Search results for: computer game

Authors: M. Rodionov, N. Sharapova, Z. Dedovets

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The article describes methods of preparation of future teachers that includes the entire diversity of traditional and computer-oriented methodological approaches. The authors reveal how, in the specific educational environment, a teacher can choose the most effective combination of educational technologies based on the nature of the learning task. The key conditions that determine such a choice are that the methodological approach corresponds to the specificity of the problem being solved and that it is also responsive to the individual characteristics of the students. The article refers to the training of students in the proper use of mathematical electronic tools for educational purposes. The preparation of future mathematics teachers should be a step-by-step process, building on specific examples. At the first stage, students optimally solve problems aided by electronic means of teaching. At the second stage, the main emphasis is on modeling lessons. At the third stage, students develop and implement strategies in the study of one of the topics within a school mathematics curriculum. The article also recommended the implementation of this strategy in preparation of future teachers and stated the possible benefits.

Keywords: education, methodological approaches, teacher, secondary school

Procedia PDF Downloads 170

Authors: Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

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Malicious users exploit vulnerabilities in computer systems, significantly disrupting their performance and revealing the inadequacies of existing protective solutions. Even machine learning-based approaches, designed to ensure reliability, can be compromised by adversarial attacks that undermine their robustness. This paper addresses two critical aspects of enhancing model reliability. First, we focus on improving model performance and robustness against adversarial threats. To achieve this, we propose a strategy by harnessing deep reinforcement learning. Second, we introduce an approach leveraging generative adversarial networks to counter adversarial attacks effectively. Our results demonstrate substantial improvements over previous works in the literature, with classifiers exhibiting enhanced accuracy in classification tasks, even in the presence of adversarial perturbations. These findings underscore the efficacy of the proposed model in mitigating intrusions and adversarial attacks within the machine-learning landscape.

Keywords: machine learning, reliability, adversarial attacks, deep-reinforcement learning, robustness

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Authors: Kuei-Chun Liu, Yu-Yu Lai, Ching-Hong Wu

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A good automated testing tool could reduce as much as possible the manual work done by testers. Traditional record-replay testing tool provides an automated testing solution by recording mouse coordinates as test scripts, but it will be easily broken if any change of resolutions. Therefore, more and more testers design multiple test scripts to automate the testing process for different devices. In order to improve the traditional record-replay approach and reduce the effort that the testers spending on writing test scripts, we propose an approach for generating the Android application test scripts based on accessibility service without connecting to a computer. This approach simulates user input actions and replays them correctly even at the different conditions such as the internet connection is unstable when the device under test, the different resolutions on Android devices. In this paper, we describe how to generate test scripts automatically and make a comparison with existing tools for Android such as Robotium, Appium, UIAutomator, and MonkeyTalk.

Keywords: accessibility service, Appium, automated testing, MonkeyTalk, Robotium, testing, UIAutomator

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Authors: Raghabendra Yadav, Baochun Chen, Huihui Yuan, Zhibin Lian

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Pseudo-dynamic test (PDT) method is an advanced seismic test method that combines loading technology with computer technology. Large-scale models or full scale seismic tests can be carried out by using this method. CFST-RC columns are used in civil engineering structures because of their better seismic performance. A CFST-RC column is composed of four CFST limbs which are connected with RC web in longitudinal direction and with steel tube in transverse direction. For this study, a CFST-RC pier is tested under Four different earthquake time histories having scaled PGA of 0.05g. From the experiment acceleration, velocity, displacement and load time histories are observed. The dynamic magnification factors for acceleration due to Elcentro, Chi-Chi, Imperial Valley and Kobe ground motions are observed as 15, 12, 17 and 14 respectively. The natural frequency of the pier is found to be 1.40 Hz. The result shows that this type of pier has excellent static and earthquake resistant properties.

