Miracle-dz/UniFAR-FTUs huggingface dataset:
Reg: An Ultra-Lightweight Container That Maximizes Memory Sharing and Minimizes the Runtime Environment

The rise of container technology has brought about profound changes in the data center, and many software have been transferred to micro-service deployment and delivery. Therefore, it is of a broad practical significance to optimize the startup, operation and maintenance of large-scale containers in a massive user environment. At present, the mainstream container technology represented by Docker has achieved great success, but there is still much room for improvement in image volume and resource sharing. We reviewed the development of virtualization technology, and clarify that lightweight virtualization technology is the future research direction, which is very important for data-sensitive applications. By establishing a library file sharing model, we explored the impact of the degree of sharing of library files on the maximum number of containers that can be launched. We present an ultra-lightweight container design that minimizes the container runtime environment that supports application execution by refining the granularity of operational resources. At the same time, we extract the library files and the executable binary files into a single layer, which realizes the maximum sharing of the host's memory resources among containers. Then, according to the above scheme, we implement an ultra-lightweight container management engine: Reg (runtime environment generation), and a Reg-based workflow is defined. Finally, we carried out a series of comparative experiments on mirror volume, startup speed, memory usage and container startup storm, verified the effectiveness of proposed method in the large-scale container environment.
Reducing Computation Times for Planning of Reference Trajectories in Cooperative Autonomous Driving

This paper considers the problem of planning reference trajectories for cooperating autonomous vehicles. Our previous work [1] relied on mixed-integer quadratic programming (MIQP) for planning, while controllable sets of hybrid automata were used to assess feasibility. In this paper, we supersede MIQP in the online part and obtain plans by directly making use of controllable sets and adapting ideas from approximate multi-parametric programming. The run-time of the algorithm is not only lower, but also more predictable than that of MIQP solvers, which is crucial for real-time operation. Even though it returns suboptimal solutions, the algorithm also enables the use of smaller sampling times or longer planning horizons.
Respiratory Rate Estimation from the Photoplethysmogram Combining Multiple Respiratory-induced Variations Based on SQI

Currently measuring respiratory rate (RR) accurately and conveniently is still a difficult problem. The photoplethysmogram (PPG) obtained from pulse oximetry contains a variety of physiological information related to human health, such as heart rate, blood pressure, oxygen saturation and RR. This paper presents an algorithm to estimate respiratory rate based on the characteristic parameters of PPG. Firstly, signal quality index (SQI) is used to identify the low-quality PPG signal. Secondly, respiratory waveform is modeled by four features extracted from PPG signal. Thirdly, Autoregressive modelling (AR) is used to obtain frequency of the four respiratory-induced variations. Fourthly, RR is estimated by a data fusion method combine the four respiratory-induced variations. Finally, the proposed method has been tested on physiological recordings from some adults. The accuracy of the RR is assessed using root mean square (RMS) error. The median RMS error obtained for RR is 0.75 breaths/min showing that the proposed algorithm has achieved high accuracy and robustness.
On OCT Image Classification via Deep Learning

Computer-aided diagnosis of retinopathy is a research hotspot in the field of medical image classification. Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common ocular diseases that can result in partial or complete loss of vision. Optical coherence tomography imaging (OCT) is widely applied to the diagnosis of ocular diseases including DME and AMD. In this paper, an automatic method based on deep learning is proposed to detect AME and AMD lesions, in which two publicly available OCT datasets of retina were adopted and a network model with effective feature of reuse feature was applied to solve the problem of small datasets and enhance the adaptation to the difference of different datasets of the approach. Several network models with effective feature of reusable feature were compared and the transfer learning on networks with pre-trained models was realized. CliqueNet achieves better, classification results compared with other network models with a more than 0.98 accuracy and 0.99 of area under the curve (AUC) value finally.
Controlling an Autonomous Vehicle with Deep Reinforcement Learning

We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy optimization in combination with a simulated environment. Training from scratch takes five to nine hours. The resulting agent is evaluated within simulation and subsequently applied to control a full-size research vehicle. For this, the autonomous exploration of a parking lot is considered, including turning maneuvers and obstacle avoidance. Altogether, this work is among the first examples to successfully apply deep reinforcement learning to a real vehicle.
Beyond Quantization in Iterative Learning Control: Exploiting Time-Varying Time-Stamps

