Miracle-dz/UniFAR-FTUs huggingface dataset:
PID slip control based on vertical suspension system for in-wheel-motored electric vehicles

A slip controller for in-wheel-motored electric vehicles (EVs) based on PID theory is proposed in this paper. Half-vehicle vertical vibration model and the vehicle body dynamics model are built to research longitudinal dynamic performance of EVs. The effect of vertical force transfer on tire is analyzed to describe vehicle movement accurately. Based on the model, PID slip controller is designed. The longitudinal slip of front wheel and rear wheel are controlled in the stable range through controlling the torque of the wheels. Finally, the effectiveness of the proposed controller is verified in the MATLAB/Simulink. The longitudinal slip ratio of the wheel can catch the optimal longitudinal slip in three seconds. Slip is maintained between 0 and 0.08 when accelerating, or -0.08 when braking. The wheels are prevented from getting locked or slipped to ensure safety of the vehicle.
Automating analysis of vegetation with computer vision: Cover estimates and classification

Abstract This study develops an approach to automating the process of vegetation cover estimates using computer vision and pattern recognition algorithms. Visual cover estimation is a key tool for many ecological studies, yet quadrat‐based analyses are known to suffer from issues of consistency between people as well as across sites (spatially) and time (temporally). Previous efforts to estimate cover from photograps require considerable manual work. We demonstrate that an automated system can be used to estimate vegetation cover and the type of vegetation cover present using top–down photographs of 1 m by 1 m quadrats. Vegetation cover is estimated by modelling the distribution of color using a multivariate Gaussian. The type of vegetation cover is then classified, using illumination robust local binary pattern features, into two broad groups: graminoids (grasses) and forbs. This system is evaluated on two datasets from the globally distributed experiment, the Nutrient Network (NutNet). These NutNet sites were selected for analyses because repeat photographs were taken over time and these sites are representative of very different grassland ecosystems—a low stature subalpine grassland in an alpine region of Australia and a higher stature and more productive lowland grassland in the Pacific Northwest of the USA. We find that estimates of treatment effects on grass and forb cover did not differ between field and automated estimates for eight of nine experimental treatments. Conclusions about total vegetation cover did not correspond quite as strongly, particularly at the more productive site. A limitation with this automated system is that the total vegetation cover is given as a percentage of pixels considered to contain vegetation, but ecologists can distinguish species with overlapping coverage and thus can estimate total coverage to exceed 100%. Automated approaches such as this offer techniques for estimating vegetation cover that are repeatable, cheaper to use, and likely more reliable for quantifying changes in vegetation over the long‐term. These approaches would also enable ecologists to increase the spatial and temporal depth of their coverage estimates with methods that allow for vegetation sampling over large spatial scales quickly.
Multiple Kinases Can Phosphorylate the N-Terminal Sequences of Mitochondrial Proteins in Arabidopsis thaliana

Phosphorylation of the transit peptides of nuclear-encoded preprotein is a well-known regulatory process of protein import in plant chloroplasts. In the Arabidopsis Protein Phosphorylation Site Database, 103 out of 802 mitochondrial proteins were found to contain one or more experimentally proven phosphorylation sites in their first 60 amino acid residues. Analysis of the N-terminal sequences of selected mitochondrial preproteins and their homologs from 64 plant species showed high conservation among phosphorylation sites. The ability of kinases from various sources including leaf extract (LE), root extract (RE), wheat germ lysate (WGL), and STY kinases to phosphorylate N-terminal sequences of several respiratory chain proteins were examined by in vitro kinase assays. The three STY kinases were shown to phosphorylate the N-terminal sequences of some proteins we tested but exhibited different specificities. Interestingly, the N-terminal sequences of two mitochondrial ATP synthase beta subunit 1/3 (pF1β-1/3) could be phosphorylated by LE and RE but not by STY kinases, suggesting that there are uncharacterized presequence-phosphorylating kinases other than STY kinases present in RE and LE. Mitochondrial import studies showed that the import of RRL-synthesized pF1βs was impeded by the treatment of LE, and the addition of a short SSU transit peptide containing a phosphorylatable 14-3-3 binding site could enhance the import of LE-treated pF1βs. Our results suggested that the transit peptide of pSSU can compete with the presequences of pF1βs for an uncharacterized kinase(s) in leaf. Altogether, our data showed that phosphorylation of transit peptides/presequences are not uncommon for chloroplast-targeted and mitochondria-targeted proteins, albeit possibly differentially regulated.
A sampled-data Hamilton-Jacobi reachability approach to safe and robust motion planning

