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Ground-based positioning systems (GBPSs), such as cellular networks and pseudolites, can provide high-precision positioning services through carrier phase positioning (CPP). Most existing methods are based on having accurate coordinates of all transmitters (TXs). In some applications, however, it could be too expensive and time-consuming to measure the coordinates of all TXs accurately, and requiring precise manual measurements severely degrades the flexibility of GBPSs. In this paper, we propose a carrier phase based autonomous coordinate evolution (CPACE) method for GBPSs, which uses the observations of multiple users to continuously improve the accuracy of TX coordinates. We first propose a Batch CPACE (B-CPACE) method and analyze its theoretical advantages over traditional single-point positioning (SPP). In the case of a large number of users, to avoid heavy burden on data transmission and calculation, we propose two distributed CPACE (D-CPACE) methods which have the same asymptotic performance with B-CPACE. Our numerical simulations prove that both B-CPACE and D-CPACE have better accuracy than SPP and reach the Cramer-Rao lower bounds. A real-world experiment shows that the proposed method can achieve centimeter-level accuracy. [ABSTRACT FROM AUTHOR]

The rapidly growing wireless communication market has resulted in tremendous changes in the requirements and capabilities of radios to support numerous multiband wireless connections. For example, the 850- and 1,900-MHz frequency spectrums are used to support the Global System for Mobile Communications (GSM) networks. New frequency bands of nearly 2,100 MHz have been adopted in 3G networks, and more frequency bands and spectrums are introduced in 4G systems, ranging from 600 MHz to 2.5 GHz ,. Wireless communications have resulted in radio that, by 2010, had evolved from a single-mode, triple-band 2G system, to a triple-mode, nine-band (4 × GSM, 5 × Universal Mobile Telecommunications System with High-Speed Packet Access+), high-speed data-capable system. The trend continues today with rapid deployment of 5G and future 6G systems, driving new bands and modes of operation. The frequency band of sub-6 GHz from 450 MHz to 6 GHz and the millimeter-wave spectrum from 24 to 52 GHz are employed in 5G systems to provide multifunctional services. To support the continuous evolution of high data rates, as shown in , 5G mobile networks have been widely developed as an ideal on-demand connection solution for devices and users in urban areas. [ABSTRACT FROM AUTHOR]

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search. [ABSTRACT FROM AUTHOR]

A frequency-modulated ultrawideband (FM-UWB) hybrid transceiver, with a high system reuse ratio up to 90%, is fabricated in the 65-nm CMOS process for both short-range wireless communication and high-resolution radar ranging. The radio frequency (RF) front-ends (RFFEs) based on module stacking and current reuse achieve significant RF power savings of about 30% and 50% for the transmitter and the receiver (RX), respectively. The presented IF zero-crossing delay discrimination benefits a high radar resolution less than 1 cm. A multimode relaxation oscillator (OSC) for the reconfigured triangular subcarrier generation and a dual-path ring voltage-controlled oscillator (VCO) for the linear FM, stacked by a wideband push–pull power amplifier (PA), generate an FCC-compliant UWB signal. An active balun embedded low-noise amplifier (LNA) and two symmetric-detuning band-passed filters (BPFs) are stacked with the bandwidth extension to linearly demodulate the UWB FM signal. Experimental results show that the 3.75–4.25-GHz hybrid transceiver has an energy efficiency of 1.9 nJ/bit with an active area of 0.7 mm2 and a power dissipation of 1.9 mW and achieves the RX sensitivity of −71 dBm and the transmitted power of −14.1 dBm, with a bit error rate (BER) of 10−4 at a distance of 4 m under the data rate (DR) of 0.1–1.0 Mbps. The transceiver also achieves the noise figure (NF) of 4.8 dB and the phase noise of −78.6 dBc/Hz at 1-MHz offset frequency. [ABSTRACT FROM AUTHOR]

Recently, contactless bimodal palmprint recognition technology has attracted increased attention due to the COVID-19 pandemic. Many dual-camera-based sensors have been proposed to capture palm vein and palmprint images synchronously. However, translations between captured palmprint and palm vein images differ depending on the distance between the hand and the sensors. To address this issue, we designed a low-cost method to align the bimodal palm regions for current dual-camera systems. In this study, we first implemented a contactless palm image acquisition device with a dual-camera module and a single-point time of flight (TOF) ranging sensor. Using this device, we collected a dataset named DCPD under different distances and light source intensities from 271 different palms. Then, a bimodal palm image alignment method is proposed based on the imaging and ranging models. After the system model is calibrated, the translation between the visible light and infrared light palm regions can be estimated quickly based on the palm distance. Finally, we designed a convolutional neural network (CNN) to effectively extract the fine- and coarse-grained palm features. Compared to widely used existing methods, the proposed networks achieved the lowest equal error rate (EER) on the Tongji, IITD, and DCPD datasets, and the average time cost of the system to perform one-time identification is approximately 0.15 s. The experimental results indicate that the proposed methods achieved high efficiency and comparable accuracy. In addition, the system’s EER and rank-1 on the DCPD dataset were 0.304% and 98.66%, respectively. [ABSTRACT FROM AUTHOR]

The study considers the class of triangulation laser measurement systems based on the laser and the camera. Such systems are widely used in practice. Their main characteristics are range and distance measurement accuracy. This work is devoted to the study of ways to improve the accuracy of triangulation laser measurement systems. The study identifies and investigates the design parameters of the measuring system that determine its range, shows their variability, impact on sensitivity and accuracy. It has been established that the ratio of the boundary values of the working range plays a key role in this influence. A quantitative criterion for assessing the uniformity of sensitivity is proposed, its dependence on design parameters is shown, and recommendations are given for their choice. Theoretical results are confirmed by natural experiment. The experiment was carried out using the industrial robot. [ABSTRACT FROM AUTHOR]