Equidistant sampling in control system may lead to quantization errors for certain measurement equipment, e.g., encoders. The aim of this paper is to develop an Iterative Learning Control (ILC) framework that eliminates quantization by exploiting time stamping. The developed ILC framework employs the non-equidistant time stamps in a linear time-varying (LTV) approach. Since the data at the time-stamps does not suffer from quantization, unparalleled performance can be achieved, while the intersample behaviour is bounded by definition. A simulation example confirms superiority of the ILC framework which employs time stamping.
Developing a Local Positioning Algorithm Based on the Identification of Objects in a Wi-Fi Network of the Mall

The proposed algorithm for local positioning in the mall is based on the Wi-Fi standard, in which the signal strength received by the mobile device from Wi-Fi access points is the only source of information about the location of objects. The study formalizes the problem of local identification of the object in the mall using Wi-Fi technology; The requirements for a local positioning system that identifies an object using Wi-Fi technology are formulated. The developed prototype can be used to determine the location and construction of a route of a moving object in the mall.
Effects of Vehicle Simulation Visual Fidelity on Assessing Driver Performance and Behavior

Automotive manufactures are rapidly developing more advanced in-vehicle systems that seek to provide a driver with more active safety and information in real-time, in particular human machine interfaces (HMIs) using mixed or augmented reality (AR) graphical elements. However, it is difficult to properly test novel AR interfaces in the same way as traditional HMIs via on-road testing. Instead, simulation could likely offer a safer and more financially viable alternative for testing AR HMIs, inconsistent simulation quality may confound HMI research depending on the visual fidelity of each simulation environment. We investigated how visual fidelity in a virtual environment impacts the quality of resulting driver behavior, visual attention, and situational awareness when using the system. We designed two large-scale immersive virtual environments; a “low” graphic fidelity driving simulation representing most current research simulation testbeds and a “high” graphic fidelity environment created in Unreal Engine that represents state of the art graphical presentation. We conducted a user study with 24 participants who navigated a route in a virtual urban environment via direction of AR graphical cues while also monitoring the road scene for pedestrian hazards, and recorded their driving performance, gaze patterns, and subjective feedback via situational awareness survey (SART). Our results show drivers change both their driving and visual behavior depending upon the visual fidelity presented in the virtual scene. We further demonstrate the value of using multi-tiered analysis techniques to more finely examine driver performance and visual attention.
Electrouter-An Automated Wireless Charging Gadget Zone

Objective of this paper is to introduce an evolutionary-based wireless charging system. Wireless charging is a technology which is inspired by Sir Nikola Tesla's basic principles of wireless power transfer. Because of which we are able to transmit an electrical power through the air gap. Many people face low battery issues in their daily life. And it becomes worst in public places where electrical sockets are limited and not available. Moreover, they also need to take care of their traditional charging chords in their hectic day to day life. In order to abate all these difficulties of people, we developed a system which provides charging wirelessly in public area as like free public WIFI provided to the user on public area. Our system is based on Electromagnetic Induction coupling as a core functionality to possess wireless charging. In this article, we also introduced a mobile application which helps to automate charging with respect to threshold charging percentage and guard our users against harmful accidental charging disaster. Also in this article, we provide a smart automation system which focuses on electricity conservation via the Internet of Things (IoT).
A Transactional Approach for Reliable Elastic Cloud Resources

The next generation of cloud computing must be reliable to fulfill dynamically changing end-user requirements. Key factor to handle this issue is the elasticity. Elasticity represents the ability to dynamically reconfigure cloud resources to adapt to these varying resource requirements. With the dynamic nature of cloud resources and rapid change of user requirements, elasticity has to be effectively supported. This involves considering possible obstacles that can hamper the successful execution of elasticity actions. More specifically, obstacles are situations that could evolve into failures, if they are not efficiently and promptly (i.e.,~no preventive measures taken) tackled. In this paper, we propose a transactional approach to effectively support reliable cloud resource elasticity by handling obstacles. Our proposition is built on an existing cloud resource description model but with emphasis on transactional properties during the execution of elasticity actions. We rely on a proof of concept implementation to demonstrate the applicability and the feasibility of our approach.
Localization and Navigation Omni-directional Robots based on Sensors Fusion and Particle Filter

One of the main challenges in autonomous mobile robots is robust and accurate navigation of a robot in its environment. Obviously, this is conceivable when the precise position of the robot in the environment is needed to get by a control algorithm before navigation. Due to the lack of coverages for using one sensor in environmental conditions, multi-sensor data fusion is one of the successful solutions to overcome these problems. This paper presents an efficient localization and navigation of an industrial robot using integration of multi sensors. To do this, the relative position of the robot is obtained by passing normalized data of laser scanner, compass and encoder sensors to a Kalman filter. Due to relative and accumulated errors in the Kalman filter, exact position is obtained by applying a particle filter on environment's map. The proposed method has been run on a real industrial robot in an industrial environment according to RoboCup rules. The experimental results achieved in mean error $x, y$, θ was 0.98 and 0.93 and 0.09 cm / m respectively. These results show that have much lower error than the other test results of each of the sensors used alone.
A First Look at Instant Service Consumption with Quick Apps on Mobile Devices

