Miracle-dz/UniFAR-FTUs huggingface dataset:
An ANN-based Real-time Unstructured Road Detection Approach under Time-varying Illumination

Road detection is a key problem in the application of autonomous driving and navigation. However, most of the recent approaches only obtain reliable results in certain well-arranged scenes. In this paper, we propose an online-learning road detection method for robust and real-time unstructured road detection in challenging scenes. Firstly, we describe an improved adaptive gamma correction method compensate for nonuniform illumination under rapidly changing illuminate conditions. Then we divide the image with a grid into suitable sized rectangular regions (cells), and some few cells are labeled as train/test samples. Finally, we adopt the online learning process through artificial neural networks so that our method can be adaptive to the varied environment. Experiments on the challenging database demonstrate the real-time capability, adaptability and reliability of our approach to varied illumination and complex scenarios.
Who Will Continue Using MOOCs in the Future? Personality Traits Perspective

Personality traits may play a significant role in understanding human-computer interactions. This research adopted the personality traits perspective to explain the variances in the intention to continue using MOOCs (ICM), taking motivational and cultural aspects into account. The research used system justification and psychological reactance theories to hypothesize these relationships. It analyzed professional adults from a High-Power Distance context (i.e., Saudi Arabia, n=212) and a Low Power Distance context (i.e., Spain, n=136) using Structural Equation Modelling. Power distance comes as a negative moderator on the impact of internal motivation in the ICM and of extraversion in ICM, and the mediated effect of the internal motivation on the relationship between agreeableness and ICM. However, it comes as a positive moderator on the effect of extraversion in internal motivation and the effect of conscientiousness in the ICM. This research has several implications regarding the design of MOOCs for different personalities in dissimilar cultures.
Coexistence and Performance Limits for the Cognitive Broadband Satellite System and mmWave Cellular Network

The application of millimeter wave (mmWave) spectrum for the next generation broadband communications has received significant attention recently. In this work, we investigate the performance limits for the coexistence of the geo-stationary orbit (GEO) broadband satellite networks and terrestrial mmWave cellular networks. Based on the assumption that the statistical channel state information (SCSI) is available at the terrestrial base station (BS), we propose a virtual uplink based transmit beamforming (BF) algorithm to maximize the ergodic capacity of the terrestrial user while satisfying the interference probability constraint of the satellite users. Furthermore, we derive the closed-form expressions for the outage probability (OP) and ergodic capacity (EC) of the terrestrial user. Besides, the asymptotic OP expression at high signal-to-noise ratio (SNR) is also developed in terms of diversity order and array gain of the terrestrial user. Finally, numerical results are given to confirm the validity of the proposed BF scheme and theoretical derivations, and reveal the impact of key parameters on the performance of the terrestrial user coexisting with the multiple satellite users.
Accurate Vigilance Detection During Gait by Using Movement Artifact Removal

Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a p<0.05). Further, this study explored motion artifact removal and applied machine learning methods. Results reveal the vigilance detection during walking has a comparable performance to the stationary state when the motion artifacts are estimated and removed.
A Brief Review on Loop Closure Detection with 3D Point Cloud

A robust loop closure detection (LCD) in simultaneous localization and mapping (SLAM) can lead to more consistent and accurate map, which is a key part of SLAM. Currently, there are two main kind of data sources used for loop closure detection, the 2D image or 3D points. The 2D image-based LCD method has been extensively studied and many literatures have been published. However, this kind of method is sensitive to the illumination, the change of weather conditions and season et al. With the rapid development of self-driving technique, LiDAR-based SLAM has attracted many researchers because of its advantages. In this paper, we make a summarization on the recent developments of LCD method with 3D data source, especially with 3D point cloud input, and classify these methods into three groups, point feature based, segmentation/object based, and learning-based methods. For each group, the typical references are introduced. For the feature based method, four kind of features for LCD are introduced. Based on these introduction, the readers can get an overview on these LCD methods and even their working principles. Some discussions and the future trend on LCD with 3D point are also presented.
An Efficient Extraction Approach of Heart Rate Variability from Real-time Smartphone Videos

Heart rate variability(HRV) is widely used to autonomic nerves system assessment and cardiac disease diagnosis in clinic. The traditional HRV analysis is based on Electrocardiogram (ECG). However, it is more convenient to measure HRV by Photoplethysmography (PPG) in most wearable applications, such as watches. This paper proposed a novel method called Temporal Difference Interval Pixels(TDIP) method to obtain PPG signal through smartphone camera. In the experiment, we compared the cost time of obtaining PPG between traditional Temporal Difference(TD) method and TDIP method. We used these two methods to obtain PPG from 5 minutes' smartphone videos, which was acquired from 10 subjects. The average cost time of these two methods are 152.72 seconds and 82.38 seconds respectively. Then, we compared the time domain parameters the standard deviation of NN intervals (SDNN)and the Root mean square of successive differences between NN intervals (RMSSD) obtained by the proposed method with that obtained by ECG. The Pearson coefficient is 0.91 for SDNN and 0.95 for RMSSD respectively. The result can meet the HRV accuracy requirement, and it also significantly reduces the amount of calculation to improve the real-time performance.
Integrative practices in hospitals and their impact on patient flow

