Linux System Research Articles

Remote Data Transmission of Intelligent Cloud Robot Based on Google Protobuf

With the maturity of robotic remote control technology, it plays an important role in military, exploration and industry. Their application in the field of daily life is also expanding. The development of 4G Mobile Internet and cloud computing technology provides a new platform for robot remote control technology. In order to realize the communication function of robot remote control, this paper designs a data transmission method based on protobuf tool. According to the characteristics of various operating systems, we choose the Linux system as the robot platform, the Alibaba Cloud server as the relay platform, and the windows application program as the implementation platform. After the system test, when the network is in good condition, through the control terminal, the real-time video collected by the robot can be viewed smoothly, and the command can be sent to control the motion of the robot.

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

The dramatic increase in the amount of garbage and complex diversity of the materials in the garbage bring serious environmental pollution problems and wastes resources. Recycling reduces waste but manual pipeline waste sorting involves a harsh working environment at high labor intensity with low sorting efficiency. In our paper, a novel intelligent garbage classification system based on deep learning and an embedded Linux system is proposed. The system is divided into three parts. First, a Raspberry Pi 4B is utilized as the master board for the hardware system. The peripherals of the system consist of a touch panel, sensors, a 2-DOF (degree of freedom) servo, and a camera. Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. Third, a GUI based on Python and QT is employed to build a human-computer interaction system to facilitate system manipulation and observation. A series of garbage classification experiments on the Huawei Garbage Classification Challenge Cup dataset were conducted. The proposed classification system’s prediction accuracy was 92.62% at 0.63 s efficiency. The experimental results in this paper demonstrate that the proposed intelligent garbage classification system delivers high performance both in terms of accuracy and efficiency.

Read-Ahead Efficiency on Mobile Devices: Observation, Characterization, and Optimization

Read-ahead schemes have been widely used in page cache to improve read performance of Linux systems. As the Android system inherits the Linux kernel, the traditional read-ahead scheme is directly transplanted to mobile devices. However, request sizes and page cache sizes on mobile devices are much smaller, which may degrade read-ahead efficiency and therefore hurt user experience. This article first observes that many pages pre-fetched by read-ahead are unused, which causes frequent page cache eviction. And these evict operations could induce extra access latency, especially when write-back is conducting. Then, this article proposes a new analysis model to characterize the factors that closely relate to the access latency. It is found that there exists a trade-off between read-ahead size and access latency. Finally, this article proposes two optimized read-ahead schemes to exploit this trade-off under different situations. Size-tuning scheme aims to find the proper maximum size of read-ahead according to the characteristics of mobile devices. While MobiRA scheme improves the read-ahead efficiency by dynamically tuning read-ahead size and stop-settings. Experimental results on real mobile devices show that the proposed schemes can increase the efficiency of read-ahead scheme and improve the overall performance of mobile devices.

Adaptive Page Migration Policy With Huge Pages in Tiered Memory Systems

To accommodate the growing demand for memory capacity in a cost-effective way, multiple types of memory are incorporated in a single system. In such tiered memory systems consisting of small fast and large slow memory components, accurately identifying the performance importance of pages is critical to properly migrate hot pages to fast memory. Meanwhile, growing address translation cost due to the increasing memory footprints, helped adopting huge pages in common systems. Although such page hotness identification problems have existed for a long time, this article revisits the problem in the new context of tiered memory systems and huge pages. This article first investigates the memory locality behaviors of applications with three potential migration polices, least-recently-used (LRU), least-frequently-used (LFU), and random with huge pages. The evaluation shows that none of the three migration policies excel the others, as the effectiveness of each policy depends on application behaviors. In addition, the results show huge pages can be effective even with page migration, if a proper migration policy is used. Based on the observation, this paper proposes a novel dynamic policy selection mechanism, which identifies the best migration policy for a given workload. It allows multiple concurrently running workloads to adopt different policies. To find the optimal one for each workload, this study first identifies key features that must be inferred from limited approximate memory access information collected using accessed bits in page tables. In addition, it proposes a parallel emulation of alternative policies to assess the benefit of possible alternatives. The proposed dynamic policy selection can achieve 23.8percent performance improvement compared to a prior approximate mechanism based on LRU lists in Linux systems.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2's unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

SLR-SELinux: Enhancing the Security Footstone of SEAndroid with Security Label Randomization

