Distance Estimation Method Research Articles

A19 The origin of acute HCV in HIV-infected men who have sex with men in the Netherlands

In recent years, several outbreaks of hepatitis C virus (HCV) infections have been observed among HIV-infected men who have sex with men (MSM) in Europe. In the Netherlands, high incidence rates of 10/1,000 person-years are reported. In this analysis, we describe whether acute HCV is linked to specific transmission networks. A total of 50 Dutch HIV-infected MSM, diagnosed with acute HCV genotype 1a between 2013 and 2014, were included. Target enrichment for viral nucleic acid separation and deep sequencing were used to recover whole HCV genomes. Phylogenetic trees were constructed by use of the maximum likelihood method. Pairwise distance matrices were generated by use of the Kimura-2 parameter distance estimation method. The consistency of the phylogenetic clustering was tested by bootstrap analysis with 1,000 replicates. A cluster was defined as having a genetic distance of at most 1.5 per cent and bootstrap values of 100 per cent. The most recent common ancestor was estimated with a coalescent-based model with a Bayesian statistical framework. Four transmission clusters were identified that included a total of 38 patients (76% of the total). The clusters were indicative of recent outbreaks, as highlighted by small genetic distances and a most recent common ancestor after the year 2000, when the first cases of HCV infection in HIV-infected MSM were reported. The HCV epidemic among HIV-infected MSM is a young epidemic, with most of the acute infections linked within the four major transmission networks. Prevention strategies identifying and targeting these transmission networks can potentially curb the epidemic.

A Novel Method for Estimating Distances from a Robot to Humans Using Egocentric RGB Camera.

Estimating distances between people and robots plays a crucial role in understanding social Human–Robot Interaction (HRI) from an egocentric view. It is a key step if robots should engage in social interactions, and to collaborate with people as part of human–robot teams. For distance estimation between a person and a robot, different sensors can be employed, and the number of challenges to be addressed by the distance estimation methods rise with the simplicity of the technology of a sensor. In the case of estimating distances using individual images from a single camera in a egocentric position, it is often required that individuals in the scene are facing the camera, do not occlude each other, and are fairly visible so specific facial or body features can be identified. In this paper, we propose a novel method for estimating distances between a robot and people using single images from a single egocentric camera. The method is based on previously proven 2D pose estimation, which allows partial occlusions, cluttered background, and relatively low resolution. The method estimates distance with respect to the camera based on the Euclidean distance between ear and torso of people in the image plane. Ear and torso characteristic points has been selected based on their relatively high visibility regardless of a person orientation and a certain degree of uniformity with regard to the age and gender. Experimental validation demonstrates effectiveness of the proposed method.

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine

This study proposes an adaptive multi-team perturbation guiding Jaya (AMTPG Jaya) algorithm. The proposed approach uses multiple teams to explore the search space. Depending on the percentage of consumed function evaluations, the AMTPG Jaya algorithm adjusts the number of teams in the search. Furthermore, all teams move from a single population set and are simultaneously guided by different perturbation equations to different regions of search space. As each team has a different perturbation scheme, the set of moves to new positions by each team is unique. During the search process, the perturbation equations are exchanged depending on the quality of the solutions produced by them. The proposed algorithm employs dominance principles and the crowding distance estimation method to handle the multiple objectives simultaneously. The proposed algorithm is examined using two multi-objective optimization case studies of a solar dish Stirling heat engine system and a multi-objective optimization case study of the Stirling heat pump. Also, the Technique for Order of Preference by Similarity to Ideal Solution decision-making method is employed for identifying an optimal solution. The computational results obtained by the proposed AMTPG Jaya algorithm are superior to those achieved by the other algorithms presented in this work.

