With the rapid development of artificial intelligence and the Internet of Things,along with the growing demand for privacy-preserving transmission,the need for efficient and secure communication systems has become inc...
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With the rapid development of artificial intelligence and the Internet of Things,along with the growing demand for privacy-preserving transmission,the need for efficient and secure communication systems has become increasingly *** communication methods transmit data at the bit level without considering its semantic significance,leading to redundant transmission overhead and reduced *** communication addresses this issue by extracting and transmitting only the mostmeaningful semantic information,thereby improving bandwidth ***,despite reducing the volume of data,it remains vulnerable to privacy risks,as semantic features may still expose sensitive *** address this,we propose an entropy-bottleneck-based privacy protection mechanism for semantic *** approach uses semantic segmentation to partition images into regions of interest(ROI)and regions of non-interest(RONI)based on the receiver’s needs,enabling differentiated semantic *** focusing transmission on ROIs,bandwidth usage is optimized,and non-essential data is *** entropy bottleneck model probabilistically encodes the semantic information into a compact bit stream,reducing correlation between the transmitted content and the original data,thus enhancing privacy *** proposed framework is systematically evaluated in terms of compression efficiency,semantic fidelity,and privacy *** comparative experiments with traditional and state-of-the-art methods,we demonstrate that the approach significantly reduces data transmission,maintains the quality of semantically important regions,and ensures robust privacy protection.
Using unmanned aerial vehicles(UAVs) for data collection has emerged as a promising technique to achieve both time-and energy-efficient data gathering while keeping data fresh. In this study, two schemes are proposed ...
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Using unmanned aerial vehicles(UAVs) for data collection has emerged as a promising technique to achieve both time-and energy-efficient data gathering while keeping data fresh. In this study, two schemes are proposed for flight cycle minimization and energy efficiency maximization to collect data from ground sensors. We first minimize the flight cycle by jointly optimizing the wake-up scheduling of sensors, the trajectory,and the time slot, which is a mixed-integer non-convex problem and difficult to solve directly. To this end,we propose an iterative algorithm based on block coordinate descent and successive convex approximation to decouple the original non-convex problem into two sub-problems and the constraints are turned to be convex approximately. Furthermore, the energy efficiency is maximized since the limited energy is a critical issue in UAV communication systems. We approximate the two subproblems as convex optimizations by introducing slack variables and applying SCA. The approximate energy efficiency is a fractional expression, and we use Dinkelbach's method to solve it. Numerical results show that the flight cycle is minimized in the first scheme with the data requirement satisfied, while in the second scheme, the energy efficiency is maximized with the trade-off between the transmission data and the propulsion power consumption.
In extremely low signal-to-noise ratio (SNR) region, the useful features of the signal are weakened by higher-power noise, making it difficult for conventional direction-of-arrival (DOA) estimation methods to adequate...
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Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimi...
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Dear Editor,This letter presents a multi-automated guided vehicles(AGV) routing planning method based on deep reinforcement learning(DRL)and recurrent neural network(RNN), specifically utilizing proximal policy optimization(PPO) and long short-term memory(LSTM).
The phenomenal rise in network traffic across various sectors, driven by advancements in network communication, has led to an explosion of connected devices. While internet-based service providers have enhanced smart ...
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Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It i...
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Coronavirus belongs to the family of Coronaviridae. It is responsible for COVID-19 communicable disease, which has affected 213 countries and territories worldwide. Researchers in computational fields have been active...
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Machine learning (ML) with data analysis has many successful applications and is widely employed daily. Additionally, they have played a significant role in combating the global coronavirus (COVID-19) outbreak. Intern...
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The discriminative correlation filter (DCF) is commonly utilized in UAV tracking because of its high tracking accuracy and computing speed. However, in aerial tracking scenarios, challenges such as target occlusion an...
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The discriminative correlation filter (DCF) is commonly utilized in UAV tracking because of its high tracking accuracy and computing speed. However, in aerial tracking scenarios, challenges such as target occlusion and similar object interference are likely to cause the predicted object position to deviate from the correct motion trajectory. To alleviate this issue, this paper proposes a correlation filter algorithm based on trajectory correction and context interference suppression for real-time aerial tracking. First, a tracking quality evaluation metric is proposed to determine the confidence of the current tracking results. When the object is in a low confidence status, the state matrices of the object position and velocity are constructed, and the Kalman filter strategy is utilized to correct the tracking trajectory automatically. In addition, temporal context-response regularization is designed to fully exploit previous temporal information in order to suppress background interference. Extensive experimental results on four mainstream datasets demonstrate that the proposed algorithm has high tracking performance while achieving a real-time tracking speed of 32 fps on a single CPU. IEEE
Proposing a performance testing methodology for vehicle collision warning algorithm at intersections, this approach addresses the issues of the key V2X technology being challenging to simulate with real-scene data in ...
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