The performance consistency of an RF MEMS switch matrix is a crucial metric that directly impacts its operational lifespan. An improved crossbar-based RF MEMS switch matrix topology, SR-Crossbar, was investigated in this article. An optimized port configuration scheme was proposed for the RF MEMS switch matrix. Both the utilization probability of individual switch nodes and the path lengths in the switch matrix achieve their best consistency simultaneously under the proposed port configuration scheme. One significant advantage of this scheme lies in that it only adjusts the positions of the input and output ports, with the topology and individual switch nodes kept unchanged. This grants it a high level of generality and feasibility and also introduces an additional degree of freedom for optimizations. In this article, a universal utilization probability function of single nodes was constructed and an optimization objective function for the SR-Crossbar RF MEMS switch matrix was formulated, which provide a convenient approach to directly solving the optimized port configuration scheme for practical applications. Simulations to demonstrate the optimized dynamic and static consistencies were conducted. For an 8×8 SR-Crossbar switch matrix, the standard deviations of contact resistances of 128 units and losses of all 64 paths decreased from 1.00 and 0.42 to 0.51 and 0.23, respectively. These results aligned closely with theoretical calculations derived from the proposed model. Full article

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively. Full article

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. Full article

The built environment’s impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people’s travel experience and travel mode choice. The streets’ built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device’s power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications. Full article

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists. Full article

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. Full article

The rotational speed standard device that can carry loads is the key device for calibrating passive rotational speed sensors. The rotor of the passive rotational speed sensor is connected to the rotor of the standard speed device through a coupling, and the standard reference speed is provided by the standard device. Due to the rotor eccentricity, the unbalanced force of the rotor occurs, and it can not only affect the rotational speed accuracy but can also damage the mechanical bearings of the standard speed device. To solve this issue, a method for suppressing the unbalanced force of the speed standard device based on an active magnetic bearing (AMB) force compensation system is proposed. First, the overall structure of the system is briefly introduced. Then, the force feedback control system model with the AMB as the force actuator is established, and a PI controller is designed to achieve the disturbed force control. Finally, a semi-physical simulation experimental platform is built to verify the effectiveness of the proposed method. The experimental results show that the AMB force compensation system can reduce 84.4%, 81.6%, and 79.8% of the unbalanced vibration force at the frequency of 30 Hz, 90 Hz, and 150 Hz, respectively. Full article

Hyper-spectral imaging (HSI) systems can be divided into two main types as follows: a group of systems that includes a dedicated dispersion/filtering component whose role is to physically separate the different wavelengths and a group of systems that sample all wavelengths in parallel, so that the separation into wavelengths is performed by signal processing (interferometric method). There is a significant advantage to systems of the second type in terms of the integration time required to obtain a signal with a high signal-to-noise ratio since the signal-to-noise ratio of methods based on scanning interferometry (Windowing method) is better compared to methods based on dispersion. The current research deals with the feasibility study of a new concept for an HSI system that is based on scanning interferometry using the “push-broom” method. In this study, we investigated the viability of incorporating a simple birefringent plate into a scanning optical system. By exploiting the motion of the platform on which the system is mounted, we extracted the spectral information of the scanned region. This approach combines the benefits of scanning interferometry with the simplicity of the setup. According to the theory, a chirped cosine-shaped interferogram is obtained for each wavelength due to the nonlinear behavior of the optical path difference of light in the birefringent plate as a function of the angle. An algorithm converts the signal from a superposition of chirped cosine signals to a scaled interferogram such that Fourier transforming (FT) the interferogram retrieves the spectral information. This innovative idea can turn a simple monochrome camera into a hyperspectral camera by adding a relief lens and a birefringent plate. Full article

The progress in markerless technologies is providing clinicians with tools to shorten the time of assessment rapidly, but raises questions about the potential trade-off in accuracy compared to traditional marker-based systems. This study evaluated the OpenCap system against a traditional marker-based system—Vicon. Our focus was on its performance in capturing walking both toward and away from two iPhone cameras in the same setting, which allowed capturing the Timed Up and Go (TUG) test. The performance of the OpenCap system was compared to that of a standard marker-based system by comparing spatial-temporal and kinematic parameters in 10 participants. The study focused on identifying potential discrepancies in accuracy and comparing results using correlation analysis. Case examples further explored our results. The OpenCap system demonstrated good accuracy in spatial-temporal parameters but faced challenges in accurately capturing kinematic parameters, especially in the walking direction facing away from the cameras. Notably, the two walking directions observed significant differences in pelvic obliquity, hip abduction, and ankle flexion. Our findings suggest areas for improvement in markerless technologies, highlighting their potential in clinical settings. Full article