5G-and-beyond (B5G) networks are moving toward the higher end of the millimeter-wave (mmWave) spectrum (i.e., from 25 to 100 GHz) to support integrated communications and ranging (ICAR) services in next-generation factory deployments. The ICAR services in factory deployments require extreme bandwidth/capacity and large ranging coverage, which a mmWave-B5G system can fulfill using massive multi-input and multioutput (mMIMO), beamforming, and advanced ranging techniques. However, as mmWave signal propagation is sensitive to harsh channel conditions experienced in typical indoor factory environments, there is a growing interest in the realistic mmWave indoor channel modeling to evaluate the practical scope of the mmWave-B5G systems. In this article, we study and implement a 3-D stochastic channel model using the baseline third-generation partnership project model. Our channel model employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions. Using the generated CIR, we present the performance analysis of an mmWave-B5G system in terms of power delay profile, path loss, communication and ranging coverage, and mMIMO channel capacity. [ABSTRACT FROM AUTHOR]

Rechargeable batteries are becoming ubiquitous in wide ranging applications including electric vehicles (EVs), consumer electronics, communication devices, aerospace equipment, household equipment, and power tools. A battery management system (BMS) is an integral part of every battery pack, tasked with ensuring the safety, efficiency, and reliability of its performance. When a battery is connected to a power supply for charging, it is the BMS that decides how much current is allowed into the battery according to various criteria, such as the reduced charging time and extended state of health (SOH). Li-ion rechargeable batteries are notorious for their susceptibility to over-voltage and undervoltages exposure; consequently, it is the responsibility of a BMS to protect the battery cell from exposure to extreme conditions, thus preserving its quality and reliability for longer time. [ABSTRACT FROM AUTHOR]

A wide range of precision applications requires video measuring systems that achieve a large number of successive measurements and deliver fast results. Their efficiency is essentially given by the technical performances of the used equipment and by the measurement technique on which they operate. In order to enhance the reliability of such a system, the paper presents a new method of measuring the distance with a single video camera intended to assess the distance at which the object of interest to the camera is located. The technique makes use of a least squares-based sharpness cost function and determines the distance between the camera and the object of interest by minimizing the least squares deviation of the current sharpness values from the sharpness values obtained by calibration. It involves the current sharpness calculation phase, the normalization phase, the phase of calculating the deviations of the current sharpness from the dependencies obtained by calibration and the phase of determining the minimum deviation index. [ABSTRACT FROM AUTHOR]

In order to reduce the measurement error of transmission line crossing distance and reduce the occurrence rate of transmission line accidents, a method of transmission line crossing distance measurement based on grid information model was proposed. The platform of crossing distance measurement including data acquisition unit, transmission network and measurement unit was designed. The data of transmission and transformation engineering phases was obtained by grid information model in the data acquisition unit, the data of the lines to be measured was extracted from the basic database of the grid information model by the measurement unit, the catenary model and the three-dimensional spatial model were created to realize the transmission line crossing distance measurement. The experimental results show that the proposed method has a high application value and feasibility for the information acquisition of transmission line crossing distance with short time and high precision. [ABSTRACT FROM AUTHOR]

A safe distance between two moving trains is an important condition to avoid train rear-end collision. Since the image data obtained by machine vision is rich in information and can be integrated in many aspects based on the collected images, this study proposes a distance measurement method based on monocular machine vision. This method uses the constant distance between the two tracks of the train (1 435 mm) as a benchmark to estimate the distance between trains. The images collected by the monocular camera are processed and analyzed by the convolution neural network to extract the track features. Based on the existing small hole imaging principle, the mapping relationship between the world coordinate system and the pixel coordinate system is derived, so as to optimize the calculation formula of the distance between trains. The experimental results show that the error rate of the system is less than 6%, and the measurement time of the system is within 40 ms, indicating that the method realizes the effective fusion and integration of ranging and other information obtained in the image, and can be used to judge the braking distance of the train. [ABSTRACT FROM AUTHOR]

Remote keyless entry systems have penetrated the vehicular market due to its convenience. However, its vulnerability against relay attacks exploiting the wireless channel is a serious threat. One of the prominent countermeasures is radio ranging between the key fob and the vehicle. Taking into account radio regulations, usability and power consumption among other things, a suitable approach is necessary to devise. This article proposes sub-GHz two-way ranging based on phase detection which offers high tolerance against reference clock offset between a pair of transceiver devices and a novel compensation scheme against multipath effects. We present the operation principle and theoretical performance under a two-path model of radio propagation and additive white Gaussian noise. In field experiments with prototype devices, the ranging accuracy is validated. We show that the proposed ranging produces sufficient performance for remote keyless entry systems, confirming if the key fob is within a few meters of the vehicle. [ABSTRACT FROM AUTHOR]

