Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dynamic Model for Skid Steering of Tracked Vehicles on Firm Ground

Tracked vehicle dynamic model is important for analyzing the dynamics of tracked vehicles and designing controllers for tracked vehicles. In this paper, a new view of the general theory for skid steering of tracked vehicles is put forward. Based on the general theory, a dynamic track force model used for 3 degrees of freedom (DOF) vehicle model is proposed for time-variant velocity. Simulation shows that the model requires moderate computation and that it can model tracked vehicles more precisely than the vehicle model based on general theory. The simulation result of the dynamic model in steady states is consistent with that of the general theory.
Deep Reinforcement Learning Based Load Balancing Routing for LEO Satellite Network

LEO satellite network (LEO-SN) constitutes an indispensable part of the space-air-ground integrated network (SAGIN). However, the characteristics of unbalanced traffic load, intricate orbits, high mobility, frequent link handovers, diverse storage and communication capabilities of nodes gradually lead to the ineffectiveness of the central routing planning methods. In order to enhance the flexibility and agility of the planning process, this paper presents an intelligent decentralized load balancing routing algorithm using deep reinforcement learning for the LEO-SN, which takes into consideration the queuing delay, storage space, communication bandwidth and propagation delay of merely one-hop satellite node. Simulation and analysis demonstrate that the proposed load balancing routing algorithm could converge quickly for both training and test sets, and possesses better performance than the comparison methods in terms of both the packet loss rate and transmission latency.
Depth- Time Dimension Signal Reconstruction of Transient Electromagnetic Logging using Compressed Sensing

Aiming at the problem of large amount of data in the downhole transient electromagnetic (TEM) array detection system, a compressed sensing-based for depth- time dimension signal reconstruction of TEM logging system is proposed, which can efficiently compress the amount of downhole data. Considering the high sparsity and high similarity of logging signal at different sampling times, the discrete wavelet transform (DWT) sparse transformation combined with the Bernoulli and Toeplitz measurement matrices are used to sparse and compress the depth-time dimension data. Moreover, the restructure of the sparse signals was realized by employing the convex optimization algorithm. Field experiments were conducted, and the results demonstrate that the proposed method can achieve the depth-time dimensional data compression, which can effectively reduce the data volume and improve the transmission efficiency of downhole TEM system.
4-Way 0.031-mm2 Switchable Bidirectional Power Divider for 5G mm-Wave Beamformers

This paper presents a miniaturized bidirectional power divider (PD) which incorporates the function of a single-pole-double-throw (SPDT) switch in addition to its regular operation. Therefore, the new component can be used as a transmitting/receiving (TRx) switch. With the proposed solution, several PDs are removed, and the SPDT is integrated, thereby saving area and reducing the overall insertion loss (IL) in phased antenna arrays. Moreover, due to its compact size and simplified routing strategy, the design is also suitable whenever many TRx elements are required, i.e., especially for 5G mm-wave beamformers. A 4-way prototype has been fabricated in a 130-nm SiGe BiCMOS technology to validate the concept and occupies an effective area of 180x170 µm2. Measurements results show an IL of 3.1 dB at 28 GHz and isolation higher than 19 dB. In addition, phase and amplitude errors are lower than 3° and 0.2 dB, respectively. In conclusion, a device that integrates the functionalities of SPDTs and PDs is reported without increasing area occupation compared to state-of-the-art PDs and, therefore, paving the way for compact, cost-effective beamformer chips.
Beam Selection and Tracking for Amplify-and-Forward Repeaters

Mobile network operators constantly have to upgrade their cellular network to satisfy the public’s hunger for increasing data capacity. However, regulatory limits regarding allowed electromagnetic field strength on existing cell sites often limit or prevent the installation of new cells on additional frequency bands. Further densifying the radio access infrastructure by means of new cell sites takes time because of the site acquisition, the need for permissions, and the civil construction. To this end, amplify-and-forward repeaters are a cost-efficient method to provide mobile wireless data capacity to concealed areas outdoors, inside buildings, or vehicles, where low signal levels drastically limit the achievable capacity or even prevent communication with distant base stations. Repeaters with high-gain beamforming antennas can be used to decrease the path loss and thereby offer a higher capacity than user equipment otherwise could achieve. Since those highly directional beams must be constantly adjusted, we propose methods to align and track the beam even without having access to in-band beam control mechanisms. Numerical analyses with measurement data show that such non-invasive (system data-agnostic) methods are feasible and only incur a few percent in throughput reduction.
Obstacle Avoidance Path Planning and Motion Control for a Multi-Joint Soft Manipulator