Reachability analysis provides safety guarantees for state-constrained control systems, which makes it an attractive tool to address motion planning problems, but there are still few applications in cluttered environments. In this paper, a Hamilton-Jacobi reachability based formulation of the motion planning problem in both static and dynamic environments for sampled-data systems is proposed. Also, a robust safety-preserving and target-reaching sampled-data feedback controller is synthesized. We use an existing sampled-data reachability algorithm to compute robust reach- avoid tubes and formally extend it to the state-time space in order to deal with moving target and obstacles. The validity of this approach is eventually demonstrated through simulations in two simple scenarios and the results are discussed.
AirExGlove — A novel pneumatic exoskeleton glove for adaptive hand rehabilitation in post-stroke patients

Stroke is one of the leading causes of disability worldwide: post-stroke disabilities affect the upper and lower limbs, significantly undermining a subject's autonomy in the Activities of Daily Living (ADLs). Among post-stroke disabilities, one of the most impairing and widespread conditions is the clenched fist deformity: the subject experiences a permanent contraction of the hand, resulting in a closed hand rest pose. In this paper, the authors propose a novel light-weight inflatable soft exoskeleton device, called the AirExGlove, to deliver high-dosage, adaptive and gradual rehabilitation therapy to patients affected by clenched fist deformity. Our system is lightweight, low-cost, adaptable to any hand size and unobtrusive. The system has been extensively tested to assess the hand-opening range in which it can operate according to the severity of the patient condition, which is typically ranked on the Modified Ashworth Scale (MAS) scale. Experimental analysis demonstrates the suitability of the glove for patients affected by post-stroke muscle spasticity scoring up to 3 out of 4 in the MAS scale. Preliminary testing with clenched-fist patient confirmed a higher level of ergonomics of the system in comparison with rigid-linked robotic systems.
Tracking objects in 3D space with back-projected correlation filter

RGB-D cameras which become popular in recent years have brought new opportunities as well as challenges to many computer vision tasks. However how to fully make use of the depth information in visual tracking is still under-investigated in current literatures of visual tracking. We present in this paper an effective method for object tracking in RGB-D videos. In our approach, depth information is integrated into the kernelized correlation filter (KCF) tracking framework. Instead of treating depth as a color channel or adopting a volumetric representation of target, we take advantage of the 3D information of the scene behind depth and back-project the 2D response map of conventional KCF into 3D space. Then the 3D location of target can be determined by mode seeking in the resulted 3D response map. Since 3D location is obtained, the scale of the template can be determined naturally which in turn can benefit the KCF. We also propose a model updating mechanism which can balance model adaption against tracker drifting. Experimental results have demonstrated the effectiveness of the proposed method.
Predicting Career Decision-Making Difficulties: The Role of Trait Emotional Intelligence, Positive and Negative Emotions

The current study evaluated the relationship between positive and negative emotions, trait emotional intelligence (trait EI) and difficulties in career decision-making. We examined whether trait EI could predict career indecisiveness, a type of career decision-making difficulty, over and above the “Big Five” personality traits. We also examined the mechanism through which trait EI affected career indecisiveness by investigating the mediating role of positive and negative emotions. Survey data were collected from a sample of 600 undergraduate students in a university in southwest United States, who completed questionnaires measuring trait EI, positive emotions, negative emotions, personality traits, and career indecisiveness. Hierarchical multiple regression analyses indicated that trait EI accounted for a significant proportion of the variance in career indecisiveness that was not explained by the “Big Five” personality traits. The parallel mediation analysis revealed that positive and negative emotions partially mediated the relationship between trait EI and career indecisiveness. Using the framework of Fredrickson’s (2001) broaden and build theory of positive emotions, the study provides empirical evidence explaining the mechanism through which trait EI helps individuals cope better with challenging situations in life. Trait EI aids the elicitation of positive emotions and the down-regulation of negative emotions, which, in turn, broaden the range of thoughts and actions that come to mind, helping individuals function more effectively in the context of career decision-making. Implications for career counseling and opportunities for further research are discussed.
Design and implementation of fuzzy supervisor controller on optimized DC machine driver

