To improve the diversity and performance of the Mayfly Algorithm (MA), this letter adopts the mutation strategies in the process of MA. The opposition-based learning (OBL) and Cauchy mutation strategies are used to mutate the global optimal solution, and the artificial mutation operator is used in the offspring population. The hybrid mutation strategies are used in a cascaded structure. The performance of the proposed algorithms is demonstrated in simulations comparatively.

This paper highlights the impact of curved and flat vehicular plastic parts on the radiation characteristics of two dual-band antennas for C-V2X applications. The radiation patterns of the antennas are measured in SATIMO near field measurement system and are compared during the following setups: (a) antennas alone in the near field system, without the presence of a plastic part; (b) antennas mounted on the inside curved surface of a driver’s side mirror cover; (c) antennas mounted on the outside curved surface of the driver’s side mirror cover; (d) antennas mounted on a flat trunk lid; (e) antennas mounted on a curved plastic retrieved from the A-pillar of a vehicle. Comparison among the antennas radiation pattern measurements during these different setups, results in the conclusion that the inside surface of the side mirror cover is the most suitable position to mount the presented dual-band antennas. The curvature of the inside surface at the point where the antenna was mounted is less steep than the placement point at the outside surface, allowing the antenna to keep its polarization axis mostly unaffected. Moreover, the curve of the inside surface makes the antenna radiation more directional, creating an increase in the antenna gain. The side mirror cover, compared to trunk lid, is further from the ground protecting the antenna radiation from additional reflections.

Pavement distress classification is a vital step for automatic pavement inspection and maintenance. Recently, patch-based approaches have achieved promising performances and thus extensive attention in this field. However, these methods simply assume that all patches contribute equally to the distress classification, leading to weakly discriminating abilities of models. Moreover, their tedious processes also leads to a low efficiency in inference. In this letter, we present a novel patch-based pavement distress classification approach named Deep Patch Soft Selective Learning (DPS$^2$L), which addresses these issues. Similar to other patch-based approaches, DPS$^2$L partitions the pavement images into patches and aggregates the patch features to accomplish the task. To address the first issue, we introduce a succinct Soft Patch Feature Selection Network (SPFSN) to assess the importance of each patch to the distress classification with a score based on its feature. These scores will be considered as patch-wise weights for feature aggregation. In such a manner, the most discriminative patches are selected in a soft way, and thereby benefit the final classification. To address the inference efficiency issue, knowledge distillation is leveraged to transfer the classification knowledge from DPS$^2$L to the image-based approaches, such as EfficientNet-B3. This distilled model enables incorporating both the advantages of patch-based approaches in classification performance and the advantages of image-based approaches in inference efficiency. Extensive experiments on a large-scale pavement image dataset named CQU-BPDD demonstrates the superiority of our methods over baselines regardless of performance or efficiency.

Presented is an improved bandgap reference, which has the performance of high accuracy and can generate the required voltage reference. In this bandgap reference, the improved base current compensation is proposed to eliminate the effect of the base current. Meanwhile, a high reference voltage generator is used to provide configurable output voltages of 1.2/1.8/2.5/3.3V needed by DC-DC converters. The bandgap reference is realized in a standard 180nm CMOS process with an area of 0.05 mm×mm. Among the 5 sample chips of the reference, in the temperature range of -40 oC to 125 oC, the temperature coefficients of all the reference voltages range from 3 ppm/oC to 38 ppm/oC. The best average value of temperature coefficients is 6.03 ppm/oC when the reference voltage is 2.5V. The best line sensitivities (LS) is 0.23%/V when the reference voltage is 1.8V with the power consumption of 150μW@VDD=5V;

It is a challenging issue how to improve the accuracy of image matching in computer vision. To address this issue, an image matching method is proposed, which is via progressive priors of a putative dataset. Distance ratio priors of a putative dataset are initially employed to calculate a tentative deformation through geometric constraints. Progressive priors of the putative dataset, obtained by the tentative deformation, are then engaged to improve the accuracy of image matching by estimating a global deformation. The comparison experiments illustrate that our proposed method more effectively enhances the accuracy of image matching than six state-of-the-art methods.