In sandwich laminates made of metal–polymer–metal (MPM) layers under forming conditions, the thickness changes along the thin‐layered specimens are of particular interest. To measure this distribution, it is common to take microscopic images and determine manually the thicknesses at particular discrete points. We compare and provide several methods determining the thickness distribution from microscopic images. First, an interpolation scheme is chosen to obtain a continuous function of the interfaces between the layers instead of the discrete pixel data. Afterwards, the methods—ruled surface approach, orthogonal projection, embedded circle scheme, and a dull but simple method—are applied and compared with each other. For very smooth data, most of the schemes show equivalent results. However, the "brute force"—called the dull approach—turns out to be the most robust scheme, particularly, if noisy data is studied. The Gaussian error propagation concept is applied to study the uncertainties resulting from noisy pixel data. The schemes are adapted to finite element simulation results as well so that a direct comparison of experimental and numerical data would be possible. [ABSTRACT FROM AUTHOR]

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively. [ABSTRACT FROM AUTHOR]

In the GNSS-denied localization scenario, only received signals and corresponding distance measurements are available. Based on that, in this letter, we construct a multistage clustering-based model for UAV swarm. We characterize the proposed model as a coalition formation game (CFG) and provide corresponding preference criteria for algorithm design. A tree-like multistage clustering mechanism is adopted based on a coalitional graph game (CGG). Each cluster (local map) performs localization calculation and then merges through neighboring drones (NDs) stage by stage. The simulation results show that the proposed scheme can achieve better localization accuracy than the comparison algorithms and is more robust for irregular network topologies. [ABSTRACT FROM AUTHOR]

In intelligent driving, the detection and ranging of drivable area are key technologies in path planning. In order to realize quick and accurate detection and segmentation of drivable area, we adopt YOLACT_ResNet38_TFPN network as an improved design of YOLACT. The original YOLACT has ResNet101 residual structure. We reduce the network layers to ResNet38 and add C6 and C7 with larger receiving field to improve the detection speed in the drivable area. Moreover, the FPN (feature pyramid network) is designed as a structure of three sizes to match C6 and C7. According to the characteristics of road image, the aspect ratio of three anchor points is redesigned to further improve the detection accuracy and speed. A univariate linear regression model is designed to accurately calculate the distance of the drivable area. The model parameters are achieved by multivariate linear fitting method based on multiple sets of distance measurement data. Finally, YOLACT_ResNet38_TFPN's FPS for the drivable area is 46.13, the box mAP is 62.36, and the mask mAP is 61.36. The maximum ranging error of this method within 96.90 m is 5.3 m. It can quickly and accurately measure the drivable distance and provide reasonable path driving suggestions for intelligent driving. [ABSTRACT FROM AUTHOR]

An absolute distance measurement sensor based on self-mixing interferometry (SMI) is suitable for application in aerospace due to its small size and light weight. However, an SMI signal with a high sampling rate places a burden on sampling devices and other onboard sources. SMI distance measurement using compressed sensing (CS) is proposed in this work to relieve this burden. The SMI signal was sampled via a measurement matrix at a sampling rate lower than Nyquist's law and then recovered by the greedy pursuit algorithm. The recovery algorithm was improved to increase its robustness and iteration speed. On a distance measuring system with a measurement error of 60 µm, the difference between raw data with 1800 points and CS recovered data with 300 points was within 0.15 µm, demonstrating the feasibility of SMI distance measurement using CS. [ABSTRACT FROM AUTHOR]

In this work, a prototype solar-blind single-photon imaging lidar based on SiC ultraviolet (UV) avalanche photodiodes (APD) is demonstrated. The SiC APD with a peak responsivity at ~280 nm can greatly lessen the strict solar-blind filtering requirement for the system. When characterized by using a gated quenching circuit, the SiC APD operating in Geiger mode could exhibit a room temperature single photon detection efficiency of ~27% with a dark count probability of $1.59\times 10 ^{-2}$. Based on the SiC APD, a 266 nm nanosecond pulsed laser, a galvanometer scanner and a time-correlated single-photon counter, 3-dimensional depth image with $128\times 128$ resolution is realized by the prototype system in daylight. The average ranging accuracy of the system is found within 28 mm based on current setup. [ABSTRACT FROM AUTHOR]

An optical vortex was applied in an underwater lidar system to improve ranging accuracy. A self-developed intensity modulated continuous wave 532 nm laser beam was used as the light source. The detector was a photomultiplier tube (PMT). The output signal from the PMT was correlated with a reference signal to retrieve the distance of the target. A spiral phase plate (SPP) was inserted before the detector to convert the spatially coherent target reflection onto an optical vortex ring. A mask with a transparent ring that matched the vortex was used to block the scattering light distributed inside and outside the ring. Experimental results showed that the ranging error was significantly reduced in a turbid medium by the method, in addition, a high-order SPP worked better. With a 24-order SPP, we obtained a ranging error reduction from 20 cm to 2.73 cm when the attenuation length was 9. [ABSTRACT FROM AUTHOR]

Private cars’ travel has been one of the main factors causing urban traffic congestion. Especially during morning and evening rush hours, private car commuters bring a significant burden to traffic. However, there is very little literature on them due to the lack of access to relevant data. A real-world dataset containing vehicle passing records of Electronic Registration Identification (ERI) of vehicles provides us with an opportunity to research private car commuters. We propose a regular behavior-based model to recognize private car commuters. In the model, a regular behavior-based definition of private car commuters is firstly proposed. Then, TDSP(Time dependent shortest path)-based distance measurement and a hierarchical clustering method are designed to extract regular behaviors. Furthermore, we utilize a regular threshold $p $ to help determine regular behaviors. The experiment, which is conducted on a real-world dataset containing one-week vehicle passing records in Chongqing of China, validates the effectiveness and accuracy of the proposed model. Moreover, we analyze the mobility pattern of private car commuters, and some typical mobility patterns of them are successfully found. [ABSTRACT FROM AUTHOR]

