Open Computer Research Articles

Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

Abstract: Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

Research on Face Recognition Based on Improved Convolutional Neural Network Using Raspberry Pi

In this paper, a face recognition system has been designed using Raspberry Pi based on convolution neural network technology. Convolution neural network that using OpenCV (open source computer vision library) to realize real-time face recognition and feature extraction. The difficult and important problem in deep learning network is that it could be over fitting very easily. In order to alleviate the over fitting phenomenon and improve the accuracy of face recognition, the dropout technology of convolutional neural network had been employed to improve the recognition accuracy and ensure the robustness of face recognition. The results of experiments show that our methods has the advantages in the computer time and recognition rate for the low construction cost, simple structure and strong portability. The accuracy of face detection for static and dynamic images using Raspberry Pi is above 85% in our testament.

MyelTracer: A Semi-Automated Software for Myelin g-Ratio Quantification.

In the central and peripheral nervous systems, the myelin sheath promotes neuronal signal transduction. The thickness of the myelin sheath changes during development and in disease conditions like multiple sclerosis. Such changes are routinely detected using electron microscopy through g-ratio quantification. While g-ratio is one of the most critical measurements in myelin studies, a major drawback is that g-ratio quantification is extremely laborious and time-consuming. Here, we report the development and validation of MyelTracer, an installable, stand-alone software for semi-automated g-ratio quantification based on the Open Computer Vision Library (OpenCV). Compared with manual g-ratio quantification, using MyelTracer produces consistent results across multiple tissues and animal ages, as well as in remyelination after optic nerve crush, and reduces total quantification time by 40-60%. With g-ratio measurements via MyelTracer, a known hypomyelination phenotype can be detected in a Williams syndrome mouse model. MyelTracer is easy to use and freely available for Windows and Mac OS X (https://github.com/HarrisonAllen/MyelTracer).

TOOLS AND SERVICES OF CLOUD-ORIENTED OPEN SCIENCE SYSTEMS FOR LYCEUM TEACHERS PROFESSIONAL DEVELOPMENT

The article is devoted to the urgent problem of using the tools and services of cloud-oriented systems for the professional development teachers of lyceums as a new type of general secondary education institution as well as the effective use of these tools and services to ensure the quality of specialized education. The purpose of the article is to substantiate the choice of tools and services of cloud-oriented open science systems for the professional development of lyceum teachers. The directions of lyceum teacher’s professional development are determined with the help of such research methods as analysis, generalization, systematization of scientific and scientific-methodical sources on the research problem, and analysis of cloud-oriented tools and services. The author proposes to select tools and services by categories that cover the full range of educational activities of teachers and use innovative approaches and technologies such as open science cloud services and computer modeling systems. The teachers' use of the AiiDA cloud-oriented environment provides access to a number of additional services, such as lecture recordings and interviews on specific aspects and results of groundbreaking research; collections of short training courses on selected topics conducted by renowned lecturers from around the world. The author notes that for biology and chemistry teachers, it would be appropriate to familiarize themselves with the 3DBIONOTES-WS web application (online process modeling), designed for adding biochemical and biomedical information to structural models by scientists from around the world. The author focuses the attention of teachers, in particular natural sciences and mathematics teachers, on the use of competency tasks and computer modeling systems in the educational process. The research provides examples of tasks detailing both areas of long-term teacher development and methodological aspects of new technologies application in educational practice.

An Illegal Dumping Detection System Based on Image Processing in OpenALPR

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field.

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

In Vivo Time-Lapse Imaging and Analysis of Dendritic Structural Plasticity in Xenopus laevis Tadpoles.

In vivo time-lapse imaging of complete dendritic arbor structures in tectal neurons of Xenopus laevis tadpoles has served as a powerful in vivo model to study activity-dependent structural plasticity in the central nervous system during early development. In addition to quantitative analysis of gross arbor structure, dynamic analysis of the four-dimensional data offers particularly valuable insights into the structural changes occurring in subcellular domains over experience/development-driven structural plasticity events. Such analysis allows not only quantifiable characterization of branch additions and retractions with high temporal resolution but also identification of the loci of action. This allows for a better understanding of the spatiotemporal association of structural changes to functional relevance. Here we describe a protocol for in vivo time-lapse imaging of complete dendritic arbors from individual neurons in the brains of anesthetized tadpoles with two-photon microscopy and data analysis of the time series of 3D dendritic arbors. For data analysis, we focus on dynamic analysis of reconstructed neuronal filaments using a customized open source computer program we developed (4D SPA), which allows aligning and matching of 3D neuronal structures across different time points with greatly improved speed and reliability. File converters are provided to convert reconstructed filament files from commercial reconstruction software to be used in 4D SPA. The program and user manual are publicly accessible and operate through a graphical user interface on both Windows and Mac OSX.

Applications, databases and open computer vision research from drone videos and images: a survey

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.

