Miracle-dz/UniFAR-FTUs huggingface dataset:
Safe Path Planning with Gaussian Process Regulated Risk Map

Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.
Parkinson’s Disease Detection from Gait Patterns

Parkinson’s disease (PD) patients display abnormal gait patterns with impairments and postural instability. In this paper, we propose an automatic system for extracting gait parameters. Various features were extracted from force sensors and analyzed using a threshold-based algorithm and machine learning techniques with the objective to identify the most significant features that would best characterize the presence of the disease. A machine learning algorithm using support vector machine method was developed to identify the presence of the disease. The analyses of the results show that the machine learning algorithm has the best accuracy of 100% in distinguish between the two groups when looking at features based on stride, swing and stance phases.
Design of an Improved Implicit Generalized Predictive Controller for Temperature Control Systems

In this study, an implicit proportional-integral-based generalized predictive controller (PIGPC) is proposed to effectively control temperatures of industrial systems with time-varying delay. The controller is designed to optimize the target function with the proportional-integral structure for improving the controlling performance of implicit PIGPC. Meanwhile, the recursive least square method is leveraged to directly identify the parameters of controller. Compared with the conventional implicit generalized predictive controller, the process of parameter identification converges faster. The Lagrange multiplier method is introduced to solve the optimal control law of implicit PIGPC by considering the input constraints of system. An approximate decoupling controller for multivariable systems is proposed to eliminate the coupling effects. Simulation and experimental results manifest the effectiveness and feasibility of the proposed controller in temperature control systems.
Faster Parallel Core Maintenance Algorithms in Dynamic Graphs

This article studies the core maintenance problem for dynamic graphs which requires to update each vertex's core number with the insertion/deletion of vertices/edges. Previous algorithms can either process one edge associated with a vertex in each iteration or can only process one superior edge associated with the vertex (an edge <inline-formula><tex-math notation="LaTeX">$\langle u,v\rangle$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>〈</mml:mo><mml:mi>u</mml:mi><mml:mo>,</mml:mo><mml:mi>v</mml:mi><mml:mo>〉</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="hua-ieq1-2960226.gif"/></alternatives></inline-formula> is a superior edge of vertex <inline-formula><tex-math notation="LaTeX">$u$</tex-math><alternatives><mml:math><mml:mi>u</mml:mi></mml:math><inline-graphic xlink:href="hua-ieq2-2960226.gif"/></alternatives></inline-formula> if <inline-formula><tex-math notation="LaTeX">$v$</tex-math><alternatives><mml:math><mml:mi>v</mml:mi></mml:math><inline-graphic xlink:href="hua-ieq3-2960226.gif"/></alternatives></inline-formula>’ core number is no less than <inline-formula><tex-math notation="LaTeX">$u$</tex-math><alternatives><mml:math><mml:mi>u</mml:mi></mml:math><inline-graphic xlink:href="hua-ieq4-2960226.gif"/></alternatives></inline-formula>'s core number) in each iteration. Thus for high superior-degree vertices (the vertices associated with many superior edges) insertions/deletions, previous algorithms become very inefficient. In this article, we discovered a new structure called joint edge set whose insertions/deletions make each vertex's core number change at most one. The joint edge set mainly contains all the superior edges associated with the high superior-degree vertices as long as these vertices are 3<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="hua-ieq5-2960226.gif"/></alternatives></inline-formula>-hop independent. Based on this discovery, faster parallel algorithms are devised to solve the core maintenance problems. In our algorithms, we can process all edges in the joint edge set in one iteration and thus can greatly increase the parallelism and reduce the processing time. The results of extensive experiments conducted on various types of real-world, temporal, and synthetic graphs illustrate that the proposed algorithms achieve good efficiency, stability and scalability. Specifically, the new algorithms can outperform the single-edge processing algorithms by up to four orders of magnitude. Compared with the matching based algorithm and the superior edge based algorithm, our algorithms show a significant speedup up to 60× in the processing time.
Image Hazing Algorithm Based on Generative Adversarial Networks

