Miracle-dz/UniFAR-FTUs huggingface dataset:
Encoding Motion Primitives for Autonomous Vehicles Using Virtual Velocity Constraints and Neural Network Scheduling

Within the context of trajectory planning for autonomous vehicles this paper proposes methods for efficient encoding of motion primitives in neural networks on top of model-based and gradient-free reinforcement learning. It is distinguished between 5 core aspects: system model, network architecture, training algorithm, training tasks selection and hardware/software implementation. For the system model, a kinematic (3-states-2-controls) and a dynamic (16-states-2-controls) vehicle model are compared. For the network architecture, 3 feedforward structures are compared including weighted skip connections. For the training algorithm, virtual velocity constraints and network scheduling are proposed. For the training tasks, different feature vector selections are discussed. For the implementation, aspects of gradient-free learning using 1 GPU and the handling of perturbation noise therefore are discussed. The effects of proposed methods are illustrated in experiments encoding up to 14625 motion primitives. The capabilities of tiny neural networks with as few as 10 scalar parameters when scheduled on vehicle velocity are emphasized.
About step-length of ZeaD (Zhang et al Discretization) formula 4IgS_Y for future minimization via fan equations

Future minimization, i.e., discrete time-varying minimization, is a difficult and meaningful problem. It has been successfully solved by Zhang et al using zeroing dynamics (ZD) and discretization formulas. In this paper, a type of discrete-time ZD (DT-ZD) model, which is obtained via utilizing ZeaD (Zhang et al Discretization) formula 4IgS_Y, is analyzed and investigated to ensure its stability. Specifically, via theoretical guarantees, we propose the step-length domain, or say, the effective domain of the step-length, which makes the discrete-time model stable. Additionally, we make further efforts to obtain the step-length optimum which provides the optimal stability of the DT-ZD model. Eventually, numerical experiments are performed to validate the step-length domain and the step-length optimum of the DT-ZD model for future minimization.
Chaotic-cipher based memory efficient symmetric key cryptosystem

In this paper we proposes a chaotic attractor based symmetric-key cryptosystem as it's alternative based on some advantages, specifications and novel performences from other systems like as; Considerably smaller key space (typically 192bits), do not relies on computational infeasibility of determining prime factor, do not relies of randomness of initial point, relies on mathematical property of equations with multiple solutions, possible to implement on minuscule amount of memory and possible to optimize to use less processor cycle hence drawing less power.
Optimal Kinematic and Elastodynamic Design of Planar Parallel Robot with Flexible Joints

This paper proposes a procedure for the optimal design of a flexible-joint planar parallel robot based on the kinematic and elastodynamic performance criteria. The optimal design consists of determining the dimensions of links to maximize the size of the workspace and the elastodynamic performance simultaneously. Consequently, the optimal design is performed using a multi-objective optimization that permits to obtain the set of the optimal solutions. The set of the optimal solutions are analyzed by assessing the properties of four different solutions to establish the appropriate solution the accomplished a specific task.
Characteristics of Transparent, PEDOT:PSS-Coated Indium-Tin-Oxide (ITO) Microelectrodes

This letter reports on electrochemical and optical characteristics of flexible, transparent microelectrodes, which consist of thin poly-(3, 4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT:PSS) spun onto indium-tin-oxide (ITO) electrodes for potential applications in biomedical optoelectronic devices. Although PEDOT:PSS/ITO combined films have been extensively investigated for applications in optical devices, such as solar cells and LEDs, PEDOT:PSS/ITO films for use in electrophysiological recording have not been well characterized yet. In this letter, PEDOT:PSS-coated ITO microelectrodes with various diameters of 10, 37, 50, and 80 μm were microfabricated and characterized, and their properties were compared with plain ITO microelectrodes. Experimental results demonstrate that PEDOT:PSS-coated ITO electrodes exhibit decreased electrochemical impedance, well-performed stability in saline, and increased charge storage capacity while preserving excellent optical transparency and mechanical flexibility. Equivalent circuit models were fitted to the experimental results to analytically extract interface capacitance, charge transfer resistance, and solution resistance at the electrode–electrolyte interface.
Tongue contour extraction from ultrasound images using image parts

In this work we propose automatic tongue contour extraction from ultrasound images based on Convolutional Neural Networks (CNN). The developed deep neural network was trained with ultrasound image parts. In training step the distances of the image patch centers from nearest tongue contour was used as the class label. By taking advantage of the fact that images consist of video frames, the time information was used as training input parameter. In the test phase, the image parts obtained by using the sliding window method are given as input to the training model and distance classes are obtained. A class that is close to zero means a position close to the tongue contour. Candidate points for the tongue contour were created using the centers points of the parts. The tongue contour is extracted by fitting a 3rd degree polynomial on center points of candidates. The work is completed by comparing the manually marked contour with the automatically found contour. The dataset consists of ultrasound videos taken in the experimental environment.
Development of a novel feature weighting method using CMA-ES optimization