One of the most challenging tasks for soft manipulators is to avoid obstacles in a cluttered workspace. This paper aims to solve the problem by providing both an obstacle avoidance path planning method and a path following control strategy. To form a general framework for path planning, we proposed a collision map generation method in configuration space, which allows the usage of a general graph search or random sampling method to find a feasible obstacle-free path. In addition, to deal with the high nonlinear dynamics of soft manipulators, we design a gain-scheduling controller in the loop of pressure control, which adaptively tunes the controller gain along with the current operating pressures, and as a result, guarantees a unified performance for different deformation levels. The methods are testified on a three degree of freedom (DOF) soft manipulator and proven to be effective for obstacle avoidance tasks in cluttered environments.
A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method.
Short-Term Power Load Forecasting Technology Based on Bi-directional LSTM Quantile Regression

For the short-term power load probability density forecasting problem of smart grid, a Bi-directional LSTM quantile regression model is proposed. First, a Bi-directional LSTM cell structure is proposed to better extract the high-order features of short-term power load forecasting. Meanwhile, an attention mechanism is introduced into the Bi-directional LSTM structure to capture the importance of each part of the long sequence problem, and then the forecasting accuracy can be improved. Using the Bi-LSTM model, the power load forecast at different quantiles are obtained, and the Gaussian smoothing non-parametric estimation method can used to obtain the power load probability density prediction. Through simulation on actual power load data, the proposed method can provide more accurate short-term power load forecasting performance than previous load probability density prediction methods.
Practical Privacy Preservation in a Mobile Cloud Environment

The proliferation of smartphone devices has led to the emergence of powerful user services from enabling interactions with friends and business associates to mapping, finding nearby businesses and alerting users in real-time. Moreover, users do not realize that continuously sharing their trajectory data with online systems may end up revealing a great amount of information in terms of their behavior, mobility patterns and social relationships. Thus, addressing these privacy risks is a fundamental challenge. In this work, we present $TP^{3}$, a Privacy Protection system for Trajectory analytics. Our contributions are the following: (1) we model a new type of attack, namely “social link exploitation attack”, (2) we utilize the coresets theory, a fast and accurate technique which approximates well the original data using a small data set, and running queries on the coreset produces similar results to the original data, and (3) we employ the Serverless computing paradigm to accommodate a set of privacy operations for achieving high system performance with minimized provisioning costs, while preserving the users' privacy. We have developed these techniques in our $TP^{3}$ system that works with state-of-the-art trajectory analytics apps and applies different types of privacy operations. Our detailed experimental evaluation illustrates that our approach is both efficient and practical.
Deep Learning and Ensemble Method for Optic Disc and Cup Segmentation

Glaucoma is a chronic retinal disease that gradually damages the optic nerve. It is a leading cause of irreversible loss of vision. In ophthalmic fundus images, the cup to optic disc ratio measured around the optic nerve is a key measure used to screen for glaucomatous damages. Unfortunately, there is high subjectivity among ophthalmologists in estimating this ratio due to challenges in making reliable disc and cup measurements. To minimize this, we propose an automatic method using deep learning and ensemble method to segment the optic disc and cup. The proposed method comprises two steps. The region of interest (ROI), where optic disc is centered, is detected from a fundus image, following which the optic disc and cup are segmented from the ROI. Mask R-CNN algorithm is used to estimate the ROI, and two ensemble models based on three fully convolutional networks are used for the segmentation of optic disc and cup in parallel. The proposed method is trained and evaluated using the RIGA dataset that contains 750 fundus images and the REFUGE database containing 400 fundus images. The results demonstrate that the proposed method has a better performance compared with the current state-of-the-art algorithms. Our best segmentation results for optic disc shows 0.9303 Jaccard Index (JI) and 0.9635 Dice Coefficient (DC). The best segmentation results for cup shows 0.8096 JI and 0.8915 DC. The average cup to optic disc ratio error shows 0.0429.
Adaptive Command Filtered Control of Uncertain Nonlinear System with Friction Input