• Decimeter-level positioning accuracy and a stable update rate of 20 Hz are achieved for tracking foresters. • Decimeter-level positioning accuracy is obtained for mapping tree stem locations. • Non-line-of-sight (NLoS) distance measurement classification and processing based on the received signal quality. This paper presents a new method for tracking foresters and mapping tree stem locations under forest canopies utilizing ultra-wideband (UWB) data, in which four estimators (collector, estimator, locator, and extractor) operate closely together. The collector records all the distance measurements (beacon to beacon and terminal to the beacons); the estimator determines the local coordinates of the beacons automatically and precisely based on the distance measured between them; the locator then determines the trajectory of the terminal/forester using the extended Kalman filter (EKF) with the assistance of non-line-of-sight (NLoS) distance measurement classification and processing; the extractor continuously determines the optimal locations of the tree stems. To fully evaluate the performance of the developed method, experiments were conducted under four forest canopies with different tree densities and species. The experimental results show that the root-mean-squared-errors (RMSEs) of the proposed method for the terminal/forester positioning and individual tree stem location are less than 0.3 m with a stable update rate of 20 Hz, and a coverage of larger than approximately 3,000 m2 with only four UWB beacons under forest canopies. Therefore, it is sufficient for forest surveys and management with reduced human efforts. [ABSTRACT FROM AUTHOR]

Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.

Density Peaks (DP) Clustering organizes data into clusters by finding peaks in dense regions. This involves computing density ($\rho$ ρ ) and distance ($\delta$ δ ) of every point. As such, though DP has been very effective in producing high quality clusters, their complexity is O($N^2$ N 2 ) where $N$ N is the number of data points. In this paper, we propose a fast distributed density peaks clustering algorithm, FDDP, based on the z-value index. In FDDP, we first employ the z-value index to map multi-dimensional data points into one dimensional space, and then range-partition the data according to the z-value to balance the load across the processing nodes. We ensure minimal overlapping range to handle computations at the boundary points. We also propose FC, an efficient algorithm that employs a forward computing strategy to calculate $\rho$ ρ linearly. Additionally, we propose another algorithm, CB, which uses a caching and efficient searching strategy to compute $\delta$ δ . Moreover, FDDP is able to reduce the time complexity from $O(N^2)$ O (N 2) to $O(N\cdot log(N))$ O (N · l o g (N)) . We provide a theoretical analysis of FDDP and evaluated FDDP empirically. Our experimental results show that FDDP outperforms the state-of-the-art algorithms significantly. [ABSTRACT FROM AUTHOR]

In the fields of radiation environment monitoring and unknown radioactive materials search, locating the far-field gamma-ray sources with high accurate coordinates is an important and necessary capability for a gamma imager. However, traditional coded gamma-ray imaging systems only provide 2-D radiation images without distance information. In this work, a four-eyes stereo gamma camera is proposed, which consists of four independent coded aperture detectors with an adjacent spacing of 286 mm. Each detector is comprised of pixelated scintillators with a pixel array of 44 $\times44$ , forming a global detection area of 140 $\times140$ mm2. The ranging capability is related to the resolution and the adjacent spacing of the detectors. With the setup of the above device, the traditional binocular-based ranging method has a short measurement range and a large measurement deviation. Here, a min-area distance measurement algorithm based on a four-eyes stereo gamma camera is proposed, combined with “manual” fine sampling and Gaussian fitting method to successfully achieve the high-precision localization of the far-field source. The simulation results show that compared with the traditional binocular-based ranging method, the min-area method based on a four-eyes stereo camera lengthens the range from 20 m to more than 50 m and improves the ranging accuracy within 20 m by more than six times. The accuracy of 20 and 50 m is less than 1.4 m (7.13%) and 10.5 m (21%), respectively. The reliability of the simulation results has been verified by experiments. Furthermore, the ranging capability of this algorithm for multipoint sources in the far field has been verified. [ABSTRACT FROM AUTHOR]

This paper explores criticality-based real-time scheduling of neural-network-based machine inference pipelines in cyber-physical systems (CPS) to mitigate the effect of algorithmic priority inversion. We specifically focus on the perception subsystem, an important subsystem feeding other components (e.g., planning and control). In general, priority inversion occurs in real-time systems when computations that are of lower priority are performed together with or ahead of those that are of higher priority. In current machine perception software, significant priority inversion occurs because resource allocation to the underlying neural network models does not differentiate between critical and less critical data within a scene. To remedy this problem, in recent work, we proposed an architecture to partition the input data into regions of different criticality, then formulated a utility-based optimization problem to batch and schedule their processing in a manner that maximizes confidence in perception results, subject to criticality-based time constraints. This journal extension matures the work in several directions: (i) We extend confidence maximization to a generalized utility optimization formulation that accounts for criticality in the utility function itself, offering finer-grained control over resource allocation within the perception pipeline; (ii) we further instantiate and compare two different criticality metrics (distance-based and relative velocity-based) to understand their relative advantages; and (iii) we explore the limitations of the approach, specifically how inaccuracies in criticality-based attention cueing affect performance. All experiments are conducted on the NVIDIA Jetson AGX Xavier platform with a real-world driving dataset. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) are set to be the next global revolution in transportation. One of the major challenges in a CAV environment is handling the positional-forgery of Basic Safety Messages while calculating critical traffic gaps at a Stop Sign Gap Assist controlled intersection. Traditionally, positional-forgery has been handled by isolated instances of physical layer detection and correction mechanisms, which proved unreliable during the multi-hop and majority malicious vehicle scenarios. In this work, we propose a novel framework called Positional-forgEry Resistant traFfic gap Estimation for Connected inTersection management (PERFECT) that performs optimal traffic gap estimation even during multi-hop scenarios and majority malicious positional-forgery attacks. The framework reduces traffic gap estimation error during majority malicious scenarios by over 60%, keeps the average error within 8.17%–10.62% and minimizes the maximum error across a range of malicious situations. [ABSTRACT FROM AUTHOR]