The root privilege escalation attack is extremely destructive to the security of the Android system. SEAndroid implements mandatory access control to the system through the SELinux security policy at the kernel mode, making the general root privilege escalation attacks unenforceable. However, malicious attackers can exploit the Linux kernel vulnerability of privilege escalation to modify the SELinux security labels of the process arbitrarily to obtain the desired permissions and undermine system security. Therefore, investigating the protection method of the security labels in the SELinux kernel is urgent. And the impact on the existing security configuration of the system must also be reduced. This paper proposes an optimization scheme of the SELinux mechanism based on security label randomization to solve the aforementioned problem. At the system runtime, the system randomizes the mapping of the security labels inside and outside the kernel to protect the privileged security labels of the system from illegal obtainment and tampering by attackers. This method is transparent to users; therefore, users do not need to modify the existing system security configuration. A tamper-proof detection method of SELinux security label is also proposed to further improve the security of the method. It detects and corrects the malicious tampering behaviors of the security label in the critical process of the system timely. The above methods are implemented in the Linux system, and the effectiveness of security defense is proven through theoretical analysis and experimental verification. Numerous experiments show that the effect of this method on system performance is less than 1%, and the success probability of root privilege escalation attack is less than 10−9.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2’s unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

Simflowny 3: An upgraded platform for scientific modeling and simulation

Simflowny is an open platform which automatically generates efficient parallel code of scientific dynamical models for different simulation frameworks. Here we present major upgrades on this software to support simultaneously a quite generic family of partial differential equations. These equations can be discretized using: (i) standard finite-difference for systems with derivatives up to any order, (ii) High-Resolution-Shock-Capturing methods to deal with shocks and discontinuities of balance law equations, and (iii) particle-based methods. We have improved the adaptive-mesh-refinement algorithms to preserve the convergence order of the numerical methods, which is a requirement for improving scalability. Finally, we have also extended our graphical user interface (GUI) to accommodate these and future families of equations. This paper summarizes the formal representation and implementation of these new families, providing several validation results. Program summaryProgram Title: SimflownyCPC Library link to program files:https://doi.org/10.17632/g9mcw8s64f.2Licensing provisions: Apache License, 2.0Programming language: Java, C++ and JavaScriptJournal Reference of previous version: Comput. Phys. Comm. 184 (2013) 2321–2331, Comput. Phys. Comm. 229 (2018), 170–181Does the new version supersede the previous version?: YesReasons for the new version: Additional featuresSummary of revisions: Expanded support for Partial Differential Equations, meshless particles and advanced Adaptive Mesh Refinement.Nature of problem: Simflowny generates numerical simulation code for a wide range of models.Solution method: Any discretization scheme based on either Finite Volume Methods, Finite Difference Methods, or meshless methods for Partial Differential Equations.Additional comments: Simflowny runs in any computer with Docker [1]. Installation details can be checked in the documentation of Simflowny [2]. It can also be compiled from scratch on any Linux system, provided dependences are properly installed as indicated in the documentation. The generated code runs on any Linux platform ranging from personal workstations to clusters and parallel supercomputers. The software architecture is easily extensible for future additional model families and simulation frameworks. Full documentation is available in the wiki home of the Simflowny project [2].

IP4M: an integrated platform for mass spectrometry-based metabolomics data mining

BackgroundMetabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users.ResultsIntegrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC–MS and LC–MS respectively.ConclusionIP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided.

Developing a real time navigation for the mobile robots at unknown environments

<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>

Realization and application of geological cloud platform

ABSTRACT In recent years, with the progress of computer technology, some traditional industries such as geology are facing changes in industrial structure and application mode. So we try to apply big data and virtualization technology in the field of geoscience. This study aims at addressing the existing problems in geological applications, such as data sharing, data processing and computing performance. A Geological Cloud Platform has been designed and realized preliminarily with big data and virtualization technology. The application of the Geological Cloud Platform can be divided into two parts: 1) to nest the geological computing model in cloud platform and visualize the results and 2) to use relevant software to conduct data analysis and processing in virtual machines of Windows or Linux system. Finally, we prospect Carlin-type deposits in Nevada by using the spatial data model ArcSDM in the virtual machine.

A MultiStack Parallel (MSP) Partition Algorithm Applied to Sorting

The CPUs of smartphones are becoming multicore with huge RAM and storage to support a variety of multimedia applications in the near future. A MultiStack Parallel (MSP) sorting algorithm is proposed and named MSPSort to support manycore systems. It can be regarded as many threads of single-pivot interleaving block-based Hoare’s algorithm. Each thread performs compare-swap operations between left and right (stacked and interleaved) data blocks. A number of multithreading features of OpenMP and our own optimization strategies have been utilized. To simulate those smartphones, MSPSort is fine tuned and tested on four Linux systems, e.g. Intel i7-2600, Xeon X5670, AMD R7-1700 and R9-2920. Their memory configurations can be classified as either uniform or non-uniform memory access. The statistical results are satisfied compared to parallel-mode sorting algorithms of Standard Template Library, namely Balanced QuickSort and MultiWay MergeSort. Moreover, MSPSort looks promising to be developed further to improve both run time and stability.

