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.
Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency *** the devices need to operate at very high frequency and ultra-wide bandwidth:They consume more energy,dissipate more powe...
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Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency *** the devices need to operate at very high frequency and ultra-wide bandwidth:They consume more energy,dissipate more power,and subsequently heat up *** overheating is a common concern of many users,and millimeter-wave would exacerbate the *** this work,we first thermally characterize millimeter-wave *** measurements reveal that after only 10 s of data transfer at 1.9 Gbps bit-rate,the millimeter-wave antenna temperature reaches 68◦C;it reduces the link throughput by 21%,increases the standard deviation of throughput by 6×,and takes 130 s to dissipate the heat *** degrading the user experience,exposure to high device temperature also creates *** on the measurement insights,we propose Aquilo,a temperature-aware,multi-antenna network *** maintains relatively high throughput performance but cools down the devices *** testbed experiments under both static and mobile conditions demonstrate that Aquilo achieves a median peak temperature only 0.5◦C to 2◦C above the optimal while sacrificing less than 10%of throughput.
In supervised learning algorithms, the class imbalance problem often leads to generating results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal as...
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A common problem on the streets of Dhaka, the capital of a developing country Bangladesh, is the reduction in the effective road width and blockage of roads due to various side friction elements. Despite significant e...
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A common problem on the streets of Dhaka, the capital of a developing country Bangladesh, is the reduction in the effective road width and blockage of roads due to various side friction elements. Despite significant efforts to study the effects of side friction in different contexts, a comprehensive study that efficiently models side friction elements and then analyzes their impacts using a contextually appropriate simulator for heterogeneous non-lane-based traffic in cities like Dhaka is yet to be conducted in the literature. Therefore, in this study, we attempt to fill this gap. Here, based on real data analysis, we list side friction elements that exhibit substantial impacts on the regular traffic flow on the streets of Dhaka, which includes different categories of roadside objects, pedestrians, and non-motorized vehicles. Subsequently, we model the side friction elements using parameters that effectively capture their characteristics. Leveraging the formulated models, we extend our microscopic road traffic simulator, DhakaSim, which has been proven to appropriately simulate the heterogeneous non-lane-based traffic in cities like Dhaka. To the best of our knowledge, the updated simulator is the only simulator capable of simulating heterogeneous traffic of cities like Dhaka while capturing its real inherent complexities, such as a large number of parked vehicles and irregular pedestrian movement. We rigorously validate the updated simulator using Google Maps data and utilize it to conduct an extensive simulation study analyzing the impacts of side friction elements. Our study reveals that the average speed of Dhaka can be reduced by up to 36.55% and the average waiting time can be increased by up to 161.3% due to side friction. Here, pedestrians exhibit a significantly more negative impact on traffic compared to other side friction elements, making them one of the leading causes of congestion on the streets of Dhaka. Moreover, while non-motorized traffic reduces t
In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending o...
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In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas
Today, machine learning is used in a broad variety of applications. Convolution neural networks (CNN), in particular, are widely used to analyze visual data. The fashion industry is catching up to the growing usage of...
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Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optim...
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The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemi...
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Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to det...
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