Due to the strong penetration ability and high transmission rate of ultra-wideband (UWB) signals, UWB technology plays a very significant role in the field of precise indoor positioning. However, the harsh and volatile indoor environment leads to non-line-of-sight (NLOS) propagation and severe attenuation of UWB signals, which may generate significant ranging and positioning errors. To mitigate NLOS effect and improve positioning accuracy, existing methods use ranging information and channel impulse response (CIR) to identify UWB signal propagation channels. However, these NLOS identification methods often require a priori knowledge and suitable thresholds, and most of them only perform a binary classification between LOS and NLOS in a particular scenario. To address these disadvantages, this paper proposes a novel multi-classification method to classify UWB signal propagation channels based on one-dimensional wavelet packet analysis (ODWPA) and convolutional neural network (CNN). This method first decomposes the CIR of known categories into colored coefficients images using a one-dimensional wavelet packet function, then uses these images to train CNNs with different structures, and finally selects CNN model with the best performance to identify the unknown category of UWB signal propagation channels. The experimental results show that the performance of the proposed method is always the best, regardless of the type of experimental scenario, and the identification accuracy is always higher than 90% and up to 100%. [ABSTRACT FROM AUTHOR]

This paper presents a digital processing system for terrestrial laser range finding systems. It is mainly comprised of a commercial digital signal processor with 400 MHz. For the signal digitization, an analog-to-digital converter with a 12-bit resolution is sampled with 50 MS/s. Such a digital system architecture has several advantages: automatic gain control, coherent multi-pulse averaging, digital signal filtering, upsampling, and advanced peak detection methods. When combined with a suitable laser and detector unit, the digital system outperforms the classic analog threshold-based laser finders with regard to required laser output power, multitarget discrimination, and peak detection accuracy. The best results were achieved using a matched filter, compared to wavelet denoising techniques and classic Savitzky-Golay filtering. Finally, this system architecture allows for a small-sized, lightweight, battery-operated, and energy-efficient, class 1 eye-safe laser range finder, capable of measuring distances up to multiple km with accuracy and precision of better than 1 m to uncooperative targets. [ABSTRACT FROM AUTHOR]

This paper provides an adaptive hybrid robust filter (AHRF) for multi-sensor relative navigation systems that can be used to support cooperative intelligent transport systems. It is known that Huber’s M-estimation based robust filter and the fault detection and exclusion (FDE) based RAIM filter each has its own drawbacks, depending on the nature of the observation error biases. Based on the interactive multiple model (IMM) framework, our proposed AHRF in this paper can take advantage of both filters in a complementary sense. A new adaptive IMM (AIMM) algorithm with Markov transition probability prediction is proposed to allow AHRF to switch efficiently between the two filters. We consider the relative navigation system with Global Navigation Satellite System (GNSS) and ultra-wideband (UWB) as observations to verify AHRF in three cases of possible failure modes and multipath-induced errors. Our results show that AHRF outperforms both the FDE and robust filter in all cases. AHRF framework can be further adapted to include many other fault-tolerant filters to improve the robustness of multi-sensor relative navigation system even further. [ABSTRACT FROM AUTHOR]

Supernovae (SNe) are luminous optical transients, which, despite being rare events in a single galaxy, nowadays are discovered at a rate of ten per day. They populate a growing zoo of different types linked to a variety of progenitors and explosion mechanisms. Through a biased selection of a century of researches, I will review how SNe have became a leading tool to probe the cosmic expansion. I will mainly focus on SNe of type Ia, originating from the thermonuclear explosion of white dwarfs, that were demonstrated to be excellent distance indicators after proper calibration and standardisation. Two decades ago, SNe Ia provided the surprising result that the cosmic expansion is accelerated by a misterious dark energy. Recently, with the decisive contribution of Cepheids variables as primary distance indicators, SN Ia allowed to measure the value of the Hubble-Lemaître constant, H 0 , with an error of only 1%. It turned out that the value of H 0 based on SNe Ia is in tension with the one measured through the fit of the CMB fluctuations. If confirmed, this finding requires a deep revision of the standard cosmological model. [ABSTRACT FROM AUTHOR]

In this paper, we present a Pedestrian Indoor Navigation system integrating Deterministic, Opportunistic, and Cooperative functionalities (PINDOC) for multi-agent navigation. The deterministic module produces a navigation solution for each agent by utilizing a Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation System (INS) augmented with an altimeter and foot-to-foot range measurements. The opportunistic module augments the deterministic solutions with pseudorange measurements extracted from cellular Long-Term Evolution (LTE) signals. The navigation accuracy of each individual agent is further enhanced by cooperative localization using Ultra-WideBand (UWB)-based inter-agent range measurements. We developed a dedicated multi-sensor pedestrian navigation hardware testbed and conducted an experiment to evaluate the navigation performance of different combinations of components comprising PINDOC: ZUPT-aided INS, altimeter, foot-to-foot ranging, LTE signals, and inter-agent ranging. The experiment involved three agents, with one agent traversing a trajectory of 600 meters in 14 minutes, during which, the other two agents remained stationary. The traversed trajectory included terrains of flat surfaces, stairs, ramps, and elevators. The PINDOC system showed high accuracy, achieving a position Root-Mean-Squared Error (RMSE), maximum error, and final error of 0.93 m, 2.23 m, and 1.28 m over the 600-meter trajectory. [ABSTRACT FROM AUTHOR]