Haze is an important factor in photography with a special aesthetic, emotional, or composi- tional meaning, an image hazing method is proposed based on generative adversarial network. The proposed network consists of two parts: the generator has a symmetric encoder and decoder structure with skip connection, which is used to generate hazy images; and the discriminator is a global fully convolutional network, which is used to identify the reality of the generated hazy images. The haze-free image is used as the input of the generative network to obtain a corresponding hazy image. Then the discriminative network judges the similarity between the original haze-free image and the corresponding hazy image to optimize the network parameters, which means that all the parameters of the entire network can be automatically learned through training. The loss function of the network is the combination of the GAN(Generative Adversarial Networks) loss and the REG(Regression) loss with a coefficient <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>. We design a REG loss which includes feature loss <inline-formula> <tex-math notation="LaTeX">$loss_{FL}$ </tex-math></inline-formula> and image loss <inline-formula> <tex-math notation="LaTeX">$loss_{IL}$ </tex-math></inline-formula>. By calculating the loss, a model mapping the relationship between the two corresponding pixels from the original image and the synthetic hazy image respectively is well trained. In the last part of the paper, we have shown some impressing synthetic hazy images obtained by our model as well as analyzing the effects on images with different scenes. Moreover, we have contrasted the experimental results of our algorithm with that of other classical ones. Experiments demonstrate that the proposed algorithm has a better performance than other state-of-the-art methods on both virtual-scene and real-world images qualitatively and quantitatively.
Review of Pavement Defect Detection Methods

Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail.
Human acute neurophysiological perceptions associated with ELF flux densities up to 50 mT

Current guidelines and recommendations limiting human exposure to extremely low frequency (ELF) magnetic field (MF) are based on the most reliable acute human neurophysiological exposure responses. However, the currently available perception thresholds are established on small sample size and non-replicated experiments. The current human study aimed to test MF levels leading to mahnetophosphenes perceptions at 20, 50, 60 and 100 Hz. Perception reported button-press were recorded in these 4 frequency groups in 55 randomly assigned magnetic flux density conditions. Results showed a frequency-dependent threshold for initial perception: 2.51 T/s at 20 Hz, 6.28 T/s at 50 Hz, 7.54 T/s at 60 Hz, and 12.57 T/s at 100 Hz; and for the 50% detection rate: 4.73 T/s at 20 Hz, 8.83 T/s at 50 Hz, 10.55 T/s at 60 Hz, and 33.46 T/s at 100 Hz. Since it is a linear correlate of the induced in-situ E-fields, the dB/dt metric was chosen to analyse the the data. It confirmed lower magnetophoshene perception at lower frequencies for a similar in-situ induced E-field.
Collaborative Robot Assistant for the Ergonomic Manipulation of Cumbersome Objects

Collaborative robotics refers to the cooperation between humans and machines and aims at improving productivity and at facilitating the worker in demanding tasks. The advantage of collaboration is to combine the superior cognitive and motor skills of the operator with the physical capabilities of the robots. This work presents a control strategy for the robotic manipulator to minimise the muscular fatigue of the human operator during the manipulation of bulky objects. The robot moves the workpiece so that the human is always operating close to his/her most natural and ergonomic posture. This way the risk of developing postures and movements inducing musculoskeletal disorders is minimised. A substantial reduction of the amplitude of the operator movements, without degrading the precision in fulfilling the task, has been registered in the experimental campaign.
A Novel Robust Approach for Correspondence-Free Extrinsic Calibration

Extrinsic calibration is a necessary step when using heterogeneous sensors for robotics applications. Most existing methods work under the assumption that the prior data correspondence is known. Considering data loss and false measurements, the correspondence may not be accessible in practice. To solve this problem without knowing the correspondence, several probabilistic methods have been proposed. However, an implicit restriction on input data limits their application. Therefore, in this paper, we propose a more stable correspondence-free method with two improvements that can relax the restrictions on inputs and improve the calibration accuracy. The first improvement finds consistent sets from raw inputs using screw invariants, which significantly improve the robustness in case of outliers and data loss. A new optimization method on matrix Lie group is proposed as the second improvement, which demonstrates better accuracy. The experimental results on both numerical and real data show the superiority and robustness of the proposed method.
Through-body Communication Measurements Using Wearable and Implantable Sensor Antennas

This manuscript presents a successful through-body communication test bench using a customized wearable sensor antenna and an implantable sensor antenna. The sensor antennas were directly connected with VNA cables and two-port S-parameter measurements were conducted. The measurements verified the complex simulation setup that allows accurate modeling for the surrounding environment of implantable and wearable sensors. The simulation model verification was accomplished through the direct comparison of the simulation setup with the measurement setup. The measurement setup with a cable-connected implantable sensor was made possible through the placement of the implantable sensor under the armpit of a human subject. For the feasibility of the S-parameter measurements, two novel sensor antennas were implemented and used. A very compact, implantable sensor antenna, and a bi-directional wearable sensor loaded with two back-to-back antennas, designed and optimized to radiate effectively towards the human body and away from the human body, respectively.
Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance

Given 3D LiDAR point clouds, how can we segment them fast and accurately? Fast and accurate segmentation of 3D LiDAR points is an important issue in mobile robotics with various applications in classification, tracking, SLAM, etc. Despite its importance, existing methods do not provide both speed and accuracy; in particular, methods performing segmentation in the 3D domain are too slow, disabling its use in real-time processing. In this paper, we propose Curved-Voxel Clustering (CVC), a fast and accurate method for segmenting 3D LiDAR point clouds utilizing LiDAR-optimized curved-voxel. CVC attains fine discriminations by considering three important aspects for clustering 3D LiDAR points: distance from the sensor, directional resolutions, and rarity of points. CVC succeeds in providing real-time performance by carefully managing curved-voxels with a hash table. Especially, CVC works well on sparse 3D point clouds. Through experiments, we show that our method is up to 1.7× faster and 30% more accurate than other segmentation methods. CVC enables real-time segmentation with more than 20 runs in a second.
Fast and Reliable LoRa-based Data Transmissions

LoRaWAN is a recently proposed MAC layer protocol which manages communications between LoRa-based gate-ways and end-devices. It has attracted much scientific attention due its physical layer characteristics, but mainly due to its versatile configuration parameters. However, it is known that LoRaWAN-based transmissions suffer from extensive collisions due to the unregulated access to the medium. For this reason, various techniques that alleviate the burst of collisions have been proposed in the literature. In this paper, we deal with the problem of fast data delivery in LoRa-based networks. We model a network where transmissions follow a Poisson process. We compute the average packet success probability per Spreading Factor (SF) assuming orthogonal transmissions. We, then, formulate an SF optimization problem to maximize the success probability given an amount of data per node and a maximum data collection time window. We show – both theoretically and using simulations – that the overall success probability can be improved by approximately 100% using optimal SF assignments. We validate our findings using a 10-node testbed and extensive experiments. Despite that experiments reveal the existence of inter-SF interference, our solution still provides the best performance compared to other LoRaWAN configurations.
Wireless Sensor Network for Cardiac Activity Monitoring

This article presents a wireless sensor network capable to acquire and analyze indoor environmental quality (IEQ) parameters and improving such indoor conditions using actuator nodes. An additional WSN end node capable associated with user's physiological parameters monitoring in indoor conditions as part of ambient assisted living for elderly people is considered. The user's cardiac activity is sensed in an unobtrusive way and the acquired and primary processed data is displayed in real time by a developed web application. As a measurement channel associated with cardiac assessment, a Photoplethysmography (PPG) sensor is embedded in a daily used office chair. The proposed system also served to study the influence of indoor environment changes obtained through a set of distributed actuators on the user's cardiac activity. Preliminary experimental results are included in the paper.
Closed-Form Equations and Experimental Verification for Soft Robot Arm Based on Cosserat Theory*

Compared with conventional robots, soft structures such as living octopus arms and various soft-robot arms have more degrees of freedom (DOFs) and greater flexibility. Soft robot arms have a considerable range of applications. However, it is arduous to establish a mechanical model for them, because of the hyper-redundant DOFs, and the hyperelasticity and nonlinearity of the soft material. In this study, to investigate the deformation of soft octopus robot arms, the simplified closed-form analytical equations for the curvature and torsion were derived according to the Cosserat theory. A closed-form model for axial constriction was developed. The analytical equations were experimentally validated, and a dynamical simulation of the multi-flexible bodies was performed for a cable-driven soft-robot arm inspired by the octopus. The results show satisfactory accuracy.
Toward Smart Mobility: Assessing Digital Service and IT Governance Consequences for Public Transportation

Public transportation has become one of the important things in sustainable city development. This study discusses the factors that influence for increasing satisfaction of public transportation passengers, in this case, train passengers. In addition to factors that were discussed in previous studies, this paper also added digital service factors. This study conducted online survey and data analyzing using PLS-SEM method. Service delivery, onboard experience and accessibility are the most important factors in ensuring passenger satisfaction. Although digital service has not been a significant determinant in passenger satisfaction, this paper also proposes issues that need attention in relation to IT governance.
IoT Based Automated Weather Report Generation and Prediction Using Machine Learning