In the field of compressed sensing, $\ell_{1-2}$-minimization model can recover the sparse signal well. In dealing with the $\ell_{1-2}$-minimization problem, most of the existing literatures use the DCA algorithm to solve the unrestricted $\ell_{1-2}$-minimization model, i.e. model $(\ref{my1})$. Although experiments have proved that the unrestricted $\ell_{1-2}$-minimization model can recover the original sparse signal, the theoretical proof has not been established yet. This paper mainly proves theoretically that the unrestricted $\ell_{1-2}$-minimization model can recover the sparse signal well, and makes an experimental study on the parameter $\lambda$ in the unrestricted minimization model. The experimental results show that increasing the size of parameter $\lambda$ in model $(\ref{my1})$ appropriately can improve the recovery success rate. However, when $\lambda$ is sufficiently large, increasing $\lambda$ will not increase the recovery success rate.

We have demonstrated waveguide integration of terahertz quantum cascade lasers (THz QCLs) at frequencies above 4.7 THz. A precision micromachining technique, followed by diamond-turning and electroless-plating has been used to manufacture hollow rectangular waveguides with integrated diagonal feedhorns. We show that surface roughness at the 1μm level is achieved, enabling outcoupling of radiation in the 4.75–5.05 THz band, with a divergence angle of 5° along the plane of the QCL substrate.

One of the major bottlenecks of the traditional Current Transformer Energy Harvester (CTEH) is the instable output induced by the wide-range variations of the current in transmission lines. In this work, a novel CTEH capable of generating a stable output is demonstrated by using a core fabricated with saturable magnetic material. The stable output of the CTEH is enabled by the constant voltage-time product in its saturable characteristic. The proposed CTEH is implemented with a resistive load representing the load of electronic devices. When the current in the primary side of the CTEH’s increases from 1A to 1000A, the maximum power on the load can reach about 0.5W, demonstrating the feasibility of using the CTEH with the saturable magnetic core as a quasi-stable power supply.

This letter focuses on the improvement in the error correction performance of the extended min-sum (EMS) decoding algorithm. Introducing factor/offset modifications in the check node update process of the EMS algorithm can improve the performance. Based on this, we propose a novel threshold-based modified EMS algorithm that introduces the factor/offset in a targeted manner by setting a threshold for the posterior probability. Simulation results show that an additional performance gain of the order of 0.08 dB is obtained with the proposed method without significantly increasing the method’s complexity.

The paper assesses the feasibility of forming a composite excitation pulse with a high potential to combat the noise and onset ambiguity when estimating the target resonance behaviour in a radar target signal. The assessment investigates four composite pulse configurations of unified or adaptive setups for the fractional bandwidth and peak weight to find the best setup in enhancing the resonance signature robustness. The assessment uses the method pencil function to extract the resonance parameters of the composite time data (coherent) and then determine the degree of robustness over-extraction onset and range of noise level. Determining the robustness rate requires finding the error between the original excitation frequencies and the extractable resonant frequencies and, second, the similarity between the original and reconstructed pulse waveforms. The qualitative assessments of the robustness merit concluded that the adaptive configuration of peak weight and small adaptive fractional bandwidth outperforms the other configurations in enhancing the resonance signature robustness.

Liben Building, constructed in 1822 AD, is located in Xinnan Village, Hukeng Town, Yongding District, Longyan City, Fujian Province, China. This square earthen building covers an area of about 2,100 square meters. In 1931 AD, the building was sacked and burned down by bandits during a war, leaving behind only the remnants of the walls of the main building. In the meta-universe environment, firstly, this study adopted blockchain DAO technology to conduct a study on the digital collection, storage, processing, display and dissemination of Liben Building, revealing the problems with digital regeneration of cultural heritage. Then, the questionnaire with 20 questions was designed, and 158 valid completed copies of the questionnaire were collected. Combining influence relationship with sample clustering analysis methods, this paper explored the findings of a study of the historical sites under blockchain digital heritage preservation and protection in the metaverse.

Edge wave detection is a key technology in steel plate quality inspection, which is beneficial to improve the production quality of steel products and the efficiency of enterprises. In this letter, a novel algorithm based on visual projection and pixel statistics is proposed to detect the edge wave using 3D camera. The proposed algorithm firstly uses a series of image preprocessing to effectively remove the influence of irrelevant interference, and transforms the colour image into the binary image. Subsequently, three key positions of the edge wave in range image are precisely determined based on visual projection and pixel statistics. Finally, two 3D cameras are used to acquire the range images to evaluate the proposed algorithm's performance. The results show that the proposed algorithm can accurately detect the edge wave instead of human eye detection.