Keywords: bridge pier, CFST-RC pier, pseudo dynamic test, seismic performance, time history

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Suman Bala, Sunil Kamboj, Vipin Saini

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Quantitative Structure Activity Relationship (QSAR) analysis has been developed to relate antifungal activity of novel substituted 1,3,4-oxadiazole against Candida albicans and Aspergillus niger using computer assisted multiple regression analysis. The study has shown the better relationship between antifungal activities with respect to various descriptors established by multiple regression analysis. The analysis has shown statistically significant correlation with R² values 0.932 and 0.782 against Candida albicans and Aspergillus niger respectively. These derivatives were further subjected to molecular docking studies to investigate the interactions between the target compounds and amino acid residues present in the active site of glucosamine-6-phosphate synthase. All the synthesized compounds have better docking score as compared to standard fluconazole. Our results could be used for the further design as well as development of optimal and potential antifungal agents.

Keywords: 1, 3, 4-oxadiazole, QSAR, multiple linear regression, docking, glucosamine-6-phosphate synthase

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

Procedia PDF Downloads 103

Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Harpa Ragnarsdóttir, Magnús Kjartan Gíslason, Kristín Briem, Guðný Lilja Oddsdóttir

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Introduction: Whiplash-Associated Disorder (WAD) is a health problem characterized by motor, neurological and psychosocial symptoms, stressing the need for a multimodal treatment approach. To achieve individualized multimodal approach, prognostic factors need to be identified early using validated patient-reported and objective outcome measures. The aim of this study is to demonstrate the degree of association between patient-reported and clinical outcome measures of WAD patients in the subacute phase. Methods: Individuals (n=41) with subacute (≥1, ≤3 months) WAD (I-II), medium to high-risk symptoms, or neck pain rating ≥ 4/10 on the Visual Analog Scale (VAS) were examined. Outcome measures included measurements for movement control (Butterfly test) and cervical active range of motion (cAROM) using the NeckSmart system, a computer system using an inertial measurement unit (IMU) that connects to a computer. The IMU sensor is placed on the participant’s head, who receives visual feedback about the movement of the head. Patient-reported neck disability, pain intensity, general health, self-perceived handicap, central sensitization, and difficulties due to dizziness were measured using questionnaires. Excel and R statistical software were used for statistical analyses. Results: Forty-one participants, 15 males (37%), 26 females (63%), mean (SD) age 36.8 (±12.7), underwent data collection. Mean amplitude accuracy (AA) (SD) in the Butterfly test for easy, medium, and difficult paths were 2.4mm (0.9), 4.4mm (1.8), and 6.8mm (2.7), respectively. Mean cAROM (SD) for flexion, extension, left-, and right rotation were 46.3° (18.5), 48.8° (17.8), 58.2° (14.3), and 58.9° (15.0), respectively. Mean scores on the Neck Disability Index (NDI), VAS, Dizziness Handicap Inventory (DHI), Central Sensitization Inventory (CSI), and 36-Item Short Form Survey RAND version (RAND) were 43% (17.4), 7 (1.7), 37 (25.4), 51 (17.5), and 39.2 (17.7) respectively. Females showed significantly greater deviation for AA compared to males for easy and medium Butterfly paths (p<0.05). Statistically significant moderate to strong positive correlation was found between the DHI and easy (r=0.6, p=0.05), medium (r=0.5, p=0.05)) and difficult (r=0.5, p<0.05) Butterfly paths, between the total RAND score and all cAROMs (r between 0.4-0.7, p≤0.05) except flexion (r=0.4, p=0.7), and between the NDI score and CSI (r=0.7, p<0.01), VAS (r=0.5, p<0.01), and DHI (r=0.7, p<0.01) scores respectively. Discussion: All patient-reported and objective measures were found to be outside the reference range. Results suggest females have worse movement control in the neck in the subacute WAD phase. However, no statistical difference based on gender was found in patient-reported measures. Suggesting females might have worse movement control than males in general in this phase. The correlation found between DHI and the Butterfly test can be explained because the DHI measures proprioceptive symptoms like dizziness and eye movement disorders that can affect the outcome of movement control tests. A correlation was found between the total RAND score and cAROM, suggesting that a reduced range of motion affects the quality of life. Significance: The NeckSmart system can detect abnormalities in cAROM, fine movement control, and kinesthesia of the neck. Results suggest females have worse movement control than males. Results show a moderate to a high correlation between several patient-reported and objective measurements.