The weather can have great impact on lives. Weather changes can influence wide range of human activities and affect agriculture and transportation. The main aim of this paper is to monitor and report weather conditions so that one is informed beforehand and necessary actions can be taken to reduce the damage by any calamity by forecasting it. Here we are using various sensors in order to collect the data and previous data is used in order to train the system and with current data collection we do the prediction. We will be analyzing temperature, pollutants, humidity and pressure and will predict the weather. Existing models are expensive in contrast to ours and hence it will make monitoring local area feasible as it will be cheaper.
Soil Biological Parameters Inluenced by Various Nitrogen Sources

Pot experiment was run out to study the impacts of different nitrogen sources on microbial biomass (C and N) and other soil biological properties in Kovarvany brown forest and meadow chernozem soil types incubated at temperature 25±3°C. Pots were filled up with soil amended with sewage sludge at the levels of 0, 12.5, 25, or 50% (w/w) in comparison with soil amended with ammonium nitrate at 0, 11.5, 23 and 46 mg/kg soil for eight weeks. The results were compared with a pot containing a combination between ammonium nitrate at 23 mg/kg soil mixed with 25% of sewage sludge and with untreated control. Soil aerobic heterotroph bacterial enumeration, fungal population, soil basal respiration, total soil organic carbon, dehydrogenase and fluorescein diacetic acid (FAD) potential activities were investigated. Results indicated that the addition of sludge or NH4NO3 caused changes in the sizes of microbial population which increased significantly with the increases of the application rates of organic N source (sewage sludge) more than the inorganic N source (NH4NO3). In addition, sewage sludge had a stimulatory effect on the soil enzymatic potential activity and increases the total organic carbon. The highest number of fungi observed in meadow chernozem soil even treated with NH4NO3. It was found that Kovarvany brown forest soil had lower soil biomass and biological properties when treated with NH4NO3. Moreover, meadow chernozem soil had higher biological properties when treated with sewage sludge than NH4NO3 over the Kovarvany brown forest soil. These results suggest that the positive correlation between organic fertilizer amendment and soil quality where enhance both microbial and enzymatic activities. However, the results indicated that soil enzyme activities have been proposed as appropriate indicators because of their intimate relationship to soil biology, ease of measurements, and rapid response to a small change in soil management.
Application of FBMC to DVB-T2: a Comparison vs Classical OFDM Transmissions

In this paper, filter bank based multicarrier/Offset Quadrature Amplitude Modulation (FBMC/OQAM) is compared with a Cyclic Prefix based OFDM (CP-OFDM). Indeed, due to its high level of compatibility with OFDM systems, FBMC system is proposed as a new technique for European Digital Video Broadcasting-Terrestrial Second generation (DVB-T2) systems. To meet the performance requirement of the DVB-T2, the approach is based on a technique named nearly perfect reconstruction (NPR) filter bank system. The main characteristic of this approach is the use of Frequency Sampling (FS) Sub-Channel Equalizer, based on Complex Finite Impulse Response (CFIR) filter, to deal with the high frequency selective impairments. Simulation results indicate that, compared to FBMC, CP-OFDM requires about 1dB extra Signal to Noise Ratio (SNR) value at a bit error rate of 10-3 for high order modulation formats and at large number of sub-carriers. As FBMC system discards the use of the cyclic prefix, commonly used in OFDM, the data rate can be increased of up to 14.84% or even 25% maintaining the same bandwidth utilization.
Image Encryption Algorithm Based on Logistic and Two-Dimensional Lorenz

In recent years, experts and scholars in the field of information security have attached great importance to the security of image information. They have proposed many image encryption algorithms with higher security. In order to further improve the security level of image encryption algorithm, this paper proposes a new image encryption algorithm based on two-dimensional Lorenz and Logistic. The encryption test of several classic images proves that the algorithm has high security and strong robustness. This paper also analyzes the security of encryption algorithms, such as analysis of the histogram, entropy process of information, examination of correlation, differential attack, key sensitivity test, secret key space analysis, noise attacks, contrast analysis. By comparing the image encryption algorithm proposed in this paper with some existing image encryption algorithms, the encryption algorithm has the characteristics of large secret key space, sensitivity to the key, small correlation coefficient and high contrast. In addition, the encryption algorithm is used. It can also resist noise attacks.