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A new adaptive frequency selective surface (AFSS) has been presented for electromagnetic (EM) shielding applications. Passive EM shields lack reconfigurability which severely limits their application. Moreover, active EM shields involving PIN diodes and other active elements consume power all the time. This paper incorporates a novel adaptive approach with frequency selective surface (FSS) to create a smart environment. The unit cell of the designed FSS has the shape of an octagon loop; each loop includes a pin diode at 180° intervals. An envelope detector has been used to trigger the microcontroller whenever an incident RF signal has been detected. The PIN diodes are switched ON or OFF automatically based on the requirements using a microcontroller to save power. When the PIN diodes are OFF, the FSS is in the shielding mode and when they are ON it is in the transmission mode. A prototype has been built for measurements. A good agreement between theory and experiment has been observed. The proposed AFSS can also be programmed as a time-based event and remote-controlled for on-demand EM wave shielding applications.

A rectangular dielectric resonator antenna (DRA) has been excited using a unique conformal H-shaped metal strip. Using this excitation, the degenerate mode pair of first higher orderTEδ13x and TE1δ3y has been excited for bandwidth improvement and high gain. A broadband circular polarization (CP) over a bandwidth of ~20% (3.67-4.4 GHz) in conjunction with a wide impedance-matching bandwidth of ~27.7% (3.67-4.73 GHz) has been achieved. A CP beamwidth of 89° has been offered by the antenna in Φ = 0° plane and ~32° in Φ = 90° plane. A high gain of ~6.8 dBic has been provided by the antenna, which is a significant improvement to those circularly polarized rectangular DRAs reported in the literature for similar applications. This broad CP bandwidth and beamwidth can be considerably beneficial for the worldwide interoperability for microwave access (WiMAX) and satellite applications. Furthermore, the proposed antenna has been fabricated to validate the simulated results. The measured results have been observed to agree well with the simulated results.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by single feeding mechanism has been studied theoretically and experimentally. The study has been carried out to find out the effect on different antenna parameters when a parasitic strip is attached to the wall beside the conformal metallic feed. The DRA is excited by an E-shaped metal strip. The novel feed produces a narrow impedance matching |S11|

In this paper, a novel wideband circularly polarized multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) has been proposed for worldwide interoperability for microwave access (WiMAX) applications. The mutual coupling (MC) between closely placed DRA units at 0.5λ has been significantly reduced by using a new hybrid technique. The exclusive features of the proposed MIMO antenna include wide impedance matching bandwidth (BW), broadband circular polarization (CP), and suppressed MC between the radiating elements. The first step of hybrid technique is the employment of parasitic patch at an optimized distance beside the conformal metal strip of the two identical rectangular DRAs in order to generate CP wave along with a wide impedance matching BW over the same frequency range. In the final step, the MC is suppressed significantly along with the broadband CP by placing the DRAs diagonally at an optimized position. The proposed MIMO antenna is the hybrid of the parasitic patch and the diagonal position of radiating elements. The CP BW offered by the proposed antenna is ~20.82% (3.58-4.40 GHz) in conjunction with an impedance-matching BW of ~38.51% (3.50-4.95 GHz). Moreover, MC less than -28 dB is achieved over the entire band and -26 dB at 3.89 GHz (minimum of the AR) through the hybrid technique. The prototype of the proposed antenna geometry is fabricated and measured. A good agreement has been attained between the simulated and measured results.

As air pollution, greenhouse gases, and global warming worsen, finding clean energy sources is critical. Renewable energy is a promising solution, especially in the transportation sector, which consumes significant energy. Hybrid electric vehicles (HEVs), combining an internal combustion engine and an electric battery, are key to reducing fossil fuel use and mitigating environmental harm. Effectively managing power distribution between these sources to enhance efficiency and minimize fuel consumption is crucial, known as an Energy Management Strategy (EMS). This article provides an overview of various EMS approaches for HEVs, analyzing their advantages and disadvantages. Rule-based strategies offer simplicity, optimization-based strategies provide superior performance, and advanced techniques like machine learning promise significant improvements. Current trends include integrating sophisticated sensors, data analytics, and artificial intelligence for real-time decision-making. Future directions aim at robust EMS frameworks integrating smart grid technologies and vehicle-to-everything (V2X) communication. The article reviews EMS methodologies, comparing their strengths and weaknesses, and discusses the main challenges and future trends in energy management for hybrid electric vehicles.