Use of computational fluid dynamics for 3D fiber tract visualization on human high-thickness histological slices: histological mesh tractography

Understanding the intricate three-dimensional relationship between fiber bundles and subcortical nuclei is not a simple task. It is of paramount importance in neurosciences, especially in the field of functional neurosurgery. The current methods for in vivo and post mortem fiber tract visualization have shortcomings and contributions to the field are welcome. Several tracts were chosen to implement a new technique to help visualization of white matter tracts, using high-thickness histology and dark field images. Our study describes the use of computational fluid dynamic simulations for visualization of 3D fiber tracts segmented from dark field microscopy in high-thickness histological slices (histological mesh tractography). A post mortem human brain was MRI scanned prior to skull extraction, histologically processed and serially cut at 430µm thickness as previously described by our group. High-resolution dark field images were used to segment the outlines of the structures. These outlines served as basis for the construction of a 3D structured mesh, were a Finite Volume Method (FVM) simulation of water flow was performed to generate streamlines representing the geometry. The simulations were accomplished by an open source computer fluid dynamics software. The resulting simulation rendered a realistic 3D impression of the segmented anterior commissure, the left anterior limb of the internal capsule, the left uncinate fascicle, and the dentato-rubral tracts. The results are in line with clinical findings, diffusion MR imaging and anatomical dissection methods.

Online Attendance Management System Based on Face Recognition Using CNN

Due to the growing trend of online classes, there is a pressing need to create an efficient system that can keep track of the students’ regular presence in these lectures. The traditional method of manual calculation of attendance proves to be ineffective and time-consuming. Hence, this problem can be addressed by building a system which will perform the task of marking the attendance of students by matching their faces in the multiple frames of an online class with their respective images present in the database. In this paper, face detection and recognition is being done on a video given as input to our program. Multiple faces are detected and recognised simultaneously in a class lecture. Face detection and recognition application is implemented using an open source computer vision called OpenCV. The paper doesn’t venture into real time application though it has a future in real time detection. The essential algorithms that have been extensively used are Convolution Neural Network and VGG16 architecture along with Haar features. Moreover, significant changes are to be integrated in order to bring an upheaval in the major image analysis modules such that the framework incorporates accuracy and robustness even when other variations on poses are imposed. Our framework has been evolved to detect and recognise faces in fixed time periods and mark the student’s attendance according to the average value of the faces detected and recognised within those fixed time periods such that the online attendance system can’t be evaded or cheated.

Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons

Abstract Robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. Robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact colour and to pick the object and sort it.

A FRAMEWORK TO DEVELOP INTELLIGENT SYSTEM FOR CAPTURING PRODUCT FEATURES USING OPEN CV TECHNIQUE

This research aims to propose an innovative framework using ISO14649 standard to detect defects in manufactured shaped objects or geometric surfaces of non-linear products of the CNC machine. The significant importance in order to recognize the potential to improve industry product quality inspection and encourage the waste of timing machines and product rejection. Open Computer Vision (Open CV) offers a smart, non-contact measurement and cost-effective technique to fulfil the requirements. The framework depends on the new technique of Open CV, which includes two parts: an intelligent selection of work-piece capturing the image for a particular inspection of the planar interfaces such as the hole, rectangular, pocket one, and the symmetric lighting model comparison approach for measurement of defects in the matched images. The contribution of this study is to build a structure in the computer vision method with a convolution neural network that predicts the classification of the feature for better accuracy and emphasizes the significant characteristics of the image processing technique coupled with experimental data on demanding image datasets and quality inspection measures.

Similarity indexes for scientometric research: A comparative analysis

A significant number of papers in the field of scientometrics addressed the comparisons of various similarity indexes. However, there is still a debate on the appropriateness of an index compared to others, beacause of the assessment differences reported in the literature. The objective of this paper is to make a comparative analysis of the five most used similarity indexes for the three scientometric analysis types: co-word, co-citation and co-authorship. A total of 388 papers addressing similarity indexes in scientometric analysis over three decades were retrieved from the Web of Science and examined; of which 49 were retained as the most relevant according to selective criteria. The approach consisted of building cross matrices for the five indexes (Jaccard, Dice-Sorensson, Salton, Pearson, and Association Strength) for the three types of scientometric analysis. For each of these analyses, a distinction is made between papers according to their theoretical or empirical results. Furthermore, papers are classified according to the mathematical formula of the similarity index being used (vector vs non vector). In the 49 relevant papers being selected, the comparative analysis showed that there is still no consensus on the appropriateness of an index for co-word and co-authorship analyses, while for co-citation, Salton is the widely preferred one. The Association Strength is the less covered and compared to other indexes for the three analysis types. An open source computer program was developed as a tool to facilitate empirical comparative studies of indexes. It allows generating normalized matrix of any chosen index for the two mathematical variants.

The 18th Top Chess Engine Championship, TCEC18

TCEC18 was the 18th season of the Top Chess Engine Championship and ran from May 3rd to 2nd July, 2020. TCEC has become the largest Open Computer Chess Championship. It attracts the best engines in the field and provides an opportunity for a comparative analysis of the Shannon-AB and the new Neural-Network engines’ styles of play. STOCKFISH took the Grand Champion title from LEELA CHESS ZERO with +7 in another exciting Superfinal. These two engines have dominated the field for the last seven TCEC seasons now. The attached files at http://centaur.reading.ac.uk/91839 provide the 628 games with engine PVs, the detail on them, some summary statistics and the completion to mate of the wins.