Keywords: whiplash associated disorders, car-collision, neck, trauma, subacute

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Authors: Carla Silva

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Recent development of information and communication technology enables us to acquire, collect, analyse data in various fields of socioeconomic – technological systems. Along with the increase of economic globalization and the evolution of information technology, data mining has become an important approach for economic data analysis. As a result, there has been a critical need for automated approaches to effective and efficient usage of massive amount of educational data, in order to support institutions to a strategic planning and investment decision-making. In this article, we will address data from several different perspectives and define the applied data to sciences. Many believe that 'big data' will transform business, government, and other aspects of the economy. We discuss how new data may impact educational policy and educational research. Large scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe educational activity and educational impact. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in educational and furthermore in economics. Finally, we highlight a number of challenges and opportunities for future research.

Keywords: data mining, research analysis, investment decision-making, educational research

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Authors: Damou Mehdi, Nouri Keltoum, Feham Mohammed, Khazini Mohammed, Bouazza Tayb Habibi Chawki

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The Half mode SIW filter is treated by two softwares (HFSS (High Frequency Structure Simulator) and CST (Computer Simulation Technology)). The filter HMSIW has a very simple structure and a very compact size. The simulated results by CST are presented and compared with the results simulated by a high-frequency structure simulator. Good agreement between the simulated CST and simulated results by HFSS is observed. By cascading two of them according to design requirement, a X-band bandpass filter is designed and simulated to meet compact size, low insertion loss, good return loss as well as second harmonic suppression. As an example, we designed the proposed HMSIW filter at X band by HFSS. The filter has a pass-band from 7.3 GHz to 9.8 GHz, and its relative operating fraction bandwidth is 29.5 %. There are one transmission zeros are located at 14.4 GHz.

Keywords: substrate integrated waveguide, filter, HMSIW, defected ground structures (DGS), simulation BPF

Procedia PDF Downloads 587

Authors: Rozo D. Juan Felipe, Ramírez L. Leonardo Juan, Puerta A. Gabriel Alberto

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The telemedicine services require correct computing resource management to guarantee productivity and efficiency for medical and non-medical staff. The aim of this study was to examine web management strategies to ensure the availability of resources and services in telemedicine so as to provide medical information management with an accessible strategy. In addition, to evaluate the quality-of-service parameters, the followings were measured: delays, throughput, jitter, latency, available bandwidth, percent of access and denial of services based of web management performance map with profiles permissions and database management. Through 24 different test scenarios, the results show 100% in availability of medical information, in relation to access of medical staff to web services, and quality of service (QoS) of 99% because of network delay and performance of computer network. The findings of this study suggest that the proposed strategy of web management is an ideal solution to guarantee the availability, reliability, and accessibility of medical information. Finally, this strategy offers seven user profile used at telemedicine center of Bogota-Colombia keeping QoS parameters suitable to telemedicine services.

Keywords: availability, medical information, QoS, strategy, telemedicine

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Authors: Dengyu You, Alireza Kashani

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This study explores the potential of 3D concrete printing in manufacturing prefabricated staircases. The applications of 3D concrete printing in large-scale construction could enhance the industry’s implementation of the Industry 4.0 concept. In addition, the current global challenge is to achieve Net Zero Emissions by 2050. Innovation in the construction industry could potentially speed up achieving this target. The 3D printing technology offers a possible solution that reduces cement usage, minimises framework wastes, and is capable of manufacturing complex structures. The performance of the 3D concrete printed lightweight staircase needs to be evaluated. In this study, the staircase is designed using computer-aided technologies, fabricated by 3D concrete printing technologies, and tested with Australian Standard (AS 1657-2018 Fixed platforms, walkways, stairways, and ladders – design, construction, and installation) under a laboratory environment. The experiment results will be further compared with the FEM analysis. The results indicate that 3D concrete printing is capable of fast production, reducing material usage, and is highly automotive, which meets the industry’s future development goal.