Feature weighting is one of the fundamental problems in machine learning algorithms and data mining to determine the importance of features. In this study, a novel feature weighting method using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization method for classification process is proposed. Experimental results are obtained by 10-fold cross validation technique with 3 different classifier models: Naive Bayes (NB), K nearest neighbors (K-NN) and Random Forest (RF) on 5 datasets in UCI Machine Learning Repository. Classification accuracy rate is used as the performance criterion. In addition, the developed CMAES-based method is also adapted to optimize these 3 classifiers in a voting-based ensemble algorithm. In this context, a different ensemble-based method is presented with CMAES-based feature weights obtained when the classifiers are individually and all together. Experimental studies show that the developed method gives better performance and promising results than the results obtained without feature weighting.
Classification of different black teas with E-nose

In this study; A low cost electronic nose circuit consisting of different sensors was used for classification of six different brewed tea. The odour of thebrewed teas are transformed into electric signal by an electronic nose circuit with eight sensors inside. These signals are classified using normalization, feature extraction, size reduction and classification algorithms. The principal component analysis method is used to reduce the size of the attribute matrix. For the classification, linear discriminant analysis, support vector machines and decision trees algorithms are used. 10-fold cross-validation method was used to determine the classification success. As a result of this study, the highest classification success was obtained as 70.9 % with linear discriminant analysis algorithm.
Dynamic Objects Segmentation for Visual Localization in Urban Environments

Visual localization and mapping is a crucial capability to address many challenges in mobile robotics. It constitutes a robust, accurate and cost-effective approach for local and global pose estimation within prior maps. Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects. Consequently, visual odometry pipelines often diverge and the localization systems malfunction as detected features are not consistent with the precomputed 3D model. In this work, we present an approach to automatically detect dynamic object instances to improve the robustness of vision-based localization and mapping in crowded environments. By training a convolutional neural network model with a combination of synthetic and real-world data, dynamic object instance masks are learned in a semi-supervised way. The real-world data can be collected with a standard camera and requires minimal further post-processing. Our experiments show that a wide range of dynamic objects can be reliably detected using the presented method. Promising performance is demonstrated on our own and also publicly available datasets, which also shows the generalization capabilities of this approach.
Use of a COAX-DBD Plasma Fluidized-Bed Reactor for Surface Modification of TiO2 and Potato-Starch Powders

The interest of studying the plasma treatment of small particles such as powders or granules has increased over the years. Organic- and inorganic-treated powders have several industrial applications, and their surface modification allows obtaining different desirable properties. A plasma fluidized-bed reactor was constructed and tested to assess the atmospheric plasma surface treatment of powders. Among all type of powders, there is a growing interest in studying the surface treatment of type-C and type-A powders in the Geldart scale. The surface modification of powder in this reactor is achieved when small particles are exposed to a coaxial-dielectric barrier discharge (COAX-DBD). The results include the analysis of two types of powders (organic and inorganic) treated by three atmospheric plasma sources. For the organic powder (potato starch, type A), calorimetry and rheological tests were performed to assess changes in the rheological behavior and thermal properties as a consequence of plasma exposure. For the inorganic powder (titanium dioxide, type C), changes in the settling time before and after plasma treatments were measured using a turbidimeter to detect modifications in the powder surface. The dielectric barrier plasma discharge was generated by the COAX-DBD at 12 kV $_{pp}$ and 25 kHz obtained by a radio frequency power supply. The results show that treatments with plasma reactor modified the properties in both powder types.
A comparative study on super resolution with deep learning

Deep learning architectures are applied in the solution of many problems and give very successful results compared to other methods. One of these problems is the Super Resolution problem. In this study, we tried to solve the problem of super resolution by using different deep learning architectures to obtain higher resolution images. The models used in this study are focused on the images scaled up by factors of 2, 3 and 4. As a result of the experimental studies, the model success is increased as the network depth and samples are increased. Instead of a shallow model with more number of parameters, a deep model with lower number of parameters offers more successful results.
Overexpression of LiDXS and LiDXR From Lily (Lilium ‘Siberia’) Enhances the Terpenoid Content in Tobacco Flowers