Mobile ad hoc networks (MANETs) face significant challenges in maintaining secure and efficient communication owing to their dynamic nature and vulnerability to security threats. Traditional routing protocols often struggle to adapt to rapidly changing topologies and potential malicious nodes, compromising network performance and security. This study addresses these challenges by proposing FLSTMT-LAR (Federated Learning Long Short-Term Memory Trust-aware Location-aided Routing), a novel framework that integrates multiobjective optimization with LSTM-based trust prediction for robust routing decisions, implements a decentralized federated learning mechanism for collaborative trust model updates while preserving node privacy, incorporates dynamic trust assessment using LSTM networks for accurate temporal behavior pattern analysis, and provides an adaptive routing decision mechanism that effectively balances multiple performance objectives including trustworthiness, energy efficiency, and network latency. We evaluate this framework against existing protocols across various scenarios, including different network densities, mobility patterns, and malicious node percentages. Results demonstrate FLSTMT-LAR’s superior performance in high-threat environments, achieving up to 80% packet delivery ratio compared with 45% for traditional approaches. In mobile scenarios, it shows improved adaptability, maintaining consistent performance as network density increases. MOO, particularly nondominated sorting genetic algorithm III, effectively balances conflicting network objectives, offering a 15% improvement in overall network performance compared with single-objective approaches. These findings highlight the potential of integrating advanced machine learning and optimization techniques in MANET routing protocols, paving the way for secure, efficient, and adaptive network communications in challenging environments.

Effective landslide representation from great spatial resolution images is significant in numerous applications. Many research works and techniques have been advertised. Still, these methods are very challenging to relate in real time since they depend on remotely sensing landslides from a solitary sensor with an exact spatial resolution. Precisely identifying landslides over a vast region with intricate background entities is difficult. Machine Learning (ML) and Deep Learning (DL) have attained extraordinary performance in classifying images utilizing remotely sensed images from numerous platforms. Moreover, techniques built within DL architectures tend to implement encoder-decoder network structures, where constant convolutions effortlessly strain out numerous landslide features. This study develops a Bidirectional Atrous Spatial Pyramid Pooling-Based Semantic Segmentation and Classification Model (BASPP-SSCM) technique for landslide Remote Sensing Images. The main goal of the BASPP-SSCM technique is to segment and classify the landslide areas. In the preprocessing stage, the BASPP-SSCM model employs an adaptive Wiener filtering (AWF) technique to eliminate the noise. Next, for the semantic segmentation method, the BASPP-SSCM technique utilizes the DeepLabV3 method with the backbone of the ConvNeXtLarge model for determining the landslide region. Furthermore, the CapsNet model is utilized for the feature extraction process. Besides, the Rigdelet neural network (RNN) technique is employed for the landslide classification process. At last, the pelican optimization algorithm (POA) methodology is implemented to fine-tune the parameters involved in the RNN model. A wide range of investigations is performed to highlight the superiority of the BASPP-SSCM method using a benchmark dataset. The performance validation of the BASPP-SSCM method underscored a superior accuracy value of 98.23% of other existing approaches.

With the widespread use of smartphones and wearable devices, Mobile Crowdsourcing (MCS) has become a powerful method for gathering and processing data from various users. MCS offers several advantages, including improved mobility, scalability, cost-effectiveness, and the utilization of collective human intelligence. However, ensuring the authenticity of users throughout the data collection process remains a challenge. Current authentication methods, such as traditional PIN codes, two-factor authentication, and biometric authentication, often struggle to provide continuous verification while adequately protecting user privacy. This paper addresses this issue by proposing a new continuous authentication approach based on Federated Learning. This approach combines continuous identity verification with privacy preservation benefits, allowing for the ongoing validation of user authenticity during data collection while improving authentication accuracy. We also discuss the non-Independently and Identically Distributed issue in Federated Learning and employ transfer learning techniques based on feature extraction to enhance the performance of the authentication models. We conducted extensive experiments using various datasets to evaluate the effectiveness of our proposed method. The results of this study demonstrate its potential to enhance the security and privacy of MCS systems.

Enhancing model performance in agricultural image analysis faces challenges due to limited datasets, biological variability, and uncontrolled environments. Deep learning models require large, realistic datasets, which are often difficult to obtain. Data augmentation, especially through Generative Adversarial Networks (GANs), has become essential in farming applications, generating synthetic images to improve model training and reduce the need for extensive image collection. This review explores various GAN approaches for image augmentation in farming, investigating their challenges and limitations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 128 publications were analyzed to identify research trends and gaps in GAN applications within the farming industry. Key applications of GANs include plant classification, weed detection, animal detection and behavior recognition, animal health and disease analysis, plant disease detection, phenotyping, and fruit quality assessment. Persistent issues like limited training datasets, occlusion challenges, and imbalanced data hinder model performance across these applications. Recognizing these challenges is critical for enhancing the efficiency and effectiveness of farming operations. Finally, this review concludes with insights and future directions to foster progress in this field.

The use of wireless connectivity in medical devices is increasingly widespread. 5G communication emerges as a promising wireless modality with its advancements in throughput and latency performance that can benefit existing and new use cases in healthcare applications. However, there are no standardized evaluation methods for assessing the impact of 5G communication failures on medical device functions. This gap has been acknowledged by industry stakeholders in both the medical device and telecommunication fields. To help address this issue, we propose in this paper a testing framework for evaluating 5G-enabled medical devices, which incorporates a comprehensive analysis of potential 5G failure modes along with a prioritization approach to focus network failure tests specifically on those most pertinent to the device under test. We outline the implementation of the testing framework using a graph database methodology and demonstrate representative test cases to validate the feasibility of the framework. The outcome is a flexible method that could be used by medical device developers, network operators, test lab engineers, and regulators for assessing the use of 5G connectivity in medical device functions.

Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system.