Purpose – The aim of this paper is to investigate which integrative planning and control practices are used in hospitals and what their effects are on patient flow.Design/methodology/approach – The study is based on a three‐hospital multi‐case study carried out in The Netherlands. The main findings are based on over 40 in‐depth interviews and the analysis of detailed patient flow data. The analysis of the flow data is used to explore the effects of integrative practices on lead times and patient flow.Findings – Based on the various patient groups examined in the different hospitals, four integrative practices stand out: sharing waiting list information, sharing planning information, cross‐departmental planning, and combining appointments. In line with earlier studies, the overall level of integration in hospitals was found to be low. However, patient flow performance is significantly better in those hospitals that employ more of the above‐mentioned integrative practices.Research limitations/implications –.
New Conditions of Analysis and Synthesis for Periodic Piecewise Linear Systems With Matrix Polynomial Approach

In this paper, new conditions of the stability, stabilization and <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-gain performance of periodic piecewise systems are proposed. Both the continuous and discontinuous Lyapunov functions with dwell-time related time-varying Lyapunov matrix polynomial are adopted, and methods guaranteeing the positive and negative definiteness of a matrix polynomial are introduced. Exponential stability conditions are derived based on continuous and discontinuous Lyapunov functions, respectively. A stabilizing controller with time-varying controller gain is designed with continuous Lyapunov function and the weighted <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-gain performance based on the discontinuous Lyapunov matrix polynomial is studied as well. Numerical examples are used to verify the effectiveness of the proposed methods.
Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion

Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as “RRT by largest available zone” (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied.
Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle.
A Cluster Algorithm for Road Surface Condition Data Constrained by a Road Network

Up-to-date maps of road surface conditions are essential for future vehicles and pavement management systems to ensure driving safety and comfort. There are different approaches to measure road properties like obstacles or unevenness automatically and to share these information by a global network. This paper presents a workflow to cope with complex cluster shapes in a road network. Therefore, the analysis is performed at first on each single road segment (RS) and finally on contiguous RSs. The hypotheses obtained by clustering have to pass a series of verification steps to gain trust. For this purpose, a statistical analysis and a comparison of different alternative hypotheses are used to identify ambiguities in analysis results. The improvements achieved by this will be shown for an example with measurements for several obstacles at an intersection.
A Novel Particle Filter Based SLAM Algorithm for Lunar Navigation and Exploration

This paper presents a simultaneous localization and mapping (SLAM) system for lunar exploration. The proposed SLAM algorithm presents a significantly lower computational complexity compared to the state-of-the-art solutions, due the use of a Rao-Blackwellised particle filter with adaptive and compound resampling. The proposed SLAM sensor system consists of one light detecting and ranging sensor (LIDAR) and one IMU, to minimize illumination-dependent errors; as the lunar environment, and in particular the target exploration region around the Marius Hills hole, presents very variable illumination conditions. The system was tested via simulation, using existing environmental data from the mare tranquillitatis pit crater.
Density Adaptive Approach for Generating Road Network From GPS Trajectories

Road networks are fundamental parts of intelligent transportation and smart cities. With the emergence of crowdsourcing geographic data, road mapping approaches by using crowdsourcing trajectories have been developed. Existing road map inference algorithms from trajectories can extract relatively accurate road networks, however, these algorithms are not robust to different trajectory datasets and the parameter optimization task is tedious and time-consuming. Therefore, we propose an adaptive approach based on trajectory density. The proposed approach contains two stages. Firstly, the density distribution for each trajectory is adaptively estimated by the Gaussian fitting approach and the density peak points are extracted to construct road centerlines corresponding to each trajectory. Secondly, these extracted road centerlines are incrementally merged by the “matching-refinement-merging” process to generate a road network. We compare the proposed approach against four representative methods through trajectory datasets that are completely different in sampling frequency, trajectory density, road density, and noise. The results show that the proposed approach provides better accuracy in terms of precision and integrity and does not require additional parameter adjustment.
Exploiting Location-Based Context for POI Recommendation When Traveling to a New Region

Traveling to a new region has become a very common thing for people, due to work or life requirement. With the development of recommendation engine and the popularity of social media network, people are more and more used to relying on personalized Points-of-Interest (POI) recommendations. However, traditional approaches can fail if users moves to a region where they had little or no active history or even social network friends information before. Under the requirement of smart city construction, the need to give high quality personalized POI recommendation when a user travels to a new region has arisen. Fortunately, with the widespread of wireless Internet, the booming of Internet-of-Things (IoT) and the common-usage of location sensors in mobile phones, the coupling degree between social media networks and location information is ever increasing, which could leads us to a new way to solve this problem in the ear of Big Data. In this research, we presented New Place Recommendation Algorithm (N-PRA) which is designed based on Latent Factor model. Many different types of social media contexts (time-related and location-related), such as a user’s interest fluctuation, different types of POIs’ popularity fluctuation, types of POIs, the influence of geographical neighborhood on POIs, and user’s social network friendship are taken into consideration in this approach. The algorithm presented is verified on Yelp, an open-source real urban data-set, and compared against several other baseline POI recommendation algorithms. Experimental results show that the algorithm presented in this paper could achieve a better accuracy.
Research on Capsule Network Optimization Structure by Variable Route Planning

