Traffic sign recognition is an integral part of driver assistance systems play a crucial role in enhancing road safety. Due to a large number of challenging targets, such as occlusion, distortion, and small targets in...
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The Nakagami-Gamma(NG) shadow fading model based on the moment-based method(MoM) generates lower tail approximation, which is inaccuracy when the gamma random variables are replaced by the lognormal random variables. ...
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The Nakagami-Gamma(NG) shadow fading model based on the moment-based method(MoM) generates lower tail approximation, which is inaccuracy when the gamma random variables are replaced by the lognormal random variables. The channel parameters of composite NG shadow fading based on the method of minimizing the Kullback-Leibler(KL) divergence were estimated and a closed-form expression for the system bit error rate(BER) was derived in this paper. The simulation results show that the KL estimated parameters solve the lower tail approximation problem, and the replacement effect of the lognormal function by the gamma function is better than the MoM when the shading parameters are around the typical value of 4 dB-9 dB. Moreover, the KL method has a lower mean square error(MSE) value for the channel analysis.
Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the curre...
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Nanofluidic memristors,which use ions in electrolyte solutions as carriers,have been developed rapidly and brought new opportunities for the development of neuromorphic *** the transport and accumulation of ions in na...
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Nanofluidic memristors,which use ions in electrolyte solutions as carriers,have been developed rapidly and brought new opportunities for the development of neuromorphic *** the transport and accumulation of ions in nanochannels to process information is an endeavor to realize the nanofluidic *** this study,we report a new nanofluidic memristor,which is a polydimethylsiloxane(PDMS)-glass chip with two platinum(Pt)electrodes and well-aligned multi-nanochannels within PDMS for ion enrichment and *** device not only exhibits typical bipolar memristive behavior and ion current rectification(ICR)but also demonstrates excellent endurance,maintaining stable performance after 100 sweep *** systematically investigate the key factors affecting ion transport behavior in this *** results show that the ICR ratio of the current-voltage(I-V)hysteresis curves decreases with increasing scan rate and solution *** potential measurements are introduced to reveal that the PDMS surface carries more negative charges in higher pH solutions,resulting in more pronounced memristive and ICR ***,our memristor can simulate short-term synaptic plasticity,such as paired-pulse facilitation(PPF)and paired-pulse depression(PPD),with a relatively low energy consumption of 12 pJ per spike per ***,the inherent accessibility and robustness of our nanofluidic memristors facilitate the optimization of device structure and *** important observations and investigations lay a foundation for advancing energy-saving and efficient neuromorphic computing.
As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical cl...
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We introduce and study reconfiguration problems for (internally) vertex-disjoint shortest paths: Given two tuples of internally vertex-disjoint shortest paths for fixed terminal pairs in an unweighted graph, we are as...
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In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies ...
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Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its t...
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Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.
The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
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The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional meth...
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The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to ***, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users' reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support(such as biomedical image diagnostics), is computed at the client *** leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device's computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenario
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