Mobile app ecosystem has gained giant success in providing services on mobile devices to facilitate almost all aspects in our daily life. However, the whole-package installation and dramatically increasing package size are now preventing users from trying more apps. To address the issue, many lightweight frameworks have emerged, enabling to provide the experience of instant service consumption where apps are of small size and no installation is needed to consuming services provided by the apps. In this paper, we conduct the first empirical study on instant service consumption on mobile devices. We focus on one of the most popular frameworks, quick apps, which are proposed and supported by nine mainstream mobile phone manufacturers in China. Quick apps are implemented with Web-based technologies, and run as native apps without the need of installation. We find that quick apps have much smaller size and only provide a limited set of services compared to their corresponding native apps. Then, we characterize the performance differences between quick apps and native apps in terms of launching time, data drain, and network connections, when the two kinds of apps provide the same services. Our observations reveal that quick apps perform better than native apps thanks to its much smaller size and less functionalities in a single page. Finally, we propose a machine learning based approach to helping developers construct the quick app from an existing native app.
Disturbance observers for general exogenous disturbances

We examine a parameterization of linear disturbance observers for general exogenous disturbances. Using the states and inputs of the system as variables, we got an parameterization of linear disturbance observers for general exogenous disturbances in frequency domain formulation. By comparing with a traditional linear disturbance observer for general exogenous disturbances in time domain formulation, we found that the traditional disturbance observer can be regarded as a specific formulation of the disturbance observer designed by us, therefore the validity of our disturbance observer should could be confirmed. We will show an numerical example to confirm the validity of our method. In another work, we have proposed a parameterization of linear disturbance observers for constant disturbances individually. Because constant disturbances can be regarded as a special case for general exogenous disturbances, we think our disturbance observer for general exogenous disturbances also could be used to estimate constant disturbances. We will offer numerical examples to confirm the guess too. Let aside of rapidity, the validity of our parameterization is undoubted.
Synthesis of Monitoring Rules via Data Mining

In online monitoring of critical systems, it is important to detect an abnormal behavior as early as possible. Signal temporal logic (STL) formulas are used to specify these undesired behaviors due to the expressivity and interpretability of the logic and the existence of efficient online monitoring algorithms. In this paper, we present a new method to synthesize formulas that belong to past time fragment of STL from a labeled dataset. In particular, we consider a dataset that includes signals and their labels marking the moment of occurrence of undesired behaviors, and propose a formula synthesis algorithm based on data mining algorithms. We first transform the dataset into a new dataset with attributes encoding basic temporal formulas, then learn a classifier from the transformed dataset and finally generate a ptSTL formula from the classifier. The proposed method requires much less computational time compared to similar algorithms and achieves competitive detection performance as shown in the case studies.
Biomechanical Comparison of Lateral Interbody Fusion with and without Fixation

Objective: To compare biomechanical behaviors of lumbar lateral interbody fusion with and without posterior fixation. Methods: Three kinds of three-dimensional nonlinear finite element models of lumbar vertebrae (L3-S1) were developed: traditional lateral interbody fusion with bilateral pedicle 4 screws and 2 rods internal fixation; only lateral interbody fusion; intact model. The models were supported to a 500N follower load with a 7.5-N.m moment of flexion, extension,lateral bending,and rotation. The change of the range of motion (ROM) and intervertebral disc pressure (IDP) of the surgical segment and adjacent segments are compared. Results: Compared with the intact model, both surgical methods reduce the range of motion at the surgical segment in all motion modes, but have almost no effect on adjacent segments. The addition of fixation induce the further reduction of ROM in the surgical segment, and the ROM is almost 0° in all motion modes except for rotation. While for the model of only lateral interbody fusion, the ROM range from 0.4° to 2.2°. Moreover, the variation of the intervertebral disc pressure in surgery groups are extremely subtle in all the segments and all the postures. Conclusion: Compared to the only lateral interbody fusion,the traditional internal fixation with bilateral pedicle 4 screws and 2 rods for lateral interbody fusion showed more bimechanical stability of the surgical segment. The extra fixation for lumbar lateral interbody fusion is indispensable for surgeon.
Cascaded Model of Nonlinear Operator for Digital Predistortion with Memory