The shearer drum height measurement plays a crucial role in the fully mechanized mining face. This paper proposes a measurement method of drum height, taking the ranging arm swing-angle and fuselage attitude angle into consideration; therefore, compared with the existing drum height measurement methods, our approach adheres more to the actual operating conditions of the shearer. Based on our approach, we further developed a shearer drum height sensor. Firstly, TLE5012B (MEMS magnetic angle chip) and MPU6050 (MEMS inertial navigation chip) are selected as the sensing elements of swing-angle and attitude angle, respectively. Secondly, a “rocker-rotating shaft” is designed to convert the swing-angle into the rotation of TLE5012B’s external magnetic field. Thirdly, an intelligent drum height sensor based on DSP-TMS320F28335 is developed, including hardware circuits and software that measure and process the angle data, calculate the drum height, store and display the data, and transmit the data by CAN or WIFI. Fourthly, the magnetic field distribution and vibration source of the shearer are simulated and analyzed, and the magnetic shielding shell and anti-vibration device are designed to reduce the influence of external factors on the sensor. Furthermore, a sensor prototype was developed and experimentally tested. The maximum absolute errors of sensor swing-angle, pitch angle, roll angle, and drum height were 0.16°, 0.07°, 0.06°, and 6.7mm, respectively. Finally, we establish a measurement error model of shearer drum height and calculate the maximum measurement error is 2.24cm. The results show that our approach and sensor meet the requirements of shearer drum height measurement. [ABSTRACT FROM AUTHOR]

One of the bottlenecks of autonomous systems is to identify and/or design models and tools that are not too resource demanding. This paper presents the concept and design process of a moving platform structure–electric vehicle. The objective is to use artificial intelligence methods to control the model's operation in a resource scarce computation environment. Neural approaches are used for data analysis, path planning, speed control and implementation of the vision system for road sign recognition. For this purpose, multilayer perceptron neural networks and deep learning models are used. In addition to the neural algorithms and several applications, the hardware implementation is described. Simulation results of systems are gathered, data gathered from real platform tests are analyzed. Experimental results show that low-cost hardware may be used to develop an effective working platform capable of autonomous operation in defined conditions. [ABSTRACT FROM AUTHOR]

The acceptance of binding and rebar installation is the last and vital step in construction engineering. However, there are many environmental constraints in the acceptance of the quality of rebar binding, which affect the acceptance. To address these problems, this study proposes an image-based intelligent distance measurement system, proving convenient for workers to measure long distances. The solution could effectively improve the measurement range during the acceptance process of rebar binding. The proposed method is divided into three steps. In the first step, a laser rangefinder was employed to measure the distance between the camera and the rebar; in the second step, the corner detector, based on the improved Harris corner detection algorithm, was used to detect the corners of the rebar images; and in the third step, the value of laser distance measured and the pixel value obtained by the corner detector was adopted for distance measurement. The method our proposed has a positive impact on the quality acceptance process of rebar binding, which can greatly improve the acceptance efficiency and save time. [ABSTRACT FROM AUTHOR]

This article presents a stepped-frequency hybrid modulation (SFHM) methodology, which is proposed to work in conjunction with a frequency scanning array to effectively perform respiration monitoring, 2-D localization for stationary human subjects, and target trajectory tracking for moving ones. Conventional frequency-shift keying (FSK) radar can accurately detect the absolute distance and small displacement but suffers from the problem of a maximum unambiguous range. In comparison, continuous-wave radar with a chirp signal has no range limitation but suffers from a limited displacement measurement accuracy. To solve the two problems, SFHM is proposed to make full use of the advantages of both FSK mode and chirp mode, collaboratively resolving each other’s issues. In addition, the high flexibility of SFHM facilitates the implementation of a frequency scanning array, which provides a simple and cost-efficient means for fast beam steering. A prototype of the radar system is implemented, and a new signal separation algorithm was derived based on the SFHM theory. Various types of experiments confirm the ability of the radar to accurately provide the respiration and range information of stationary subjects and trajectory information of moving subjects. [ABSTRACT FROM AUTHOR]

