Road Side Units (RSUs) play a pivotal role in enhancing the safety of Connected Vehicles (CVs), yet their safety benefits hinge significantly on effective deployment strategies. Traditional approaches often focus on high-risk areas, but such locations may not necessarily yield the most substantial safety improvements. This study redefines hot spots as road segments where RSU deployment results in the greatest reduction of collision risk and introduces a cost-efficient method for identifying such locations. The proposed method requires only small-scale real-world driving data, which is further augmented to support broader scenario evaluation. Additionally, an accelerated sampling strategy is incorporated to enhance the efficiency of the identification process. Simulation-based evaluations demonstrate that the method achieves superior performance in terms of safety impact, data efficiency, compared to conventional approaches.

In this paper, we develop a novel receiver design for massive MIMO-OFDM systems in time-varying scenarios. Due to the Doppler shift effects, the received subcarriers measurements suffer from severely inter-carrier interference (ICI), which degrades the system performance. Motivated by this, we proposed a novel joint channel estimation and signal detection method with ICI suppression. By exploiting the channel sparsity in the delay-angle-doppler domain, the proposed method utilizes variational Expectation Maximization (EM) framework for iterative channel and signal updates. Concretely, in E-step, the turbo message passing algorithm is performed for calculating the mariginal posteriors. In M-step, the unknown parameters are updated. Numerical results show that this proposed receiver design outperforms the counterpart methods.

Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information, making target recognition extremely difficult. Most detection algorithms for camouflaged targets use only the target’s single-band information, resulting in low detection accuracy and a high missed detection rate. We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper. First, we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target. Second, a loss function is created, and the K -Means++ clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness. Finally, a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches. More crucially, we create a multispectral camouflage target dataset to test the suggested technique. Experimental results show that the proposed method has the best comprehensive detection performance, with a detection accuracy of 96.5%, a recognition probability of 92.5%, a parameter number increase of 1×10 4 , a theoretical calculation amount increase of 0.03 GFLOPs, and a comprehensive detection index of 0.85. The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.

The wheel-bipedal robot has the advantages of both wheeled robots and legged robots, but as a cost, it is more challenging to perform flexible movements in various surroundings while keeping it balanced. The inaccurate dynamics of the robot makes the balance problem even more intractable. To solve this problem, the robot Ollie is used as a testbed. The whole-body control (WBC) framework is adopted to enhance the dexterity of the robot with multiple degrees of freedom in the task space. Moreover, a learning-based adaptive technique is applied to assist the WBC such that the balance controller can be designed in the absence of the accurate dynamics. Physical experiments demonstrate that the robot can manage various actions, with the help of the combination of the WBC and the learning-based adaptive technique.

This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without moving backward and forward mightily. To take into account nonlinearities of the model and obtain a large domain of stability, a nonlinear controller based on the interconnection and damping assignment - passivity-based control (IDA-PBC) method is exploited to control the robot in more general scenarios. Physical experiments are performed with various control tasks. Experimental results demonstrate that the proposed linear output regulator can maintain the standing of the robot, while the proposed nonlinear controller can balance the robot under an initial starting angle far away from the equilibrium point, or under a changing robot height.

High-resolution range profile (HRRP) has obtained intensive attention in radar target recognition and convolutional neural networks (CNNs) are among predominant approaches to deal with HRRP recognition problems. However, most CNNs are designed by the rule-of-thumb and suffer from much more computational complexity. Aiming at enhancing the channels of one-dimensional CNN (1D-CNN) for extracting efficient structural information oftargets form HRRP and reducing the computation complexity, we propose a novel framework for HRRP-based target recognition based on 1D-CNN with channel attention and channel pruning. By introducing an aggregation-perception-recalibration (APR) block for channel attention to the 1D-CNN backbone, channels in each 1D convolutional layer can adaptively learn to recalibrate the extracted features for enhancing the structural information captured from HRRP. To avoid rule-of-thumb design and reduce the computation complexity of 1D-CNN, we proposed a new method incorporated withthe global best leading artificial bee colony (GBL-ABC) to prune the original network based on the lottery ticket hypothesis in an automatic and heuristic manner. The extensive experimental results on the measured data illustrate that the proposed algorithm achievesthe superiorrecognition rate by combing APR and GBL-ABC simultaneously.

In this paper, nonlinear control techniques are exploited to balance an unmanned bicycle with enlarged stability domain. We consider two cases. For the first case when the autonomous bicycle is balanced by the flywheel, the steering angle is set to zero, and the torque of the flywheel is used as the control input. The controller is designed based on the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) method. For the second case when the bicycle is balanced by the handlebar, the bicycle's velocity is high, and the flywheel is turned off. The angular velocity of the handlebar is used as the control input and the balance controller is designed based on feedback linearization. In these cases, the global stability of the closed-loop unmanned bicycle is theoretically proved based on Lyapunov theory. The experiments are conducted to validate the efficacy of the proposed nonlinear balance controllers.

In this paper, the gain scheduling technique is applied to design a balance controller for an autonomous bicycle with an inertia wheel. Previously, two different balance controllers are needed depending on whether the bicycle is stationary or dynamic. The switch between the two different controllers may cause the instability of the autonomous bicycle. Our proposed gain scheduled controller can balance the autonomous bicycle in both stationary and dynamic cases. A physical system is built and experiments are carried out to demonstrate the effectiveness of the gain scheduled controller.

In future wireless systems, increasing the downlink capacity becomes more important due to potential high data rate applications such as wireless internet access. Turbo codes and downlink transmit diversity are two of the most explored techniques to improve performance and capacity. In this paper, we evaluate the performance of turbo coded wideband code-division multiple-access (WCDMA) systems with downlink transmit diversity employing a space-time block code on correlated fading channels. The performance improvement using downlink space-time transmit diversity (STTD) is found to be more significant in the case of slow fading when the temporal correlation is high. By considering the spatial correlation between the transmit antennas at the base station, we quantify the tolerable correlation between transmit antennas by means of simulation for which the turbo coded WCDMA systems with transmit diversity still attain performance gains compared to a single transmit antenna system.

Combined spatial and temporal processing has been shown to increase the potential link capacity enormously for wireless communication systems, especially when the channels between different transmit and receive antenna pairs are uncorrelated. We consider both spatial and temporal channel correlations that may be encountered in space-time processing and present the performance analysis of convolutionally coded direct-sequence code-division multiple-access systems. An upper bound for the average bit-error probability (P~/sub b/) is derived for the case of perfect channel estimation, and an analytical approximation for P~/sub b/ is derived in the case of erroneous channel estimates. The analytical approach is general enough to be applicable to various space- and time-diversity situations, such as wideband multipath channels and antenna arrays.

Minimum duration outages have been introduced for lognormal shadow fading where the durations of signal fades were considered in evaluating outages. We develop and analyze such minimum duration outages for channels susceptible to Rayleigh fading. The results show that under typical Doppler frequencies the outages due to Rayleigh fading are more likely to cause frame or packet errors rather than call dropping due to the short time scales in effect.

We present a new method to analyze the performance of Reed-Solomon codes for hybrid automatic-repeat-request (ARQ) over Rayleigh fading channels with imperfect interleaving. Using the asymptotic results of level crossings of the faded envelope, we characterize the packet-error probability as a function of the Doppler frequency and mean signal-to-noise ratio. We then use the notion of equivalent Doppler frequency to include the effect of imperfect interleaving on performance. This approach allows us to directly evaluate the throughput of hybrid-ARQ schemes and also specify minimum interleaving depths for achieving optimum throughput on correlated Rayleigh channels.