Producer-consumer Model Based Thread Pool Design

The concurrency of the server has a crucial impact on the efficiency of processing requests on the server side, so the thread pool technology has been widely applied to the server. Different thread pools play an important role in improving the performance of highly concurrent servers. This article first introduces socket technology, thread pools, etc., which are high-concurrency server development technologies in Linux systems, then proposes a thread pool model based on the producer consumer model, and gives key codes. Finally, the performance is compared through experiments . Experimental results show that lightweight servers can achieve higher concurrency by using this thread pool model.

Implementation of IOT Based Smart Energy Meter in Oman

Managing the energy efficient and conserving it intelligently for appliances is very much important. On the other side, it may be possible events mistake cause while reading on energy meter, monitoring and keeping track of your electricity consumption for verification is a tedious task today. Our main objective of measuring the power consumption at homes using IOT with raspberry pi during period time, which can be controlled as well monitored through the raspberry pi across the IOT. We used Python programming language to control raspberry pi. It's based on raspbian which is operating system for all models of the raspberry Pi that subject to linux system. As we say before raspberry pi has inputs and we use it for connecting the supply, energy meter and load such as a lamp or Drill. The energy meter is connected to the raspberry pi. This allows user to easily check the energy usage along with the cost charged online using a simple web application connecting to Wi-Fi. Thus, the energy meter monitoring system allows consumer to effectively monitor electricity meter readings and bill amount in an easy way. It presents a low cost and flexible energy meter monitoring system using IOT. In addition, we use camera which is called camera pi. Camera pi takes picture from meter reading and communicates to consumer via email. All information on the energy meter screen will be taken by raspberry pi module. Using this data, the raspberry pi will calculate the bill amount then send to the consumer by email. Finally, this project will help for the proper and accurate reading of the billing process automatically. Also, it enables consumer to save the money for a long time. This technology offers new and exciting opportunity to reduce the work of workers.

Construction of Lightweight Big Data Experimental Platform Based on Dockers Container

To reduce the large data experiment platform construction cost and reduce the learning difficulty big data, this article is based on virtualization technology through the Docker software installed on the Linux system, using the Open VpN routing forwarding, using Java web technology, realizing the big data within the local area network (LAN) cluster environment fleetly, and constructed of lightweight data experiment platform. Through this platform, we can create a big data cluster with one key, provide a variety of experimental environments matching the courses, focus on the technology itself, and greatly improve learning efficiency. Experimental analysis shows that the proposed construction method has a host occupancy rate of around 10% and a memory occupancy rate of around 10%, and the system runs stably.

A Study of Memory Placement on Hardware-Assisted Tiered Memory Systems

Recent advances in memory technology, memory hierarchy is becoming diverse with performance-differentiated memory such as high bandwidth memory (HBM) and non-volatile memory (NVM) in modern computer systems. However, the current memory placement has been designed with the assumption that all the memory has the same capabilities based on DRAM. In this letter, we analyze memory placement schemes in state-of-the-art Linux systems to the hardware-assisted tiered memory system. Our analysis is conducted on the real system equipped with Intel's Optane Persistent Memory, enabling tiered memory with DRAM (fast) and Optane (slow) memory. We observe that tiered memory augmented on traditional NUMA form do not exhibit that accessing the local memory provides better performance than that of the remote memory because local Optane memory is slower than remote DRAM. Due to this distinct characteristic, there are several inefficiencies in the commodity operating systems. To make use of tiered memory systems efficiently, this paper explores the design space of practical software solutions, which can be currently applicable in the Linux system.

Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity.

The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections of genomes from resources such as GISAID must be subsampled to enable computationally feasible phylogenetic and other analyses. We present genome-sampler, a software package that supports sampling collections of viral genomes across multiple axes including time of genome isolation, location of genome isolation, and viral diversity. The software is modular in design so that these or future sampling approaches can be applied independently and combined (or replaced with a random sampling approach) to facilitate custom workflows and benchmarking. genome-sampler is written as a QIIME 2 plugin, ensuring that its application is fully reproducible through QIIME 2's unique retrospective data provenance tracking system. genome-sampler can be installed in a conda environment on macOS or Linux systems. A complete default pipeline is available through a Snakemake workflow, so subsampling can be achieved using a single command. genome-sampler is open source, free for all to use, and available at https://caporasolab.us/genome-sampler. We hope that this will facilitate SARS-CoV-2 research and support evaluation of viral genome sampling approaches for genomic epidemiology.

FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems

As existing system calls-based Host Anomaly Detection Systems (HADSs) exclude hidden patterns that can reside in the elapsed times of system calls with respect to the lifecycle of a kernel-calling process, they lack precision in the construction of behavioral regions for assisting in reliably protecting hosts against modern unknown attacks. In this paper, a HADS, the so-called Fuzzy Gaussian Mixture-based Correntropy (FGMC-HADS), based on the fuzzy rough set attribute reduction (FRAR) method, Gaussian mixture model (GMM) and Correntropy mechanism, is proposed. FGMC-HADS comprises two novel modules: (1) the FRAR method is applied to combine system calls’ identifiers and elapsed times to construct relevant hidden patterns; and (2) the GMM and Correntropy approaches is an anomaly detection technique, the so-called ’Corr-GMM’, developed to fuse multivariate features and recognize unknown anomalous activities, respectively. The posterior probabilities of the GMM are used as input to the Correntropy model to determine the time-series interdependencies of host activities, and then the Corr-GMM constructs legitimate boundaries as a threshold for discovering abnormal behaviors. The proposed FGMC-HADS is trained and validated using the datasets of NGIDS-DS, KDD-98 and new ToN_IoT of Linux data. The experimental results indicate that the proposed FGMC-HADS a reliable defense layer for Linux-based hosts against unknown attacks compared with other compelling HIDS techniques.

Design of Light Emitting Diodes (LEDs) Lighting System and Its Application in Garden Landscape Decoration

Light Emitting Diode (LED) is widely used in garden landscape decoration because of its small size, low power, concentrated light, and the capability of showing more vivid colors. While designing the LED lighting system, considering that a single Advanced RISC Machine (ARM)-based control system cannot achieve large-scale LED display, and a single Field Programmable Gate Array (FPGA)-based control system cannot control the lighting system well, an LED system with the combination of ARM processor-FPGA is proposed. In this system, the ARM processor is used as the major control component. The Linux system realizes remote monitoring and intelligent management of image data. In addition, FPGA is used for LED data output. The lighting system consists of a major control node and a lighting node. The nodes are connected in parallel through a chain network. The major control node uses an ARM Cortex processor and is equipped with a Linux operating system. The lighting node uses ARM + FPGA hardware architecture. During the experiments, the LED lighting system is tested first. The results show that the reading and writing speed is fast. The LED display screen meets the lighting requirements. This LED lighting system is used for night lighting of garden landscapes. During the brightness test, the brightness of lighting objects and the background is used as research objects. Experiments have proved that the ratio of the lighting object brightness to the background brightness between (Yu, M. and Li, X., 2012. A little current k-factor method for measuring junction temperature of aviation lighting power led. Guangxue Jishu/Optical Technique, 38(3), pp.371–375; Monas, A., Verma, A., Gawari, A. and Paswan, R. S., 2016. Portable network monitor using arm processor. Procedia Computer Science, 92, pp.493–497.) is suitable for night lighting of garden landscape decoration, which will not bring discomfort to people who enjoy night scenery.

Jackalope: A swift, versatile phylogenomic and high-throughput sequencing simulator.

High-throughput sequencing (HTS) is central to the study of population genomics and has an increasingly important role in constructing phylogenies. Choices in research design for sequencing projects can include a wide range of factors, such as sequencing platform, depth of coverage and bioinformatic tools. Simulating HTS data better informs these decisions, as users can validate software by comparing output to the known simulation parameters. However, current standalone HTS simulators cannot generate variant haplotypes under even somewhat complex evolutionary scenarios, such as recombination or demographic change. This greatly reduces their usefulness for fields such as population genomics and phylogenomics. Here I present the R package jackalope that simply and efficiently simulates (i) sets of variant haplotypes from a reference genome and (ii) reads from both Illumina and Pacific Biosciences platforms. Haplotypes can be simulated using phylogenies, gene trees, coalescent-simulation output, population-genomic summary statistics, and Variant Call Format (VCF) files. jackalope can simulate single, paired-end or mate-pair Illumina reads, as well as reads from Pacific Biosciences. These simulations include sequencing errors, mapping qualities, multiplexing and optical/PCR duplicates. It can read reference genomes from fasta files and can simulate new ones, and all outputs can be written to standard file formats. jackalope is available for Mac, Windows and Linux systems.