In this paper, the main target is the intelligent control of a DC machine to achieve accurate and fast speed control. In order to tackle this important and challenging issue, the Imperialist Competitive Algorithm (ICA) has been used to tune PI controllers' coefficients accurately. The simulation results of our proposed algorithm are compared with similar approaches. In addition to software simulation, the laboratory prototype is made up of two DC 750W motors (one in motor mode and the other as a system load), in order to validate the results obtained in the simulation. In our work, the motor drive is controlled by MATLAB software then a fuzzy observer controller is used to improve the system performance. The obtained practical results of the proper design clearly demonstrate the high performance of our intelligent controller.
Mediterranean Conference on Embedded Computing MECO ’ 2018 , Budva , Montenegro Adaptive average BM 3 D filter for reconstruction of images with combined noise

A method for reconstruction of images corrupted by mixed, salt-and-pepper and Gaussian, noise is proposed. The adaptive average BM3D filter is a combination of two algorithms for the reconstruction of images corrupted with mixed noise. The two-stage adaptive average filter showed good results in the denoising of images corrupted by a salt-and-pepper noise, whereas the BM3D algorithm showed good results in denoising images corrupted by a Gaussian noise. The algorithm was compared to a state-of-the-art algorithm. Keywords— Adaptive average filter, BM3D, Denoising, Gaussian, Image processing, salt-and-pepper
Multi-Task Learning with Incomplete Data for Healthcare

Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalization performance. However, missing features in the input matrix is a much more difficult problem which needs to be carefully addressed. Removing records with missing values can significantly reduce the sample size, which is impractical for datasets with large percentage of missing values. Popular imputation methods often distort the covariance structure of the data, which causes inaccurate inference. In this paper we propose using plug-in covariance matrix estimators to tackle the challenge of missing features. Specifically, we analyze the plug-in estimators under the framework of robust multi-task learning with LASSO and graph regularization, which captures the relatedness between tasks via graph regularization. We use the Alzheimer's disease progression dataset as an example to show how the proposed framework is effective for prediction and model estimation when missing data is present.
Efficient multiple hypotheses tracking scheme using adaptive number of ‘K’ best hypotheses for target tracking in clutter

In a cluttered target tracking environment multiple hypotheses tracking (MHT) based algorithms improve the data association by considering a batch of measurements. To reduce the computational complexity generated by the exponential growth of hypotheses, the number of hypotheses is limited to k in the k best MHT algorithm. In conventional k best MHT algorithm the value of k is fixed. This paper proposes a method to keep the value of k adaptive depending on the scenario complexity and also depending on the likelihood of the valid hypotheses. The Monte Carlo simulation results carried out in this paper justifies the advantage of the proposed method compared to the fixed k-best MHT algorithm.
Parameterized slot scheduling for adaptive and autonomous TSCH networks

Internet of things (IoT) technologies have been widely used in various applications, especially in industrial field, which often require energy efficiency, high reliability, and low delay. Recently, Time Slotted Channel Hopping (TSCH) that operates based on TDMA with channel hopping has been standardized to achieve such multiple goals. Based on TSCH protocol, the nodes are scheduled at slots by scheduling algorithm, where there is a trade-off between energy efficiency and contention among the senders. In this paper, we propose a novel slot scheduling mechanism that works on TSCH, called PAAS (Parameterized Adaptive and Autonomous Scheduling), which minimizes the energy consumption with the reasonably low latency while guaranteeing the reliability. PAAS is an autonomous and distributed algorithm and it works adaptively to traffic intensity, Slotframe length, and reliability requirements. To evaluate PAAS, we implement PAAS in Contiki OS and perform extensive simulations using Cooja simulator where we confirm that energy consumption is reduced by up to 63 % comparing to existing scheduling algorithms with 99.997 % reliability.
Hybrid computation offloading in fog and cloud networks with non-orthogonal multiple access

Fog Computing is proposed as a promising technique that extends the computing infrastructure from the cloud center to the network edge. To enable applications to offload efficiently, the multiple resource allocation, including computation, spectrum, and energy resource as well as user scheduling, plays a quite important role in future networks. However, due to the constrained computing, storage and radio resource of fog nodes (FNs), the multiple resource allocation would be a fundamental problem and it should be well investigated. Thus, in this paper, we focus on this problem in a general multi-user multi-FN system with a remote cloud in small cell networks, where each user has multiple independent tasks that can be executed at FNs or at the remote cloud. The multiple FNs are response for performing the task distribution, user scheduling, and resource allocation dynamically and independently. Furthermore, to reduce offloading transmission latency and release the constraint of the limited radio resource, non-orthogonal multiple access (NOMA), which enables the multiple users to transmit data to the same FN on the same radio resource, is introduced into fog and cloud networks. To this end, we formulate the offloading decision, user scheduling, and resource allocation problem as an optimization problem. Our objective is to minimize the total system cost of energy as well as the delay of users. Furthermore, we transform the original problem into a convex problem and then decompose it in a distributed and efficient way. Simulation results show that the proposed approach achieves a better performance compared with existing schemes.
Phase locked loop Ku band frequency synthesizer based on a tuned YIG oscillator