An adaptive command filter control of nonlinear system with friction input is formulated in this paper. First, based on the obtained state space model, a command filter control method is proposed, which can address the “explosion of complexity” problem existed in traditional backstepping design and ensure the asymptotic convergence of the tracking errors. Moreover, to cope with the problem of filter error between filter output and virtual control signal, dynamic error compensation system is designed. Next, a HONN system is employed to simplify the calculation and approximate the uncertainties in the system. At last, in order to clarify the effectiveness of the above theory, simulation results are given.
About Quantising Unconscious Decision Processes and Their Origin and Proving Intelligent Design

Prompted by previous research results we analysed human decision processes for unconscious patterns. Like former studies [1, 2] we were able to find law-like patterns that were not consciously created. We calculated the residual uncertainty about the pattern to 1 : 1,000,000,000 and proved a high correlation ratio for the data source related to the found pattern through an intra class correlation test (3.2). We showed that conscious (3.3) and unconscious human behaviour and natural selection (3.4) are not able to explain this result. This includes the possibility to prove Intelligent Design (3.4).
Rough Terrain Traveling Method Using an Elastic Telescopic Arm and a Tether

Towing a mobile platform with a tether fixed to the external environment is an effective way to traverse rough terrain. A flexibly bendable telescopic arm that can carry the tether at its arm tip was developed in a previous study. This study is focused on the rough terrain traveling method using a compact elastic telescopic arm installed on a mobile platform. Furthermore, by fusing the linear and bending mechanisms, we developed an elastic telescopic arm that is compact and can perform extension and bending motions on the same body. The results of the arm operation experiment showed that the extension-to-contraction ratio was improved by 16%, and the tip position control accuracy was improved by 70% compared to the previous study. Finally, the effectiveness of the proposed method was demonstrated by a step-climbing experiment, wherein, the step height was more than the wheel diameter of the mobile platform.
Space and Energy Distribution of Dark Matter

I start by an introduction to the notion of, and motivation for, Dark Matter, as well as a short motivation for supersymmetry, the theoretical framework usually examined when looking for places that dark matter may hide. After assuming a gravitino undergoing g → νγ decay, as a matter of convenience, there follows an exposition and use of the necessary tools to calculate the flux one might expect from galactic and extragalactic sources under such circumstances. The galactic model choice is given only a cursory glance, in anticipation of its ultimately minor importance to my main goal. That is, to determine if the decay of gravitinos over cosmological timescales is something that could influence our signal for reasonable values of the mean life.
Global Edge Bandwidth Cost Gradient-based Heuristic for Fast Data Delivery to Connected Vehicles under Vehicle Overlaps

The emergence of vehicle connectivity technologies and associated applications have paved the way for increased consumer interest in connected vehicles. These modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers thereby improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data delivery, an efficient edge resource allocation framework needs to be developed. In a recent work, data/service delivery to connected vehicles assumed a worst-case scenario that all vehicles with routes passing through an edge appear in the edge coverage region simultaneously. However, this worst-case scenario is very pessimistic, which results in overestimation of edge resources. We address this by precisely computing the set of vehicles which simultaneously appear in the coverage region of an edge (which we call vehicle overlaps). In this work, we first propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data delivery to connected vehicles while considering the traffic flow and vehicle overlaps. Then, we propose an efficient heuristic to deliver data based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in resource allocation considering vehicle overlaps. Using real world traffic data, we also demonstrate reduction in data delivery times using the proposed heuristic.
Determining Appropriate Classification Method Based on Influential Factors for Predicting Students’Academic Success

