The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the reg...The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.展开更多
Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many s...Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.展开更多
This paper presents a new sensorless method, the so-called harmonic impedance / admittance, for detecting speed of induction motors, which is based on the impedance measurement, harmonic analysis and digital signal p...This paper presents a new sensorless method, the so-called harmonic impedance / admittance, for detecting speed of induction motors, which is based on the impedance measurement, harmonic analysis and digital signal processing. The method improves theperformance of conventional voltage-based and current-based techniques, because the impedance or admittance harmonics is independent of input or output of motor system due to the system-inherent nature of impedance. It has been used successfully in detecting the rotor speed of three-phase induction motors. A comparison between the proposed method and the conventionalcurrent-based method is also demonstrated.展开更多
The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to de...The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to detect running vehicle position for synchronized current generation. To maintain the stable levitating condition during vehicle running, the irregularity of guideway surface should be monitored by sensors measuring the displacement and acceleration between vehicle and guideway. In this study, the application methods of these sensors in the high speed maglev are investigated and through the experiments by using the small-scale test bed, the validity of examined methods is confirmed.展开更多
Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes...Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes a you only look once(YOLO)algorithm for speed bump detection(SPD-YOLO),a lightweight model based on YOLO11s that integrates three core innova-tive modules to balance detection precision and computational efficiency:it replaces YOLO11s’original backbone with StarNet,which uses‘star operations’to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency;its neck incorporates context feature calibration(CFC)and spatial feature calibration(SFC)to improve detection performance without significant computational overhead;and its detection head adopts a lightweight shared convolutional detection(LSCD)structure combined with GroupNorm,minimizing computational complexity while preserving multi-scale feature fusion efficacy.Experi-ments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision(mAP)of 79.9%,surpassing YOLO11s by 1.3%and YOLO12s by 1.2%while reducing parameters by 26.3%and floating-point operations per second(FLOPs)by 29.5%,enabling real-time deploy-ment on resource-constrained platforms.展开更多
The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help...The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.展开更多
The perovskite photodetectors can be used for image sensing, environmental monitoring, optical communication, and chemical/biological detection. In the recent five years, the perovskite photoelectric detectors with va...The perovskite photodetectors can be used for image sensing, environmental monitoring, optical communication, and chemical/biological detection. In the recent five years, the perovskite photoelectric detectors with various devices are welldesigned and have made unprecedented progress of light detection. It is necessary to emphasize the most interesting works and summarize them to provide researchers with systematic information. In this review, we report the recent progress in perovskite photodetectors, including highly sensitive, ultrafast response speed, high gain, low noise, flexibility, and narrowband, concentrating on the photodetection performance of versatile halide perovskites(organic–inorganic hybrid and all inorganic compositions). Currently, organic–inorganic hybrid and all-inorganic halide microcrystals with polycrystalline film, nanoparticle/wire/chip, and block monocrystalline morphology control show important performance in response rate,decomposition rate, noise equivalent power, linear dynamic range, and response speed. It is expected that a comprehensive compendium of the research status of perovskite photodetectors will contribute to the development of this area.展开更多
Vehicle speed is an important parameter that finds tremendous application in traffic control identifying over speed vehicles with a view to reducing accidents. Many methods, such as using RADAR and LIDAR sensors have ...Vehicle speed is an important parameter that finds tremendous application in traffic control identifying over speed vehicles with a view to reducing accidents. Many methods, such as using RADAR and LIDAR sensors have been proposed. However, these are expensive, and their accuracy is not quite satisfactory. In this paper, a video-based vehicle speed determination method is presented. The method shows satisfactory performance on standard data sets and gives that error rate of velocity estimation is within 10%.展开更多
文摘The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.
文摘Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.
文摘This paper presents a new sensorless method, the so-called harmonic impedance / admittance, for detecting speed of induction motors, which is based on the impedance measurement, harmonic analysis and digital signal processing. The method improves theperformance of conventional voltage-based and current-based techniques, because the impedance or admittance harmonics is independent of input or output of motor system due to the system-inherent nature of impedance. It has been used successfully in detecting the rotor speed of three-phase induction motors. A comparison between the proposed method and the conventionalcurrent-based method is also demonstrated.
文摘The high speed maglev is mainly characterized by propulsion using linear synchronous motor (LSM) and vehicle levitation from the guideway surface. In LSM propulsion control, the position detection sensor is used to detect running vehicle position for synchronized current generation. To maintain the stable levitating condition during vehicle running, the irregularity of guideway surface should be monitored by sensors measuring the displacement and acceleration between vehicle and guideway. In this study, the application methods of these sensors in the high speed maglev are investigated and through the experiments by using the small-scale test bed, the validity of examined methods is confirmed.
文摘Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes a you only look once(YOLO)algorithm for speed bump detection(SPD-YOLO),a lightweight model based on YOLO11s that integrates three core innova-tive modules to balance detection precision and computational efficiency:it replaces YOLO11s’original backbone with StarNet,which uses‘star operations’to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency;its neck incorporates context feature calibration(CFC)and spatial feature calibration(SFC)to improve detection performance without significant computational overhead;and its detection head adopts a lightweight shared convolutional detection(LSCD)structure combined with GroupNorm,minimizing computational complexity while preserving multi-scale feature fusion efficacy.Experi-ments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision(mAP)of 79.9%,surpassing YOLO11s by 1.3%and YOLO12s by 1.2%while reducing parameters by 26.3%and floating-point operations per second(FLOPs)by 29.5%,enabling real-time deploy-ment on resource-constrained platforms.
文摘The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.
基金Project supported by the International Cooperation and Exchange Project of Jilin Province,China(Grant Nos.20170414002GH and 20180414001GH)
文摘The perovskite photodetectors can be used for image sensing, environmental monitoring, optical communication, and chemical/biological detection. In the recent five years, the perovskite photoelectric detectors with various devices are welldesigned and have made unprecedented progress of light detection. It is necessary to emphasize the most interesting works and summarize them to provide researchers with systematic information. In this review, we report the recent progress in perovskite photodetectors, including highly sensitive, ultrafast response speed, high gain, low noise, flexibility, and narrowband, concentrating on the photodetection performance of versatile halide perovskites(organic–inorganic hybrid and all inorganic compositions). Currently, organic–inorganic hybrid and all-inorganic halide microcrystals with polycrystalline film, nanoparticle/wire/chip, and block monocrystalline morphology control show important performance in response rate,decomposition rate, noise equivalent power, linear dynamic range, and response speed. It is expected that a comprehensive compendium of the research status of perovskite photodetectors will contribute to the development of this area.
文摘Vehicle speed is an important parameter that finds tremendous application in traffic control identifying over speed vehicles with a view to reducing accidents. Many methods, such as using RADAR and LIDAR sensors have been proposed. However, these are expensive, and their accuracy is not quite satisfactory. In this paper, a video-based vehicle speed determination method is presented. The method shows satisfactory performance on standard data sets and gives that error rate of velocity estimation is within 10%.