This paper presents design and realization of Ku band frequency synthesizer based on a YIG oscillator employed as a high frequency signal source. The key aspect of this design is ADC sampling of a phase locked loop filter voltage to compensate an output frequency drift caused by temperature changes. A dedicated circuit model was created and used to determine loop filter component values. The synthesizer was built and its measured performance was compared to calculated values.
Influence in time-dependent citation networks

In this paper, we present a running influence model for evaluating the pairwise impact between communities in citation networks and define the time limitations of the model. The model improves a recent one from the literature. To compare and contrast different communities influence, we present a metric for estimating the overall running influence value of particular community. We also study the problem of the time dependency of the influence within citation networks. To conduct the community influence analysis, we perform the experiments using a real-world database. The results are compared to both real-world ranking and the state-of-the-art method. They show the adequacy of the proposed metrics and model of running influence. We also demonstrate the importance of proposed differentiation within calculation and interpretation of the influence in time-dependent citation networks.
Distributed Control for Distributed Energy Resources: Long-Term Challenges and Lessons Learned

Recently, the academic and industrial literature has arrived at a consensus in which the electric grid evolves to a more intelligent, responsive, dynamic, flexible, and adaptive system. This evolution is caused by several drivers including: decarbonization, electrified transportation, deregulation, growing electricity demand, and active consumer participation. Many of these changes will occur at the periphery of the grid, in the radial distribution system, and its potentially billions of demand-side resources. Such spatially distributed energy resources naturally require equally distributed control and electricity market design approaches to enable an increasingly active “smart grid. ” In that regard, this paper serves to highlight lessons recently learned from the literature and point to seven open long-term challenges facing the future design of electricity markets. They are: 1) simultaneously manage the technical and economic performance of the electricity grid; 2) span multiple operations time scales; 3) enable active demand side resources; 4) activate the power grid periphery; 5) promote synergies with interdependent infrastructures; 6) respect organizational jurisdictions; and 7) promote resilient self-healing operation. For each challenge, some recent contributions are highlighted and promising directions for future work are identified.
YouTube for Patient Education: A Deep Learning Approach for Understanding Medical Knowledge from User-Generated Videos

YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic solution to evaluate videos from a health literacy and patient education perspective. We develop a deep learning method to understand the level of medical knowledge encoded in YouTube videos. Preliminary results suggest that we can extract medical knowledge from YouTube videos and classify videos according to the embedded knowledge with satisfying performance. Deep learning methods show great promise in knowledge extraction, natural language understanding, and image classification, especially in an era of patient-centric care and precision medicine.
A cost optimized reverse influence maximization in social networks

In recent years, Influence Maximization (IM) has gained great research interest in the field of social network research. The IM is a viral marketing based approach to find the influential users on the social networks. It determines a small seed set that can activate a maximum number of nodes in the network under some diffusion models such as Linear Threshold model or Independent Cascade model. However, previous works have not focused on the opportunity cost defined by the minimum number of nodes that must be motivated in order to activate the initial seed nodes. In this work, we have introduced a Reverse Influence Maximization (RIM) problem to estimate the opportunity cost. The RIM, working in opposite manner to IM, calculates the opportunity cost for viral marketing in the social networks. We have proposed the Extended Randomized Linear Threshold RIM (ERLT-RIM) model to solve the RIM problem. The ERLT-RIM is a Linear Threshold (LT)-based model which is an extension to the existing RLT-RIM model. We also have evaluated the performance of the algorithm using three real-world datasets. The result shows that the proposed model determines the optimal opportunity cost with time efficiency as compared to existing models.
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering

Weighted correlation clustering is hard to solve and hard to approximate for general graphs. Its applications in network analysis and computer vision call for efficient algorithms. To this end, we make three contributions: We establish partial optimality conditions that can be checked efficiently, and doing so recursively solves the problem for series-parallel graphs to optimality, in linear time. We exploit the packing dual of the problem to compute a heuristic, but non-trivial lower bound faster than that of a canonical linear program relaxation. We introduce a re-weighting with the dual solution by which efficient local search algorithms converge to better feasible solutions. The effectiveness of our methods is demonstrated empirically on a number of benchmark instances.