Enhancing resilience analysis of power systems using robust estimation

It has been well-recognized that the distribution of the blackout size of a power grid system has a heavy tail. The power-law distribution is a popular model for the heavy-tail phenomenon, and it is widely used in power system disruptions. However, there are significant reporting errors in the disruption data reported in public available databases, such as the database of the Electric Disturbance Events (OE-417) maintained by the US Department of Energy. Traditional inference techniques such as the maximum likelihood estimation can be sensitive to such contaminated data due to the reporting errors. In this paper, we propose a robust estimation procedure for the power-law distribution based on the minimum distance estimation method. A comprehensive simulation is used to evaluate the performance of the proposed method, and compare the performance with the existing maximum likelihood method. It is found that the proposed method outperforms the existing maximum likelihood method in the presence of contaminated data. We apply the proposed method to the blackout data from Jan 2002 to Aug 2016 based on the OE-417 database.

Distance Estimation of Monocular Based on Vehicle Pose Information

In order to measure the distance between our vehicle and the target vehicle by monocular vision and eliminate the estimation error bring by changing of vehicle pose, we propose the distance estimation method based on the vehicle pose information, which can be used to eliminate the error of distance estimation effectively cause by the change of the pitch angle and roll angle of unmanned vehicle. In addition, the pose information could also help us estimate whether the vehicle is in a slope, thus engage in distance estimation for the vehicles. Several groups of data was collected for the experiments. And the result prove the validity of the algorithm in distance estimation.

Combination of Metal Shielding and Distance Estimation for Electromagnetic Compatibility Guarantee

In this paper, a solution for electromagnetic compatibility guarantee based on the combination of metal shielding and circuit components distance estimation methods is presented. The electromagnetic noises generated from a working radio-electronic unit can expand into the space and act on other around radio-electronic units. An EMC guaranteed radio-electronic unit by the suitable technique method will not cause the electromagnetic noise to others. In opposition, it will not be under electromagnetic action from another one. Due to the power of electromagnetic noise, the metal shielding, distance estimation or other technique methods should be used to guarantee EMC. Every method has own advantage as so as weakness for detail radio-electronic unit, so it is necessary to choose a suitable method to guarantee EMC for them, the combination of metal shielding and distance estimation is a choice, for example. The proposed solution has been evaluated by using CST (Computer Simulation Technology) software and EMxpertEHX analyzer in oscillator circuit context. The simulated results on CST show that the proposed solution decreases the electromagnetic radiation about of 39.1 dB at frequency 500 MHz in comparison to results when nothing electromagnetic compatibility methods are not used. The experimental results on the oscillator circuit are presented. The electromagnetic radiation reduction of the oscillator circuit is about of (25 - 30) dB. In comparison to individual metal shielding and distance estimation methods, the effectiveness of the proposed solution for electromagnetic compatibility guarantee is significantly increased.

Vision‐based inter‐vehicle distance estimation for driver alarm system

Determining the position of the vehicles on road is an important information in the driver assistance system which is used to increase driver's safety. The authors propose a robust real-time vehicle detection and distance estimation method for vision-based driving assistance system. They detect the vehicles in front of their own and estimate their three-dimensional position by using a single monochrome camera. After detecting the preceding vehicles using the common method, they use a new method for distance measurement based on monocular camera to estimate the inter-vehicle distance. The method also provides the longitudinal distance information for the driver alarm system. Finally, they utilise the Kalman framework to update the information of the vehicle which they detected before, the information includes horizontal distance, vertical distance and speed, and use these information to predict risk. Experimental results indicate that the system correctly detects vehicles in real time and effectively estimates the position of preceding vehicles.

Intervehicle distance estimation through camera images

Vehicles have evolved into synthetic engineering systems owing to the developments in mechanical, electronic, and information technologies. Recently, driving assistance systems have gained considerable attention and are installed in vehicles under development by manufacturers. An important technology for the driving assistance systems is intervehicle distance measurement, for which several technologies have been proposed through hardware and software developments. The intervehicle distance measurement generally requires auxiliary sensor systems, complex algorithms, and high costs. An intervehicle distance estimation method is proposed, which calculates the distance using images obtained through a camera installed in the vehicle. For calculating the distance, the proposed method does not need any other physical sensors or information about the roads on which the vehicle moves. For estimating the intervehicle distance, the proposed method takes advantage of the fact that the width between two adjacent lanes on the road is constant. The difference in the distance calculated using the proposed method and the actual distance is <7 % .