Light field (LF) enables high-dimensional image data representation since it can capture spatial and angular information of light rays simultaneously. The low spatial resolution caused by the limited imaging ability of the capturing equipment and the trade-off between spatial and angular resolution greatly affects the quality and application of LF images. In this letter, we propose an end-to-end LF super-resolution (SR) method via geometric feature interaction. Firstly, the low-resolution LF images are stacked in the horizontal and vertical epipolar plane image (EPI) directions and form 3D VI stacks. Then, these stacks are put into a dual-branch network, and we alternately perform 3D convolution on the viewpoint images (VIs) and EPIs by reshaping features for better feature extraction and interaction. The proposed method can fully explore the texture information and geometric consistency of the LF, and super-resolve all VIs at the same time. Experimental results on both real-world and synthetic LF datasets show that the proposed method has higher performance than other state-of-the-art methods.

A compact high-sensitivity probe for sensing up tangential E-field is proposed and measured in this paper. The probe is consisted of an electric dipole with patches used to induce the tangential E-field, a resonator made up of open circuit stubs (capacitor) and short circuit stubs (inductor), for achieving a specific resonance, and a compact coupled balun for transforming the differential- mode voltage into the single output voltage and impedance matching. The probe is fabricated on five-layer dielectric lamina. Compared with the referenced broadband probe, the measured |S12|of the probe is enhanced by 18.81 dB. The proposed probe in this paper is validated with simulation and measurement.

Environmental perception system is the premise of the safety and stability of the autonomous vehicle system. However, studies have shown that the on-board sensors included in the perception system are extremely vulnerable to external attacks and interference, leading to incorrect driving strategies and bringing great security threats. Aiming at the problem, this paper divides the vehicle-mounted sensors into a positioning group and an identification group according to their role in the perception system. Then, based on the information correlation between sensors in the same group and the information correlation of a single sensor on adjacent time series, the distance metric model between sensors in a group and the distance metric model for each sensor of this group on time series is established. And the normal distance intervals corresponding to the confidence interval are calculated respectively. According to the distance metric model between sensors, we can detect anomalies in the perception system in real-time. Further, according to the distance metric model for each sensor on adjacent time series, we can identify anomaly sensors. Our experimental results quantitatively show that the method achieves real-time anomaly recognition, and demonstrate the effectiveness and robustness of the method on the open-source KITTI dataset.

Synaptic crosstalk, which characterizes the interaction between synapses when the neighboring neurons are activated at the same time, plays an important role in the transmission of neural signals. To discover the effect of synaptic crosstalk on associative memory, a memristor-based associative memory circuit considering synaptic crosstalk is proposed in this letter. The inhibitory effect of negative crosstalk on associate memory in the initial learning stage and the consolidation influence of positive crosstalk on associate memory when the synaptic weight exceeds the critical value are revealed. Pspice simulations are conducted on the resultant circuit to verify its correctness.

This study applied the World Health Organization (WHO) guidelines to redesign the Portable Health Clinic (PHC), as a Remote Healthcare System (RHS), for the spread of COVID-19 containment. Additionally, the proposed system not only collects people data but also classifies the case according to the the main symptoms of coronavirus using the COVID-19 triage process (CT-process) based on the analysis of measurement readings taken from patients, where drones are used as a PHC platform and are equipped with the required sensors and essential COVID-19 medications for testing and treating people at their doorstep autonomously when a full curfew is imposed.

Optical and acoustic stereo imaging has great potential for the precise and consistent localization of intervention underwater robots; however, it is still being explored due to its sensing limitations and various technical challenges. This study presents a novel localization method by combining an inertial navigation system and an optical and acoustic stereo imaging system. As a strategy for localization correction relative to underwater structures, the robot’s pose is estimated based on a single acoustic image using a sonar simulator for mid-range localization, and a robust visual tracking using a 3-D wireframe model is employed for high-precision localization near the target structures. The performance of the proposed technique was demonstrated through experimental validation using real data obtained from a test tank.