Nonlinear distortion with memory perturbing the wideband signal in power amplifiers (PAs) is challenging both for analytical treatment and implementation of practical PA linearizers. In the present letter we focus on a simplified cascaded model of the PA memory nonlinearity that enables to approach such problems as the effect of modeling and identification errors and undersampling on the digital predistortion performance. Based on the presented analysis, we propose a cascaded nonlinearity model for predistorter with a 2-step identification algorithm using look-up table to improve stability of the model identification. Proposed model is shown to have significant performance gain compared to conventional memory polynomial nonlinearity model for specific models of PA distortion.
On Optimal Control Based on Parametric Gradient Approximations for Nonlinear Systems with Stochastic Parameters

This paper presents a design method for a suboptimal feedback controller to minimize the expectation of a cost function for uncertain nonlinear systems. The uncertainty is described by time-invariant stochastic parameters, which cause difficulties in solving the optimal control problem. The conventional notion of the principle of optimality cannot be applied to solve this problem. Furthermore, the optimal input and the expected cost cannot be described explicitly because of the nonlinearity of the system. These difficulties are overcome by combining a parametric approximation with a gradient-based optimization method. This approach enables us to obtain the gradient of an approximated cost function in an explicit form. A Monte Carlo (MC) approximation is employed to calculate the expectation of the derived gradient with respect to the stochastic parameters. This expected gradient is used to optimize the parameter of the controller.
A Game Theory Approach for Solving the New Concept of Car Sequencing Problem

The problem of car sequencing is considered in the literature in several contexts. The paper presents different approaches to the issue and introduces new concept of this problem, called Car Sequencing Problem 4.0, which concerns the paint shop. The problem of effective car sequencing in the paint shop results from the specifics of production process itself and from the structure of production line. Sequencing of cars intended to painting process is justified by economic reasons. The main goal is to minimize the number of costly changeovers of painting guns, resulting from color changes and synchronize those with periodic cleanings, forced by technological requirements. For this purpose, a buffer located in the paint shop is applied. In the paper a game-theoretic framework is presented to analyze the considered problem. Two games: Buffer Slot Assignment Game and Buffer-OutShuttle Game in complete context are introduced. Based on the performed simulations it is verified the influence of periodic cleaning interval on the effectiveness of the investigated approach.
FEC & BCH: Study and Implementation on VHDL

Channel encoding and Forward Error Correction is a crucial element of any communication system. This paper gives a brief overview of the fundamentals, mechanism and importance of Forward Error Correction. The design and implementation of a (63,36,5) BCH Codec is also projected in the later sections. All simulations are made on MATLAB R2018b and the VHDL implementations have been carried out using Xilinx Vivado 2018.2.
Urban Localization with Street Views using a Convolutional Neural Network for End-to-End Camera Pose Regression

This paper presents an end-to-end real-time monocular absolute localization approach that uses Google Street View panoramas as a prior source of information to train a Convolutional Neural Network (CNN). We propose an adaptation of the PoseNet architecture [8] to a sparse database of panoramas. We show that we can expand the latter by synthesizing new images and consequently improve the accuracy of the pose regressor. The main advantage of our method is that it does not require a first passage of an equipped vehicle to build a map. Moreover, the offline data generation and CNN training are automatic and does not require the input of an operator. In the online phase, the approach only uses one camera for localization and regresses poses in a global frame. The conducted experiments show that augmenting the training set as presented in this paper drastically improves the accuracy of the CNN. The results, when compared to a handcrafted-feature-based approach, are less accurate (around 7.5 to 8 m against 2.5 to 3 m) but also less dependent on the position of the camera inside the vehicle. Furthermore, our CNN-based method computes the pose approximately 40 times faster (75 ms per image instead of 3 s) than the handcrafted approach.
A Study of the Effect of Dielectric Composition on Metamaterial Performance in a Multilayer Environment

This paper discusses the effect of dielectric composition on metamaterial performance in a multilayer substrate environment. It is a well-established fact that metamaterials provide negative permittivity and permeability values to a non-magnetic conducting structure by their design in the designated band of frequencies. This work delves into an analysis of how a metamaterial structure designed for 10 GHz operating frequency can be made to operate at different resonance frequencies by selecting the substrate layers in a stacked configuration. The performance exhibited by the multilayer substrates exhibit the same characteristics as that of a single layer one. The reference substrate material selected is FR4 for 10 GHz operation. Subsequently, one material with higher permittivity value (Alumina 96%) and second with lower permittivity value (Rogers RT/Duroid 5880) are incorporated into the design to analyse the performance.