Pseudo-photometric distances of 30 open clusters

Aims. We demonstrate that reliable photometric distances of stellar clusters, and more generally of stars, can be obtained using pseudomagnitudes and rough spectral type without having to correct for visual absorption. Methods. We determine the mean absolute pseudomagnitude of all spectral (sub)types between B and K. Distances are computed from the difference between the star’s observed pseudomagnitude and its spectral type’s absolute pseudomagnitude. We compare the distances of 30 open clusters thus derived against the distances derived from TGAS parallaxes. Results. Our computed distances, up to distance modulus 12, agree within 0.1 mag rms with those obtained from TGAS parallaxes, proving excellent distance estimates. We show additionally that there are actually two markedly different distances in the cluster NGC 2264. Conclusions. We suggest that the pseudomagnitude distance estimation method, which is easy to perform, can be routinely used in all large-scale surveys where statistical distances on a set of stars, such as an open cluster, are required.

Exploiting Sensed Radio Strength and Precipitation for Improved Distance Estimation

The received signal strengths (RSSs) of wireless radios are susceptible to environmental dynamics, such as weather conditions, which can severely degrade the performance of RSS-based distance estimators. This paper proposes an enhanced RSS-based distance estimator that accounts for precipitation. The rain attenuation effect is incorporated into the path loss model to derive an analytical expression for maximum-likelihood-based distance estimation by using an iterative form of Newton’s method. In addition, the Cramer–Rao lower bound for the proposed distance estimator is proposed. This study performed experiments on the mobile networks at a campus, sensing the actual RSS using smartphones and deploying local weather stations to record transmission conditions. This is a pioneer work to overcome the rain attenuation problems and real measurements verify the effectiveness of an RSS-based distance estimator. Simulation and experimental results indicated that the proposed algorithm provides robust distance estimation during the periods of precipitation. The parameters of radio propagation and rain rate models can be extracted offline. With these parameters and the real-time measured RSSs and rain rate, our proposed distance estimation method achieves an 85%–90% error reduction rate over the conventional approach.

Measuring the absolute distance of a front vehicle from an in-car camera based on monocular vision and instance segmentation

The requirements of distance measurement have increased with the development of auto vehicle driving. Traditional methods for distance estimation require the complex calibration from intrinsic and external parameters of the camera. Recent methods based on the neural network structure mainly measure the relative depth of whole images. We adopt monocular vision with instance segmentation and camera focal length to detect the absolute distance of front cars from in-car cameras. First, we extract the location of the cars from the object detection network. Second, the location of cars is sent to the vehicle classification network and instance segmentation network to obtain the type of the cars and their mask value. Here, we use a model trained by the CompCars dataset to classify car types, and we train a new instance segmentation model using the Cityscapes dataset to obtain each car’s mask. Third, in accordance with the camera imaging principle, the absolute distance of cars in the images is calculated based on the relationship between the size information of different car types and their mask values. The proposed method is examined with the KITTI dataset, and the experiment shows that its results can be close to the ground truth. Moreover, the proposed method uses the instance segmentation network to reduce complexity of the depth estimation process and it can still generate satisfactory results even when cars are partly occluded.

Node localization with AoA assistance in multi-hop underwater sensor networks

This paper proposes a novel node localization method in sparse underwater wireless sensor networks (UWSNs) where the locations of only a small number of anchor nodes are available. The proposed method estimates the Euclidean distances from anchor nodes to multi-hop sensor nodes with the help of angle of arrival (AoA) measurements in the local coordinate systems of the routing nodes. A new distance estimation method is proposed for sensor nodes with greater-than-two-hops to an anchor node by accurately estimating the rotation matrix between the routing nodes. By forwarding distances hop-by-hop through the network, the sensor nodes are able to obtain distance estimates to more than four or five anchor nodes. Then the location of the sensor node is solved by a weighted Least Squares method. This paper develops a practical table of weights for different number of hops and AoA estimation errors. Simulation results show that the proposed localization method outperforms the existing multi-hop localization algorithms, such as DV-hop, DV-distance, Euclidean, and Cosine-law methods, in terms of distance estimation and location estimation, even if the AoA measurement error is large. Meanwhile, the proposed method maintains almost the same high localization coverage as the existing methods.

BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities

While the Internet of Things (IoT) is driving a transformation of current society toward a smarter one, new challenges and opportunities have arisen to accommodate the demands of IoT development. Low power wireless devices are, undoubtedly, the most viable solution for diverse IoT use cases. Among such devices, Bluetooth low energy (BLE) beacons have emerged as one of the most promising due to the ubiquity of Bluetooth-compatible devices, such as iPhones and Android smartphones. However, for BLE beacons to continue penetrating the IoT ecosystem in a holistic manner, interdisciplinary research is needed to ensure seamless integration. This paper consolidates the information on the state-of-the-art BLE beacon, from its application and deployment cases, hardware requirements, and casing design to its software and protocol design, and it delivers a timely review of the related research challenges. In particular, the BLE beacon’s cutting-edge applications, the interoperability between packet profiles, the reliability of its signal detection and distance estimation methods, the sustainability of its low energy, and its deployment constraints are discussed to identify research opportunities and directions.

Estimation of Aircraft Operations at Airports Using Mode-C Signal Strength Information

An understanding of aircraft operations counts at airports is important due to the use of that information in the allocation of the Federal Airport Improvement Program funds. Operations at airports lacking full-time personnel are typically sampled over time. The sample sizes are generally small, due to the inherent difficulty and expense of deploying acoustic or pneumatic counting devices for extended periods of time, leading to inaccuracies in the estimation of long-term totals. A means of counting operations using Mode S extended squitter (ES) aircraft transponder data received with a 1090-MHz software-defined radio (SDR) system has been developed by the authors. A limitation of this approach, however, is the relatively small ratio of Mode S ES signals to Mode S short squit and Mode C signals, which do not contain aircraft position information. This article presents a method for estimating distance information from the latter two types of transponder signals, enabling them to be used by the SDR-based device, along with Mode S ES signals, in the operations counting process. The distance estimation method described here is based on an adaptive Kalman filter incorporating parameter optimization using known distance information from Mode S ES signals and results in an average error of 0.83 nm in measurements within a 5-nm radius of the receiver. This level of uncertainty enables the use of Mode S short squit and Mode C signals to count aircraft operations at airports without the additional overhead of multilateration.

Compounded inverse Weibull distributions: Properties, inference and applications

ABSTRACTIn this paper two probability distributions are analyzed which are formed by compounding inverse Weibull with zero-truncated Poisson and geometric distributions. The distributions can be used to model lifetime of series system where the lifetimes follow inverse Weibull distribution and the subgroup size being random follows either geometric or zero-truncated Poisson distribution. Some of the important statistical and reliability properties of each of the distributions are derived. The distributions are found to exhibit both monotone and non-monotone failure rates. The parameters of the distributions are estimated using the expectation-maximization algorithm and the method of minimum distance estimation. The potentials of the distributions are explored through three real life data sets and are compared with similar compounded distributions, viz. Weibull-geometric, Weibull-Poisson, exponential-geometric and exponential-Poisson distributions.

Precision evaluation method of measuring frequency modulated continuous wave laser distance

