non-deterministic by nature. Vehicular Cyber-Physical Systems (VCPS) is the emerging field of dynamics of vehicle management. Vehicle localization is considered an important task in VCPS. Many researchers proposed methodologies for this based on the Global Positioning System (GPS) which poses few location identification errors. Also, there are more vulnerabilities to the existing vehicular positioning system due to Zig-Zag attacks and bad-mouth attacks. In this work, an error-free and secure environment for communication between dynamically moving vehicle models has been proposed. In our proposed model a localization technique based on mathematical geometry which is capable of GPS outages and encompasses the dynamism of vehicle and on-road trajectory has been developed. The proposed model includes Extended Kalman filter-based routing to predict the neighbouring vehicle position. To avoid vulnerabilities created by the malicious nodes, a trust-based computation is performed by each node on its neighbours perceiving the authenticity of received messages. To validate the methodology, NS2 tool has been used to simulate the VCPS and to test the efficiency with different scenarios such as erroneous location, GPS outage, and malicious attack. The result shows that the proposed approach is more optimal and secure than the existing methodologies.