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda.

Trail bridges can improve access to critical services such as health care, schools, and markets. In order to evaluate the impact of trail bridges in rural Rwanda, it is helpful to objectively know how and when they are being used. In this study, we deployed motion-activated digital cameras across several trail bridges installed by the non-profit Bridges to Prosperity. We conducted and validated manual counting of bridge use to establish a ground truth. We adapted an open source computer vision algorithm to identify and count bridge use reflected in the digital images. We found a reliable correlation with less than 3% error bias of bridge crossings per hour between manual counting and those sites at which the cameras logged short video clips. We applied this algorithm across 186 total days of observation at four sites in fall 2019, and observed a total of 33,800 daily bridge crossings ranging from about 20 to over 1,100 individual uses per day, with no apparent correlation between daily or total weekly rainfall and bridge use, potentially indicating that transportation behaviors, after a bridge is installed, are no longer impacted by rainfall conditions. Higher bridge use was observed in the late afternoons, on market and church days, and roughly equal use of the bridge crossings in each direction. These trends are consistent with the design-intent of these bridges.

Construction and Implementation of Open Computer Innovation Experiment Teaching Mode

The organic combination of open and innovative practice teaching mode and computer application helps to improve the backward practice education mode and enhance the design, comprehensiveness and innovation of innovative experimental teaching, so it has high research value. Based on this, this paper first analyses the current situation and problems of open innovation experimental teaching, then studies the construction of open computer innovation experimental teaching platform, and finally gives the implementation strategy of open computer experimental teaching mode.

Design and Research of Access Control System for Virtual Cloud Desktop

Virtual cloud computing is a typical application of cloud computing. Based on physical server and desktop virtualization technology, hardware, software, network and data are allocated with virtualization means to provide flexible and scalable service platform for computer applications, which can make more effective use of IT resources, greatly reduce the maintenance and management costs of equipment and application environment, and provide a share Open and efficient experimental computer teaching environment. This paper introduces the relevant technologies of virtual cloud desktop, analyzes the overall architecture of virtual desktop platform and its application in teaching management, and provides ideas and methods for computer experiment teaching innovation management.

VR Open Computer Network Virtual Laboratory Based on Big Data Technology

The rapid development of computer technology and network bandwidth has produced unprecedented influence on the way of human learning. Along with the humanized development of computer software technology and high-level program language, experimental teaching is gradually becoming virtualized. Not only a variety of multimedia teaching courseware emerges one after another, but also a large number of virtual laboratories appear on the network. Virtual laboratory is essentially the simulation of real experiment scene, in which students should have similar experience and the same learning effect as real laboratory. Therefore, the virtual laboratory should be able to satisfy the students to carry out autonomous exploratory operation. The purpose of this paper is to study the open computer network virtual laboratory based on big data technology. This paper first introduces the meaning of big data technology, then studies the content of video decoding technology, and then introduces the definition scope and characteristics of virtual reality technology. This paper designs and implements VR open computer network virtual laboratory based on big data technology. The test results show that the system designed in this paper has good performance. The CPU consumption rate of the system server designed in this paper only accounts for 4.5%.

Implicit discrete ordinates discontinuous Galerkin method for radiation problems on shared-memory multicore CPU/many-core GPU computation architecture

The implicit discrete ordinates (SN ) discontinuous Galerkin (DG)/generalized minimal residual (GMRES) method has been implemented on shared-memory, multicore CPU/many-core GPU heterogeneous computation architecture via standard application programming interfaces (APIs) of open multiprocessing (OpenMP) and computer unified device architecture (CUDA). The compiler derivative based OpenMP parallelization has been performed to port the sequential SN DG/GMRES implementation to run in parallel on multiple CPU cores that share global memory. Benchmarked on a workstation comprising 12 Intel(R) Xeon(R) CPU cores, a parallel efficiency of ∼90% has been achieved for all reference problems. The ILUT preconditioning has been performed to improve algebraic conditions of associated linear systems, and it has been demonstrated that preconditioning has accelerated simulations of all reference problems by a factor of 10. Finally, it has been showcased that a K20m GPU has boosted the computation capacity of an Intel(R) Xeon(R) CPU core by a modest factor of 2–4 for all the reference problems.

Elastic medium containing interacting fluid inclusions

We extend the Eshelby’s (equivalent) inclusion method to consider ellipsoidal volumes occupied by fluids of limited compressibility. The new method decomposes the problem into two parts – a stress free transformation problem, and a volume compatibility problem. Implemented in an open source computer code, Esh3D, the new method allows one to model interacting and fluid–solid mixed inclusions in whole and truncated spaces. We verify the new method against finite element (FE) method, showing it to be accurate and efficient.