With the rapid development of industrial manufacturing, people are stricter and stricter for measuring accuracy and demanding for measurable objects. The demand for a new generation of industrial measurement has evolved from the cooperative target toward the diffuse surface object with faster measurement speed and higher precision. Frequency modulated continuous wave (FMCW) laser ranging technology has proved to be an efficient method in the high-precision ranging fields for absolute distance measurement of a diffuse reflecting target.However, its accuracy is subjected to the stability of continuous-wave light source which cannot scan frequency linearly, which further leads to the instability of beat frequency and poor spectrum resolution. Generally, this problem could be solved by the active linearization technique and the post-processing technique. The most popular method is the non-uniform interval resampling technique, which belongs to the post-processing scheme and uses the zero-crossings or peaks of a long delay Mach-Zehnder interferometer signal as triggers for acquiring the measurement signal data. This technique is low cost, easy to be integrated into FMCW ladar system, and especially suitable for short-range small-band scanning measurements. However, in the large-bandwidth long-distance measurement cases, due to the jitter and dispersion of a long fiber, the spectrum obtained by this method is deteriorated such as the spectral broadening and distance shifting, so the range position cannot be determined precisely. To improve the precision, the fast Fourier transform, chirp Z transform and the multiple signal classification methods are used to obtain the distance spectral information. There are also other methods to solve this problem, but there is no common precision evaluation method to test the validities of these methods.In this paper, a precision evaluation method of measuring the FMCW absolute distance based on two-fiber interferometer is presented. A lower Cramer-Rao lower bound on the variance of distance parameter of the resampled signal in the presence of noise is derived. It shows that the precision of absolute distance is affected by the signal-to-noise ratio of the system and chirp bandwidth. This result is verified experimentally.Besides, the proposed method is not restricted to any distance estimation algorithm. According to this boundary, an optimal distance estimation method could be chosen. Moreover, a simulation of range precision versus signal-to-noise ratio and bandwidth is also demonstrated. When the chirped bandwidth is equal to 20 nm and the signal-to-noise ratio of absolute distance measurement interferometer is raised to more than 70 dB, the obtained precision is below 1 μm. This method can provide a theoretical reference for improving the precision of FMCW distance measurement and it could be widely used in the future.

Position adjustment method and distance estimation method of magnetic field supply and detection unit for magnetic hyperthermia

In magnetic hyperthermia, the temperature and localization of a ferromagnetic implant in a tumor region is vital in determining the effectiveness of therapeutic heating. We have developed a noninvasive wireless temperature measurement method by utilizing the magnetic permeability property of a ferromagnetic implant with low Curie temperature (FILCT) that varies with the temperature. In clinical settings, when the FILCT is injected into a tumor region, the position of the FILCT is expected to deviate from the central axis of the heating coil (drive coil). When it deviates, the magnetic flux density applied to the FILCT decreases, thus causing difficulty in detecting the temperature of FILCT via a pickup coil and heating of the FILCT at a constant treatment temperature. Therefore, to adjust the magnetic field supply and detection (MFSD) unit to be directly above the FILCT, we propose a position adjustment method by referring to three voltages induced in three pickup coils symmetrically installed inside the drive coil. By using the average value of the three voltages, we also propose a method to estimate the distance between the MFSD unit and the FILCT, which is required for wireless thermometry and determination of the optimal energy applied. The proposed methods are experimentally examined by a verification system.

A novel distance estimation algorithm for periodic surface vibrations based on frequency band energy percentage feature

Abstract Earth surface vibration signal processing becomes attractive recently due to its significance in source detection and localization, which can be adopted in a number of real world applications, such as footstep detection, underground pipeline network surveillance, etc. In this paper, we investigate the distance estimation problem for earth surface periodic vibration signal localization. The signal attenuation principle between the propagation distance and the signal frequency is exploited and a novel frequency band energy percentage (FBEP) feature is developed to characterize the energy distribution property within different frequency bands of different propagation distances. To obtain the fundamental frequency of periodic vibrations, the cepstrum approach is employed. An enhanced computationally efficient k nearest neighborhood (EH-kNN) algorithm is developed to perform the distance estimation. Experiments on real periodic vibration signals generated by an electric hammer under different collecting distances and transmission medias are conducted to show the superiority of the proposed distance estimation method in this paper.

A fast algorithm for the coordinate-wise minimum distance estimation

ABSTRACTApplication of the minimum distance (MD) estimation method to the linear regression model for estimating regression parameters is a difficult and time-consuming process due to the complexity of its distance function, and hence, it is computationally expensive. To deal with the computational cost, this paper proposes a fast algorithm which makes the best use of coordinate-wise minimization technique in order to obtain the MD estimator.R package (KoulMde) based on the proposed algorithm and written in Rcpp is available online.