Lilium, the famous and significant cut flower, emits a variety of volatile organic compounds, which mainly contain monoterpenes, such as myrcene, (E)-β-ocimene, and linalool. To understand the molecular mechanism of monoterpene synthesis in Lilium, we cloned two potential genes in the methylerythritol 4-phosphate pathway, namely LiDXS and LiDXR, from the strong-flavored oriental Lilium ‘Siberia’ using a homology-based PCR strategy. The expression levels of LiDXS and LiDXR were consistent with the emission and accumulation of monoterpenes in different floral organs and during the floral development, indicating that these two genes may play key roles in monoterpene synthesis. Subcellular localization demonstrated that LiDXS and LiDXR are expressed in the chloroplasts. Ectopic expression in transgenic tobacco suggested that the flowers of LiDXS and LiDXR transgenic lines accumulated substantially more diterpene, sclareol, compared to the plants transformed with empty vector. Surprisingly, increased content of the monoterpene, linalool and sesquiterpene, caryophyllene, were detected in the LiDXR transgenic lines, whereas the emission of caryophyllene, increased in one of the LiDXS transgenic tobacco lines, indicating that these two genes play significant roles in the synthesis of floral volatiles in the transgenic plants. These results demonstrate that LiDXR can contribute to monoterpene biosynthesis in Lilium ‘Siberia’; however, the role of LiDXS in the biosynthesis of monoterpenes needs further study.
Usage of fully convolutional network with clustering for traffic light detection

In this paper we consider a traffic light detector constructed on the basis of a fully convolutional neural network for segmenting traffic lights on image and subsequent clustering, which allows us to obtain bounding boxes for traffic lights. The proposed approach is compared with one of the most effective object detectors — Single Shot Multibox detector (SSD). We implemented algorithms for objects detection on an embedded system based on the NVidia Jetson TX2 platform. Traffic light detection recall for the proposed approach is better than SSD and higher than 0.9 on both the testing and training samples from relatively small data set (500 training images and 107 testing images). Time of traffic lights detection on one frame is about 50 ms. The results of the traffic light detection prove the possibility of applying the approach based on fully convolutional neural network with clustering for embedded autonomous vehicle control systems and driver assistance systems.
Analysis of influence of losses in the core of the inductor on parameters of the buck converter

In the paper the influence of the applied model of power losses in the core of the inductor on parameters of the buck converter is analysed. The selected literature dependences describing power losses in the ferromagnetic core are presented. Equations describing values of quantities occurring in these equations for the inductor operating in the considered converter are formulated. Computer analyses illustrating the influence of frequency, converter load resistance and the applied model of losses on the power dissipated in the core of the inductor are performed. The obtained results of calculations are discussed.
Neuropeptide B and Vaspin as New Biomarkers in Anorexia Nervosa

Introduction The aim of the study was to assess the correlation between the levels of neuropeptide B (NPB), neuropeptide W (NPW), vaspin (VAS), and the total antioxidant status (TAS) in the blood, as well as nutritional status of patients with anorexia nervosa (AN). Materials and Methods The study covered a cohort of 76 female teenagers, including 46 females with extreme AN and 30 healthy peers (CONTR) aged 12-17. Results AN persons were characterized by higher (in comparison to CONTR) NPB and VAS concentrations and lower values of TAS levels, body weight, and anthropometric values. Positive correlations between NPB and VAS levels were noted in the AN group (R=0.33; p<0.001) as well as between concentrations of NPW and VAS in the same group (R=0.49; p<0.001). Furthermore, positive correlations existed between NPB and NPW concentrations across the whole studied population (AN+CONTR; R=0.75; p<0.000001), AN (R=0.73; p<0.000001) and CONTR (R=0.90; p<0.0005). Conclusions In detailed diagnostics of AN it is worth considering testing NPB and VAS levels.
Perioperative glucose management and outcomes in liver transplant recipients: A qualitative systematic review

AIM To investigate the relationship between post-liver transplantation (LT) glycemic control and LT outcomes. METHODS A qualitative systematic review on relevant prospective interventions designed to control glucose levels including insulin protocols. Studies investigating an association between glycemic control and post-LT outcomes such as mortality, graft rejection, and infection rate were reviewed. PubMed, EMBASE, and other databases were searched through October 2016. RESULTS Three thousands, six hundreds and ninety-two patients from 14 studies were included. Higher mortality rate was seen when blood glucose (BG) ≥ 150 mg/dL (P = 0.05). BG ≥ 150 mg/dL also led to higher rates of infection. Higher rates of graft rejection were seen at BG > 200 mg/dL (P < 0.001). Mean BG ≥ 200 mg/dL was associated with more infections (P = 0.002). Nurse-initiated protocols and early screening strategies have shown a reduction in negative post-LT outcomes. CONCLUSION Hyperglycemia in the perioperative period is associated with poor post-LT outcomes. Only a few prospective studies have designed interventions aimed at managing post-LT hyperglycemia, post-transplant diabetes mellitus (PTDM) and their impact on post-LT outcomes.
LTE-CAS: LTE-based criticality aware scheduling for UAV assisted emergency response

