Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks ...Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios(SNRs)and non-Gaussian impulsive noise,where observations may be distorted differently across feature modalities,making conventional fusion unstable and degrading detection reliability.To address this challenge,the generalized Gaussian distribution(GGD)is adopted as the noise model,and a multimodal fusion framework termed BCAM-Net(bidirectional cross-attention multimodal network)is proposed.BCAM-Net adopts a parallel dual-branch architecture:a time-frequency branch that leverages the continuous wavelet transform(CWT)to extract time-frequency representations,and a temporal branch that learns long-range dependencies from raw signals.BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features,generating a fused representation that is highly robust to complex noise.Simulation results show that,under GGD noise with shape parameterβ=0.5,BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines.At a false alarm probability Pf=0.1 and SNR of−14 dB,it attains a detection probability of 0.9020,exceeding the CNN-Transformer,WT-ResNet,TFCFN,and conventional CNN benchmarks by 5.75%,6.98%,33.3%,and 21.1%,respectively.These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios,and provides a lightweight,high-performance solution for practical cognitive radio spectrum sensing.展开更多
Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles.An internal combustion engine(ICE)driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the eng...Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles.An internal combustion engine(ICE)driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required.This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling.First,an optimization problem is formulated to minimize the fuel consumption of a vehicle with free-wheeling when the traveling distance,the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy.The speed profile of the vehicle,engine operating point,and engine on/off timing are obtained as the results of the optimization.The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile.The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage,adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model.The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions.The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling.展开更多
基金supported in part by JSPS Grants-in-Aid for Scientific Research 25K07742 and 25K23457.
文摘Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access(DSA)and a key approach to alleviating spectrum scarcity in the Internet of Things(IoT).The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios(SNRs)and non-Gaussian impulsive noise,where observations may be distorted differently across feature modalities,making conventional fusion unstable and degrading detection reliability.To address this challenge,the generalized Gaussian distribution(GGD)is adopted as the noise model,and a multimodal fusion framework termed BCAM-Net(bidirectional cross-attention multimodal network)is proposed.BCAM-Net adopts a parallel dual-branch architecture:a time-frequency branch that leverages the continuous wavelet transform(CWT)to extract time-frequency representations,and a temporal branch that learns long-range dependencies from raw signals.BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features,generating a fused representation that is highly robust to complex noise.Simulation results show that,under GGD noise with shape parameterβ=0.5,BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines.At a false alarm probability Pf=0.1 and SNR of−14 dB,it attains a detection probability of 0.9020,exceeding the CNN-Transformer,WT-ResNet,TFCFN,and conventional CNN benchmarks by 5.75%,6.98%,33.3%,and 21.1%,respectively.These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios,and provides a lightweight,high-performance solution for practical cognitive radio spectrum sensing.
文摘Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles.An internal combustion engine(ICE)driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required.This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling.First,an optimization problem is formulated to minimize the fuel consumption of a vehicle with free-wheeling when the traveling distance,the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy.The speed profile of the vehicle,engine operating point,and engine on/off timing are obtained as the results of the optimization.The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile.The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage,adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model.The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions.The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling.