Building green airports can be regarded as among the most promising routes to sustainable development of ecosystems and human health. This study aims at addressing the problem of green airport plan selection under an uncertain context by developing an uncertain multiattribute group decision making (MAGDM) model. In the proposed model, the assessment information is characterized in the form of a proportional hesitant 2‐tuple linguistic term set (PH2TLTS), which incorporates in binary form linguistic information that can accurately quantify subjective assessment information provided under uncertainty. The weights of assessment attributes of green airport plans are obtained automatically through a nonlinear programming model, which enhances the robustness of the decision‐making method. Subsequently, on the basis of PH2TLTSs, three distance measures are proposed: the proportional hesitant 2‐tuple linguistic Jaccard distance (PH2TLJD), the supplementary proportional hesitant 2‐tuple linguistic normalized Minkowski distance (SPH2TLNMD) and the cluster‐based proportional hesitant 2‐tuple linguistic normalized Minkowski distance (CBPH2TLNMD). The TOPSIS‐based comparison method proposed here can better determine the priorities of PH2TLTSs. The ranking and selection of green airport plans are derived using the PH2TL‐VIKOR model. Finally, a case study accompanied by sensitivity and comparative analyses is performed to verify the rationality and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Optical time-of-flight sensors have potential in the revolution of distance measurement. These sensors can continuously monitor the distance and track the movement of objects. However, the existing sensing methods for such distance optical sensors mainly calculate the flight time, e.g. pulse transmission and receiving time, without considering the environmental effects. Therefore, the measurement accuracy is severely reduced. There are other technologies with higher accuracy in distance measurement. Nonetheless, they are too expensive due to the high accurate power supply. In this paper, we innovatively improve the accuracy of continuous distance measurement using the artificial neural network (ANN) technique. The proposed method can be applied for very cheap optical distance sensors with analog output in a real-time system. Moreover, the proposed method can self-calibrate and be miniaturized for cheap analog sensor applications. The prototype is built with the infrared sensor GP2Y0A02YK0F and an Arduino control board (ESP32_DevC), and the ANN is implemented using the deep learning algorithm. The test results show that the distance measurement accuracy is significantly improved and the measuring range is increased from 15 to 150 cm. In addition, we calculate mean squared error, mean absolute error, mean bias error, and R 2 for further performance evaluation. The experimental results have proven the superiority of the proposed ANN method in optical distance measurement. The proposed method can be applied to many types of sensors. [ABSTRACT FROM AUTHOR]

Because each indoor site has its own radio propagation characteristics, a site survey process is essential to optimize a Wi-Fi ranging strategy for range-based positioning solutions. This study examines an unsupervised learning technique that autonomously learns an optimal ranging strategy for each site using Wi-Fi and sensor data accumulated while users access a positioning application. Using the collected sensor data, the device trajectory is regenerated, and a Wi-Fi ranging module is optimized to generate the shape of the estimated trajectory using Wi-Fi, similar to that obtained from sensors. In this process, the ranging module learns the way to identify the channel conditions from each Wi-Fi access point (AP) and produces ranging results accordingly. Furthermore, we collect the channel state information (CSI) from beacon frames to investigate the benefit of using CSI in addition to received signal strength measurements. With the CSI, the ranging module can identify diverse channel conditions from each AP and more accurately generate the reliability of each distance estimate to achieve accurate positioning results. The effectiveness of the proposed learning technique is verified using a real-time positioning application implemented on a PC platform. [ABSTRACT FROM AUTHOR]

In this research, we examine a spatially homogeneous anisotropic locally rotationally symmetric (LRS) Bianchi type-I cosmological model in f (R , T) gravity, for some choices of the function f (R , T) = f 1 (R) + f 2 (T) (arXiv:1104.2669 [gr-qc]). The precise solutions of the field equations are introduced for the periodic-time varying deceleration parameter that can adjust to meet the Λ CDM model. We have reviewed the physical and dynamic characteristics of these models to understand the accelerated expansion of the Universe in the current era. Furthermore, SNe Ia union data (arXiv:0811.2379 [astro-ph]), SN Ia with BAO and CMB observations (arXiv:1203.3213 [astro-ph.CO]) and OHD + JLA data (arXiv:1711.03437 [astro-ph.CO]) are used to estimate the statefinder diagnostics to get the geometric dynamics of the Universe at distinct stages and also to find the luminosity distance, angular-diameter distance, and distance modulus. We have also investigated the transit points for the deceleration parameter (q) in the framework of these three observations within the range of redshift 1. 2 0 ≤ z ≤ 1. 6 5. [ABSTRACT FROM AUTHOR]

In harmonic radar (HR), the transmitter illuminates a nonlinear target (the tag), causing the return signal to consist of harmonics at multiples of the transmitted carrier frequency. Of them, the second harmonic is usually the strongest and the one to which the receiver is tuned. This frequency difference distinguishes the tag reflection from environmental clutter, which remains at the illuminating frequency. However, the passive nature of HR tags severely limits the reflected power, and therefore the operational range of a HR system. We propose to increase the range and/or signal to noise ratio (SNR) by novel restructuring at the physical and signal levels. For this, we accompany the original transmitter with auxiliary transmitters able to send simple tones that are synchronized to arrive at the tag in phase, and we design the receiver to detect an intermodulation component. The resulting range and SNR are much greater than those of the original, conventional HR system, even if the original system were to transmit with power equal to the aggregate power of the proposed system. Achieving mutually coherent, (in phase), arrival of the tones at the tag is the focus of the present paper. We provide a system framework that models the tag, then present the adaptive phase coherence algorithm and analyze the probabilistic growth of the output signal power. We also account for the effects of frequency shifts due to transmitter mobility and the frequency offset errors in the transmitter local oscillators. [ABSTRACT FROM AUTHOR]

The Stretched-FrOnt-Leg (SFOL) pulse is a high-accuracy distance measuring equipment (DME) pulse developed to support alternative positioning and navigation for aircraft during global navigation satellite system outages. To facilitate the use of the SFOL pulse, it is best to use legacy DMEs that are already deployed to transmit the SFOL pulse, rather than the current Gaussian pulse, through software changes only. When attempting to transmit the SFOL pulse in legacy DMEs, the greatest challenge is the pulse shape distortion caused by the pulse-shaping circuits and power amplifiers (PAs) in the transmission unit such that the original SFOL pulse shape is no longer preserved. This letter proposes an inverse-learning-based DME digital predistortion method and presents successfully transmitted SFOL pulses from a testbed based on a commercial legacy DME that was designed to transmit Gaussian pulses. [ABSTRACT FROM AUTHOR]