Highly selective etching of silicon nitride (Si₃N₄) and silicon dioxide (SiO₂) has received considerable attention from the semiconductor community owing to its precise patterning and cost efficiency. We investigated the etching selectivity of Si₃N₄ and SiO₂ in an NF₃/O₂ radio-frequency glow discharge. The etch rate linearly depended on the source and bias powers, whereas the etch selectivity was affected by the power and ratio of the gas mixture. We found that the selectivity can be controlled by lowering the power with a suitable gas ratio, which affects the surface reaction during the etching process. X-ray photoelectron spectroscopy of the Si₃N₄ and QMS measurements support the effect of surface reaction on the selectivity change by surface oxidation and nitrogen reduction with the increasing flow of O₂. We suggest that the creation of SiO_xN_y bonds on the surface by NO oxidation is the key mechanism to change the etch selectivity of Si₃N₄ over SiO₂. Full article

Despite recent notable advancements in highlight image restoration techniques, the dearth of annotated data and the lightweight deployment of highlight removal networks pose significant impediments to further advancements in the field. In this paper, to the best of our knowledge, we first propose a semi-supervised learning paradigm for highlight removal, merging the fusion version of a teacher–student model and a generative adversarial network, featuring a lightweight network architecture. Initially, we establish a dependable repository to house optimal predictions as pseudo ground truth through empirical analyses guided by the most reliable No-Reference Image Quality Assessment (NR-IQA) method. This method serves to assess rigorously the quality of model predictions. Subsequently, addressing concerns regarding confirmation bias, we integrate contrastive regularization into the framework to curtail the risk of overfitting on inaccurate labels. Finally, we introduce a comprehensive feature aggregation module and an extensive attention mechanism within the generative network, considering a balance between network performance and computational efficiency. Our experimental evaluations encompass comprehensive assessments on both full-reference and non-reference highlight benchmarks. The results demonstrate conclusively the substantive quantitative and qualitative enhancements achieved by our proposed algorithm in comparison to state-of-the-art methodologies. Full article

Autonomous driving, as a pivotal technology in modern transportation, is progressively transforming the modalities of human mobility. In this domain, vehicle detection is a significant research direction that involves the intersection of multiple disciplines, including sensor technology and computer vision. In recent years, many excellent vehicle detection methods have been reported, but few studies have focused on summarizing and analyzing these algorithms. This work provides a comprehensive review of existing vehicle detection algorithms and discusses their practical applications in the field of autonomous driving. First, we provide a brief description of the tasks, evaluation metrics, and datasets for vehicle detection. Second, more than 200 classical and latest vehicle detection algorithms are summarized in detail, including those based on machine vision, LiDAR, millimeter-wave radar, and sensor fusion. Finally, this article discusses the strengths and limitations of different algorithms and sensors, and proposes future trends. Full article

The advancements in deep learning have significantly enhanced the capability of image generation models to produce images aligned with human intentions. However, training and adapting these models to new data and tasks remain challenging because of their complexity and the risk of catastrophic forgetting. This study proposes a method for addressing these challenges involving the application of class-replacement techniques within a continual learning framework. This method utilizes selective amnesia (SA) to efficiently replace existing classes with new ones while retaining crucial information. This approach improves the model’s adaptability to evolving data environments while preventing the loss of past information. We conducted a detailed evaluation of class-replacement techniques, examining their impact on the “class incremental learning” performance of models and exploring their applicability in various scenarios. The experimental results demonstrated that our proposed method could enhance the learning efficiency and long-term performance of image generation models. This study broadens the application scope of image generation technology and supports the continual improvement and adaptability of corresponding models. Full article

Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research. Full article

Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or buildings to measure electricity usage and allow power suppliers or consumers to monitor and manage it in real time. However, SMs require a secure service to address malicious attacks during memory protection and communication processes and a lightweight communication protocol suitable for devices with computational and communication constraints. This paper proposes an authentication protocol based on a one-way hash function to address these issues. This protocol includes message authentication functions to address message tampering and uses a changing encryption key for secure communication during each transmission. The security and performance analysis of this protocol shows that it can address existing attacks and provides 105,281.67% better computational efficiency than previous methods. Full article

This work introduces a generalized version of the pulse width modulation (PWM) switch model applied in the small-signal modeling of converters based on the multistate switching cell (MSSC) operating in discontinuous conduction mode (DCM). It consists of extending the concept formerly introduced by Vorperian for the representation of multiphase converters in DCM, yielding a circuit-based approach that does not rely on matrix manipulations unlike state-space averaging (SSA). The derived dc and ac models are valid for any number of switching states and any operating region defined in terms of the duty cycle, thus allowing for determining the voltage gain and distinct transfer functions. A thorough discussion of results is presented to demonstrate the applicability of the derived models to represent distinct configurations of the MSSC. Full article

With the rising incidence of various diseases in China and the constant development of the pharmaceutical industry, there is a growing demand for floxacin-type antibiotics. Due to the large-scale production and high cost of waste treatment, the parent drug and its metabolites constantly enter the water environment through domestic sewage, production wastewater, and other pathways. In recent years, the pollution of the aquatic environment by floxacin has become increasingly serious, making the technology to degrade floxacin in the aquatic environment a research hotspot in the field of environmental science. Metal–organic frameworks (MOFs), as a new type of porous material, have attracted much attention in recent years. In this paper, four photocatalytic materials, MIL-53(Fe), NH₂-MIL-53(Fe), MIL-100(Fe), and g-C₃N₄, were synthesised and applied to the study of the removal of ofloxacin and enrofloxacin. Among them, the MIL-100(Fe) material exhibited the best photocatalytic effect. The degradation efficiency of ofloxacin reached 95.1% after 3 h under visible light, while enrofloxacin was basically completely degraded. The effects of different materials on the visible photocatalytic degradation of the floxacin were investigated. Furthermore, the photocatalytic mechanism of enrofloxacin and ofloxacin was revealed by the use of three trappers (®O₂⁻, h⁺, and ®OH), demonstrating that the role of ®O₂⁻ promoted the degradation effect of the materials under photocatalysis. Full article

Polyhydroxyalkanoates (PHAs) are intracellular biopolymers that microorganisms use for energy and carbon storage. They are mechanically similar to petrochemical plastics when chemically extracted, but are completely biodegradable. While they have potential as a replacement for petrochemical plastics, their high production cost using traditional carbon sources remains a significant challenge. One potential solution is to modify heterotrophic PHA-producing strains to utilize alternative carbon sources. An alternative approach is to utilize methylotrophic or autotrophic strains. This article provides an overview of bacterial strains employed for PHA production, with a particular focus on those exhibiting the highest PHA content in dry cell mass. The strains are organized according to their carbon source utilization, encompassing autotrophy (utilizing CO₂, CO) and methylotrophy (utilizing reduced single-carbon substrates) to heterotrophy (utilizing more traditional and alternative substrates). Full article

Breast cancer is associated with high mortality and morbidity rates. As about 20–30% of patients exhibiting ER-positive phenotype are resistant to hormonal treatment with the standard drug tamoxifen, finding new therapies is a necessity. Postbiotics, metabolites, and macromolecules isolated from probiotic bacteria cultures have been proven to have sufficient bioactivity to exert prohealth and anticancer effects, making them viable adjunctive agents for the treatment of various neoplasms, including breast cancer. In the current study, postbiotics derived from L. plantarum and L. rhamnosus cultures were assessed on an in vitro breast cancer model as potential adjunctive agents to therapy utilizing tamoxifen and a candidate aziridine–hydrazide hydrazone derivative drug. Cell viability and cell death processes, including apoptosis, were analyzed for neoplastic MCF-7 cells treated with postbiotics and synthetic compounds. Cell cycle progression and proliferation were analyzed by PI-based flow cytometry and Ki-67 immunostaining. Postbiotics decreased viability and triggered apoptosis in MCF-7, modestly affecting the cell cycle and showing a lack of negative impact on normal cell viability. Moreover, they enhanced the cytotoxic effect of tamoxifen and the new candidate drug toward MCF-7, accelerating apoptosis and the inhibition of proliferation. This illustrates postbiotics’ potential as natural adjunctive agents supporting anticancer therapy based on synthetic drugs. Full article