Convolutional Neural Networks (CNN) perform quite good on image classification and object recognition tasks. To solve the problem that CNN is not robust to affine transformation and can not consider the spatial relationship between objects in images, Capsule Network (CapsNet) is proposed and the best accuracy of MNIST dataset has achieved. It shows developability on other datasets and its performance is significantly higher than CNN. However, the data categories in the existing routing algorithms of the CapsNet need to be given in advance, which is very difficult to estimate. In a complex dataset or a small sample dataset, calculating the maximum likelihood solution is more complicated, and it will lead to an inevitable singularity problem. For these shortcomings of the routing algorithm, this paper first proposes a variational routing algorithm. The algorithm does not bring additional computational burden, can automatically determine the most suitable data category, and can effectively avoid over-fitting problems. The algorithm uses the variational distribution to integrate the prior distribution information to increase the regularization limit, maximize the lower bound of the model evidence, and avoid the singularity problem caused by computing the maximum likelihood function. In this paper, the MNIST dataset is classified by a simple CapsNet architecture, and the result is encouraging.
A systematic review of adherence measurement methods currently used in randomised controlled trials of home-based rehabilitation interventions

www.nmahp-ru.ac. uk Documenting participant adherence in clinical trials is paramount for assessing the extent to which effectiveness is dependent on the intervention dose. This is particularly vital in home-based therapies where greater patient commitment is required. Despite this, there are few evidence-based evaluations of the methods used and no rigorous guidance as to the which is the optimum method in a given intervention.
Error Correction in Pitch Detection Using a Deep Learning Based Classification

While pitch detection has been the focal subject of numerous research efforts for several decades, it is still a challenging task in noisy conditions. In this article, we propose a method to improve the pitch detection accuracy of conventional pitch detection methods. The proposed pitch detection process starts with using the pitch value estimated by a conventional pitch detection method. Then, it extracts pitch candidates according to the most probable types of errors in the initial estimation of high-pitch and low-pitch frames classified by a Deep Convolutional Neural Network (DCNN). Next, a restrained selection procedure is run to find the true pitch value from the set of pitch candidates. In this procedure, we employ two features (harmonic summation and Euclidian deviation), the soft decision of the DCNN, the pitch smoothness feature in successive frames, and the effect of the initial estimation in a cost function. The pitch value which leads to the lowest cost value is chosen as the estimated pitch value. The simulations on CSTR and KEELE databases, in noisy environments with twelve types of noise, were performed. The results show the superiority of the proposed method over the state-of-the-art methods under different SNR conditions.
Literature Review of Energy Efficient Transmission in Wireless LANs by Using Low-Power Wake-Up Radio

Power consumption is a key consideration in every WLAN MAC protocol design for wireless devices and extending battery life requires more efficient power management scheme considering that Carrier sensing by WLAN modules consumes enormous amount of power. Researchers over the years have proposed and implemented various schemes using a low-power Wake-up Radio for carrier sense which has proven to be effective. In this paper, a comprehensive literature review of the research progress in WuR-based energy saving is presented. This paper compares the various protocols already proposed for mitigating latency and energy consumption in WLAN networks and provides synthesis of wake-up radio (WUR) based carrier sensing approaches with analysis and discussions of merits and limitations of each technique. The operation principles of duty-cycle and wake-up radio bases MAC protocols were looked into in relation to how they affect energy efficiency and latency reduction.
Actor-Critic Method-Based Search Strategy for High Precision Peg-in-Hole Tasks

In the field of 3C(Computer/Communication/Consumer Electronic) product assembly, Peg-in-hole task, such as fiber assembly, is widely used. However, it remains as a big challenge for robots to automatically execute peg-in-hole tasks. Building a contact model is the traditional idea, which requires lots of time and effort. However, the model suffers low accuracy in the situation with tighter clearance. Currently, the most learning-based methods do not take into account the particularity of such assembly tasks, which lead to slow convergence. In this paper, we propose a new search strategy based on reinforcement learning for high precision peg-in-hole assembly tasks. The assembly task is divided into two steps: search and insert. Afterwards, a Markov Decision Process (MDP) is designed for the two steps according to different assembly features and solved by an Actor-Critic method. The robot can learn how to choose the optimal action and accomplish peg-in-hole task with less training and execute steps, high success rate and smaller contact force. Moreover, the proposed method can be applied to the multi-hole task without retraining. The results of simulation and experiment demonstrate its fast and stable performance.