Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojournin...
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Today’s working IT professionals frequently struggle with stress issues. The patient is now more likely to experience stress due to changing lifestyle and workplace cultures. Even while many companies and sectors del...
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Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge server...
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In this paper, we delve into the transformative landscape of education amidst the disruptive advances of generative AI (GenAI), characterized by an unprecedented capacity to generate new information with tools such as...
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As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promi...
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As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.
Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to t...
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Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparativel
Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things(IoT)*** the emergence of IoT...
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Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things(IoT)*** the emergence of IoT-based services,the industry of internet-based devices has *** number of these devices has raised from millions to billions,and it is expected to increase further in the near ***,additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user *** data aggregation models for Fog enabled IoT environ-ments possess high computational complexity and communication ***-fore,in order to resolve the issues and improve the lifetime of the network,this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer(HDAG-CBMO)*** HDAG-CBMO technique derives afitness function from many relational matrices,like residual energy,average distance to neighbors,and centroid degree of target ***,a chaotic theory based population initialization technique is derived for the optimal initial position of ***,a learning based data offloading method has been developed for reducing the response time to IoT user requests.A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.
Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memris...
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Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.
In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper...
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To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO)...
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