In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communicati...
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In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communication through open networks is insecure and has many vulnerabilities, making it susceptible to unauthorized access and misuse. Encryption models are used to secure medical data from unauthorized access. In this work, we propose a bit-level encryption model having three phases: preprocessing, confusion, and diffusion. This model is designed for different types of medical data including patient information, clinical data, medical signals, and images of different modalities. Also, the proposed model is effectively implemented for grayscale and color images with varying aspect ratios. Preprocessing has been applied based on the type of medical data. A random permutation has been used to scramble the data values to remove the correlation, and multilevel chaotic maps are fused with the cyclic redundancy check method. A circular shift is used in the diffusion phase to increase randomness and security, providing protection against potential attacks. The CRC method is further used at the receiver side for error detection. The performance efficiency of the proposed encryption model is proved in terms of histogram analysis, information entropy, correlation analysis, signal-to-noise ratio, peak signal-to-noise ratio, number of pixels changing rate, and unified average changing intensity. The proposed bit-level encryption model therefore achieves information entropy values ranging from 7.9669 to 8.000, which is close to the desired value of 8. Correlation coefficient values of the encrypted data approach to zero or are negative, indicating minimal correlation in encrypted data. Resistance against differential attacks is demonstrated by NPCR and UACI values exceeding 0.9960 and 0.3340, respectively. The key space of the proposed model is 1096, which is substantially mor
Fruit safety is a critical component of the global economy, particularly within the agricultural sector. There has been a recent surge in the incidence of diseases affecting fruits, leading to economic setbacks in agr...
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In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to tr...
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The formation of 2D lateral heterostructures in rippled MoS2 and similar transition metal dichalcogenides (TMDs) is studied using density functional theory. Compression of rippled TMDs beyond a threshold compression l...
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The formation of 2D lateral heterostructures in rippled MoS2 and similar transition metal dichalcogenides (TMDs) is studied using density functional theory. Compression of rippled TMDs beyond a threshold compression leads to the formation of a flat valence band associated with strongly localized holes. The implications for exciton manipulation and the emergence of one-dimensional heavy fermion behavior are discussed.
A key component of behavior analysis and human-computer interaction (HCI) is facial expression detection, which helps systems understand and react to human emotions more effectively and nuancedly. While previous resea...
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Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficie...
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Thermal imaging has become a vital tool for analyzing temperature variations in various fields, including medical diagnostics, industrial inspection, and environmental monitoring. However, the application of homograph...
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The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic...
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The growing integration of Distributed Energy Resources (DERs) into modern power grids, managed via DER Management Systems (DERMS), has introduced significant cybersecurity challenges. Communication vulnerabilities in...
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In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as secondary users (SUs) to opportunistically utili...
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In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as secondary users (SUs) to opportunistically utilize detected "spectrum holes". Our overall framework consists of three main stages. Firstly, in the model training stage, we explore dataset generation in a multi-cell environment and train a machine learning (ML) model using the federated learning (FL) architecture. Unlike the existing studies on FL for wireless that presume datasets are readily available for training, we propose an end-to-end architecture that directly integrates wireless dataset generation, which involves capturing I/Q samples from over-the-air signals in a multi-cell environment, into the FL training process. To this purpose, we propose a multi-label classification problem for wideband spectrum sensing to detect multiple spectrum holes simultaneously based on the I/Q samples collected locally by the UAVs. In the traditional FL that employs federated averaging (FedAvg) as the aggregating method, each UAV is assigned an equal weight during model aggregation. However, due to the differences in wireless channels observed at each UAV in a multi-cell environment, the received signal powers and collected datasets at different UAV locations could be significantly different, which could degrade the FL performance using equal weights. To address this issue, we propose a proportional weighted federated averaging method (pwFedAvg) in which the aggregating weights are proportional to the received signal powers at each UAV, thereby integrating the intrinsic properties of wireless channels into the FL algorithm. Secondly, in the collaborative spectrum inference stage, we propose a collaborative spectrum fusion strategy that is compatible with the unmanned aircraft system traffic management (UTM) ecosystem. In particular, we improve the accuracy of spectrum sensing results by combining the multi-lab
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