The need for accuracy in a prediction is a non-negotiable thing. One of the determinants of the accuracy of a prediction model is the classification method. Data mining offers various classification methods for predicting. Therefore, determining appropriate classification methods that produce high accuracy prediction model is a must. Several previous studies have shown excellent results based on influential factors for predicting students’ academic success. However, the research only focuses on one influential factor category rather than a combination of multiple influential factor categories. It becomes a serious issue since there are influential factors on the dataset that not only have one influential factor category but mostly multiple factor categories. Therefore, the best classification method for a multiple influential factor category has not been known yet. This research analyzes the performance of classification methods based on multiple categories of influential factors. The result will help the researcher find the best combination of factor category and classification method should they used. Among multiple factor category and classification methods have been tested show combination of certain classification method give the best result for certain multiple factor category.
A Human Intention Based Fuzzy Variable Admittance Control System for Physical Human–Robot Interaction

In human-robot interaction, accurate estimation of human intention can improve the interaction and enhance the stability of the interaction process. However, in the past research process, the research on human intention was relatively inaccurate or performed in a single point-to-point (PTP) task, which cannot meet most application scenarios. This paper proposes a fuzzy variable admittance system based on human-robot interaction. In this method, not only the direct intention is represented by the product of the Cartesian velocity and the interaction force, but the angular constraint with the axis represents the indirect intent, and fuzzy rules integrate the two purposes. Finally, to verify the performance of this method, PTP and trajectory tracking experiments are performed using a FRANKA PANDA robot.
Database-Driven Model Predictive Control System based on Alarm Management

Solve social issues and create new value through a cyber-physical system (CPS) is expected. As an effective technology for CPS, the control system utilizing data and models has been researched by authors. The control system estimates the model of the controlled object, and performs model predictive control using that model. However, the control performance may deteriorate due to the oscillation of the model parameters by performing the model estimation in each control cycle. This paper presents a design scheme of database-driven model predictive control system based on alarm management. By determining the model update timing by alarm management, these problems can be solved. The effectiveness of the proposed method is verified by a numerical simulation.
Multi-image Stitching for Smartphone-based Retinal Fundus Stitching

Adequate visualization of the retina is an important factor in assisting ophthalmologists in diagnosing various eye conditions and diseases. Retinal images pertaining to the posterior surface of the eye are captured using specialized devices called fundus cameras. Shortages of such devices are prevalent in areas without sufficient funding due to their expensive costs and, therefore, introduce a barrier to proper eye care. The advent of portable, low-cost smartphone-based fundus cameras has presented a promising solution to address this issue. However, images captured from recently developed smartphone-based portable fundus cameras and ophthalmoscopes have a lower field-of-view and quality compared to expensive tabletop fundus cameras. This paper proposes a method to enhance the diagnostic capacity of such smartphone-based fundus cameras and ophthalmoscopes by stitching multiple retinal images into a mosaic with a greater field-of-view of the retina. The key steps include feature detection, feature correspondence, image warping, and image blending. The method is implemented as a smartphone app intended to be used with low-cost portable fundus cameras and ophthalmoscopes. It successfully generates high-quality mosaics that retain the landmark structures and other notable clinically relevant features present in the original images while maintaining low profile image boundaries. The proposed method is validated by comparison with a standard stitching method on a success rate metric corresponding to the success and quality of image warping and blending. The dataset used for the validation includes 16 retinal fundus mosaicking image sets captured by smartphone-based retinal imaging devices. The comparison results indicate the proposed method is much more effective in stitching retinal fundus images than standard stitching techniques.
Analyzying Bivariate Ordinal Polytomous Data: A Marginal Multinomial Logistic Approach

The multinomial cell counts based likelihood and the generalized estimating equations (GEE) approaches are widely used for analysis of bivariate ordinal categorical responses. In both of these approaches, the joint cell probabilities are usually modeled in terms of a global odds ratio as a measure of association and the marginal probabilities for each of the two ordered response variables. These methods utilize the stochastic ordering of the responses by modelling the cumulative margins with certain suitable link functions so that the link function of a cumulative margin is linear in covariates and an intercept representing the ordinal category. This type of modelling, therefore, requires suitable order restricted inference for the cutpoints (intercepts) separating the ordinal categories. These cutpoints are, however, frequently estimated in traditional ways without challenging their order restrictions. In this paper, we distinguish the ordinal categories in a general way so that the covariate effects are generally different under different ordinal categories. This allows one to model the cumulative margins through certain non-linear regression functions which does not require any introduction of the cutpoints.