In this paper, Cu thin films were deposited on Si (100) substrates by the high−power impulse magnetron sputtering (HiIPMS) technique, and the effects of different duty cycles (from 2.25% to 5.25%) on the plasma discharge characteristics, microstructure, and electrical properties of Cu thin films were investigated. The results of the target current test show that the peak target current remains stable under 2.25% and 3% duty cycle conditions. Under the conditions of a 4.5% and 5.25% duty cycle, the target peak current shows a decreasing trend. The average power of the target shows a rising trend with the increase in the duty cycle, while the peak power of the target shows a decreasing trend with the increase in the duty cycle. The results of OES show that with the increase in the duty cycle, the total peak intensity of copper and argon emissions shows an overall increasing trend. The duty cycle from 3% to 4.5% change in copper and argon emission peak total intensity change is not obvious. The deposition rate and surface morphology of the copper film were investigated by scanning electron microscopy, and the deposition rate of the copper film increased with the increase in the duty cycle, which was mainly due to the increase in the average power. The surface roughness of the copper film was evaluated by atomic force microscopy. X−ray diffraction (XRD) was used to analyze the grain size and texture of the Cu film, and the results showed that the average grain size of the Cu film increased from 38 nm to 59 nm on the (111) and (200) crystal planes. Four−probe square resistance test copper film resistivity in 2.25%, 3% low duty cycle conditions of the copper film resistivity is generally higher than 4.5%, 5.25% high duty cycle conditions, the copper film resistivity shows the trend of change is mainly affected by the copper film grain size and the (111) face of the double effect of the optimal orientation. The lowest resistivity of the copper film measured under the 4.5% duty cycle condition is 1.7005 μΩ·cm, which is close to the intrinsic resistivity of the copper film of 1.67 μΩ·cm. Full article

This research focuses on the evaluation of a heritage office building in the town of Kenadsa in the Southwest of Algeria (a famous oasis in the arid regions of the country). Emphasizing user’s perception as a metric key for performance assessment, this study aims to investigate the users’ perception and behaviors of a heritage office building on an oasis settlement. The research was conducted in 2023, and employing a multidimensional approach, both quantitative and qualitative methodologies, along with agent-based modeling, were integrated. One qualitative methodology is based on a series of field surveys and the other quantitative methodology relies on in situ measurements of the physical dimensions of the environment. The quantitative aspect involves an agent-based modeling framework, simulating user interactions by incorporating physical dimensions, spatial layout, historical context, and user behavior. The main findings of this study, examining perceptions and behaviors under varying luminous and thermal environments, show that this integrated approach provides insights into user satisfaction, spatial utilization, and the identification of user behaviors and productivity in each office. Full article

Resin infiltration is an effective method to mask vestibular white spots. If needed, external bleaching is usually recommended before infiltration, whilst in clinical practice, this sequence may not always be feasible. This in vitro study evaluated the effect of bleaching after resin infiltration regarding surface roughness and color using bovine incisors. Unlike for the untreated specimens (control, n = 25), artificial caries lesions were created within the test group (n = 25) using a demineralization solution at 37 °C for five days (pH = 4.95). The lesions were subsequently infiltrated using a resin infiltrant (Icon, DMG, Hamburg, Germany), followed by polishing. Afterwards, all specimens were bleached with a 10% carbamide peroxide gel (Opalescence, Ultradent, South Jordan, UT, USA) for 8 h/day over a ten-day period. Between bleaching treatments, specimens were stored in an opaque container with moistened paper tissues at 37 °C. Surface roughness was measured using a profilometer, and color in the L*a*b* space was assessed spectrophotometrically before and after bleaching. Bleaching increased the L*-values of both infiltrated (mean ± SD; ΔL* = 3.52 ± 1.98) and untreated (control) specimens (ΔL* = 3.53 ± 2.30) without any significant difference between the groups (p = 0.983). Bleaching also induced a significant increase in the mean surface roughness of both infiltrated (p < 0.001) and untreated (p = 0.0134) teeth. It terms of clinical relevance; it can be concluded that bleaching resin-infiltrated enamel is as effective as bleaching sound enamel. Full article