Pedestrian Dead Reckoning (PDR) is a good alternative positioning method in Global Navigation Satellite Systems (GNSS)-challenged environments. It has the advantage in terms of infrastructure-independent, while the disadvantage is that, the estimated errors grow rapidly with time. The Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong transmission power, which contributes to better urban propagation and building penetration than GNSS signals. So, in this paper, an enhanced PDR algorithm aided with DTMB signals is proposed. In the study, a complete software-defined radio receiver is developed to estimate the Doppler speed and range information from DTMB signals. To reduce the accumulated errors of the PDR, the Extended Kalman Filter (EKF) algorithm is carried out to fuse the information of Doppler speed and ranging from the DTMB signals, as well as the information of walking speed and heading from the PDR. The results of the field tests showed that the 95% positioning error of the proposed algorithm is less than 3.94 m, while the PDR only is less than 9.19 m, which suggests the effectiveness of the proposed method to provide continuous positioning by fusing DTMB signals with PDR. [ABSTRACT FROM AUTHOR]

As the accuracy of satellite laser altimetry is susceptible to real-time atmospheric conditions, within footprint topography fluctuation, detector noise, and so on, we proposed a method by comprehensively analyzing the laser ranging error and evaluation labels to extract high-accuracy elevation control points that is suitable for satellite imagery-based topographic mapping applications. Using the ICESat laser altimetry data, a global high-accuracy laser altimetry dataset, including more than 60 million control points, based on the laser altimetry ranging error model and waveform quality analysis is proposed in this article. For land areas, except for areas of water, snow/ice, and polar ice sheets, the dataset can provide the elevation control points for worldwide satellite topographic mapping using high spatial resolution imageries or other science research that depend on accurate Earth’s elevation information. We further used airborne LiDAR data from six study areas around the world to carefully validate the datasets’ accuracy. The results showed that this dataset can meet the accuracy requirement of global mapping using high spatial resolution satellite imageries in terrains with a slope below 25°. Compared to the raw dataset, the proportion of footprint elevations that conform to the accuracy standard (0.5 m @ slope < 2°, 1.5 m @ 2° $\le $ slope < 6°, and 3 m @ $6^{\circ } \le $ slope $< 25^{\circ }$) is increased from 68.24%, 59.97%, and 26.52% to 87.58%, 90.04%, and 83.91%, respectively. This method can assure that its extracted results’ accuracy is either very close to or better than that obtained by the methods proposed in relevant studies, with a much larger number of laser footprints (LFs) that have been reserved. [ABSTRACT FROM AUTHOR]

Optical resonances of a metallically coated wave-chaotic microcavity are investigated. Mode resonances with a low-contrast quality factor and distinct field patterns are demonstrated. Unlike the resonances in the dielectric chaotic-wave cavity where the refractive escape leads a high optical loss, the resonances in metallically coated cavity hold high quality factors with a low loss induced by metal dissipation. As a potential application, the metallically coated chaotic-wave microcavity could be used for a semiconductor laser source with dense modes lasing and accordingly a low spatial coherent emission. [ABSTRACT FROM AUTHOR]

Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras. The stereo vision system is generally composed of two cameras to mimic the human binocular vision. In this paper, a Python‐based algorithm is proposed to find the parameters of each camera, rectify the images, create the disparity maps and finally use these maps for distance measurements. Experiments using real‐time images, which were captured from our stereo vision system, of different obstacles positioned at multiple distances (60–200 cm) prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate. The accuracy of distance measurement is up to 99.83%. The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s. [ABSTRACT FROM AUTHOR]

The achievement of accurate and reliable localization is invariably a predominant bottleneck to the realization of shearer automation. Unfortunately, the positioning accuracy may significantly degrade and the yield may be unsatisfactory due to the existence of the line-of-sight (LOS), non-line-of-sight (NLOS) along with mixed LOS-NLOS situations in complicated under-ground environments. To address this issue, this article proposes a novel localization algorithm called GMM-NNIMM-CLMFO, which is a combination of the Gaussian mixture model (GMM), neural network (NN)-based interacting multiple model (NNIMM), Caffery localization (CL), and moth-flame optimization (MFO). The GMM is first used to re-estimate the range, and the variational Bayesian cubature Kalman filter with the assistance of NN under the IMM framework is then respectively employed to diminish the LOS and NLOS errors. Then, based on the result of GMM-NNIMM, the CL method is used to compute the position coordinates of the target node. Finally, the MFO algorithm is implemented to optimize the positioning outcome sourced from the GMM-NNIMM-CL method. The proposed method is evaluated by using P440 sensors, and the experimental results demonstrate that the proposed technique is able to remarkably enhance the overall positioning accuracy, and outperforms other approaches. It can therefore efficiently alleviate the influence of NLOS errors and achieve more accurate position estimation, thereby manifesting better positioning performance and higher robustness in mixed LOS/NLOS situations. [ABSTRACT FROM AUTHOR]

In the article, three types of proximity sensors that might be used in bicycle rangefinder to measure the distance between the bicycle and an overtaking car are compared. The influence of various factors on the accuracy of the distance measurements obtained using ultrasonic, infrared and laser sensors is tested, among others, light conditions, car surface type and colour, rain, pollination and vibrations.