In disaster scenarios like cyclone, flood, earthquake, etc., not only the infrastructure is in a shattered state, but also victims are in critical situations. In such scenarios, Unmanned Aerial Vehicles (UAVs) are emerging as powerful new class of emergency response systems. UAVs mounted with communication infrastructure, such as a Long Term Evolution (LTE) based Evolved NodeB (eNB) are used to provide communication links to the affected people and to send/receive messages, videos, images to/from the victims. Providing delay bound and guaranteed communication during emergency response operations is a major challenge as the victims are stuck in critical conditions; may have limited remaining energy left to communicate with unpredictable link quality due to indoor topology. In this paper, we propose a communication framework using eNB mounted UAVs, and propose a novel uplink scheduling scheme called Criticality Aware Scheduling (CAS) for an emergency scenario. It takes into consideration the criticality of user location, available battery power and link quality of the users. From extensive simulation results, we observe that the proposed uplink scheduling scheme improves the Quality of Experience (QoE) of the critical users significantly as compared to the state-of-the-art scheduling schemes.
Real-time QoE estimation of DASH-based mobile video applications through edge computing

Video applications using MPEG-DASH (Dynamic Adaptive Streaming over HTTP, such as YouTube and Netflix) have been dominating the Internet traffic in recent years. It is increasingly acknowledged that in order to provide video clients with better Quality-of-Experience (QoE), both content service providers and network operators need to be aware of clients' QoE in the first place. In this paper, we present a novel real-time QoE estimation system through edge computing, which has been implemented and deployed at a real LTE-A network edge. When equipped with such a system, any virtual network function (VNF) deployed in a mobile network will be able to infer all DASH clients' QoE under its coverage in real time, where no feedback from clients are needed. Furthermore, our scheme is able to work robustly in busy network environments involving air interface where packet errors frequently occur. The significance of such a scheme is the availability of accurate and real-time knowledge on user QoE through a very lightweight mechanism at the mobile edge, which can be instantaneously used for various content manipulation or resource adaptation operations in order to assure user QoE in dynamic conditions. Through experiments in a real LTE-A network, we demonstrate that our scheme is able to estimate DASH clients' QoE with very high accuracy with very low CPU and RAM footprint.
An adaptive multi-factor fault-tolerance selection scheme for FPGAs in space applications

Fault-Tolerant is currently an essential feature in critical systems. In space applications, many of these systems utilize SRAM-based FPGAs. Due to the small device dimensions in today's FPGAs and the harsh environment in outer space, Multiple Event Upsets (MEUs) can no longer be neglected. This paper presents a technique able to tolerate both MEUs and Single Event Upsets (SEUs); it also dynamically determines the appropriate Fault-Tolerant architecture to be applied to each module in the system. This technique aims at maximizing system reliability while consuming as little battery power as possible. Another important contribution of this technique is that it does not only depend on the failure rate but relies on several other factors that keep changing during the mission such as the battery level, module criticality, type of failures, etc. A case study is presented to illustrate the use of the technique.
A plant-inspired kinematic model for growing robots

This paper presents a kinematic model inspired by plant growth strategies and used to describe the movement of a robotic root, able to self-build its body structure using a 3D printer-like mechanism embedded in its tip. The proposed model is implemented in simulation and validated through a comparative analysis of the position, in space, of the robotic and simulated tip, obtaining a maximal positional error of ∼7% with the smallest curvature radius within a curvature arc of ∼10 cm. The model is able to describe the motion of any robot that navigates its environment and moves by growing from the tip in a 3D space, and it has been validated on a plant-inspired robot. The new emerging generation of growing robots offers an alternative locomotion perspective in robotics, which is grounded on the ability of this kind of bioinspired robots to morphologically and dynamically adapt their body to surrounding environments, offering new scenarios of use in search and rescue tasks, and hazardous conditions.
Filter-based feature selection for microarray data using improved binary gravitational search algorithm

Today, high-dimensional data have become one of the most important challenges in machine learning. Among thousands of features which exist in such data, some are redundant or unrelated and selecting a few of them improves classifier performance. Micro-array data which are one of the most important high-dimensional data in medicine have a large number of features and a few number of samples. Thus, old simple methods can be used to select features of such data effectively. Among several methods which have been proposed for selecting features of high-dimensional data, Swarm intelligence-based methods have attracted attentions more than ever. These methods are suitable to solve time-consuming and complex problems such that they search near-optimal solution with desirable computational cost. In this paper, a filter based Swarm intelligence-based search method based on Improved Binary Gravitational Search Algorithm (IBSGA) is proposed to integrate filter approaches with Swarm intelligence-based methods to improve feature selection process in micro-array data. The proposed method is applied to 5 high-dimensional micro-array databases and the obtained results are compared with one of the up-to-date methods used for feature selection in micro-array data. Experimental results verify efficiency of the proposed algorithm.