Keywords: concrete 3D printing, staircase, sustainability, automation

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Authors: K. Adu Michael, K. Alese Boniface

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Developing a reliable and sustainable software products is today a big challenge among up–coming software developers in Nigeria. The inability to develop a comprehensive problem statement needed to execute proper requirements engineering process is missing. The need to describe the ‘what’ of a system in one document, written in a natural language is a major step in the overall process of Software Engineering. Requirements Engineering is a process use to discover, analyze and validate system requirements. This process is needed in reducing software errors at the early stage of the development of software. The importance of each of the steps in Requirements Engineering is clearly explained in the context of using detailed problem statement from client/customer to get an overview of an existing system along with expectations from the new system. This paper elicits inadequate Requirements Engineering principle as the major cause of poor software development in developing nations using a case study of final year computer science students of a tertiary-education institution in Nigeria.

Keywords: client/customer, problem statement, requirements engineering, software developers

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

Procedia PDF Downloads 350

Authors: Alec Wells, Aminu Bello Usman, Justin McKeown

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The growing usage of smart speakers raises many privacy and trust concerns compared to other technologies such as smart phones and computers. In this study, a proxy measure of trust is used to gauge users’ opinions on three different technologies based on an empirical study, and to understand which technology most people are most likely to trust. The collected data were analysed using the Kruskal-Wallis H test to determine the statistical differences between the users’ trust level of the three technologies: smart speaker, computer and smart phone. The findings of the study revealed that despite the wide acceptance, ease of use and reputation of smart speakers, people find it difficult to trust smart speakers with their sensitive information via the Direct Voice Input (DVI) and would prefer to use a keyboard or touchscreen offered by computers and smart phones. Findings from this study can inform future work on users’ trust in technology based on perceived ease of use, reputation, perceived credibility and risk of using technologies via DVI.

Keywords: direct voice input, risk, security, technology, trust

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Authors: Mohammad Arif Khan

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This paper analyzes and presents the development of Artificial Neural Network based controller of space vector modulation (ANN-SVPWM) for a seven-phase voltage source inverter. At first, the conventional method of producing sinusoidal output voltage by utilizing six active and one zero space vectors are used to synthesize the input reference, is elaborated and then new PWM scheme called Artificial Neural Network Based PWM is presented. The ANN based controller has the advantage of the very fast implementation and analyzing the algorithms and avoids the direct computation of trigonometric and non-linear functions. The ANN controller uses the individual training strategy with the fixed weight and supervised models. A computer simulation program has been developed using Matlab/Simulink together with the neural network toolbox for training the ANN-controller. A comparison of the proposed scheme with the conventional scheme is presented based on various performance indices. Extensive Simulation results are provided to validate the findings.

Keywords: space vector PWM, total harmonic distortion, seven-phase, voltage source inverter, multi-phase, artificial neural network

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Authors: Brian Kozak, Ryan Ferguson, Evan Hockeridge

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Aerospace engineering and aeronautical engineering students studying propulsion often learn about propellers and their importance in aviation propulsion. In order to reinforce concepts introduced in the classroom, laboratory projects are used. However, to test a full scale propeller, an engine mounted on a test stand must be used. This engine needs to be enclosed in a test cell for appropriated safety requirements, is expensive to operate, and requires a significant amount of time to change propellers. In order to decrease costs and time requirements, the authors designed and built an electric motor powered desktop Arduino controlled test stand. This test stand is used to enhance student understanding of propeller size and pitch on thrust. The test stand can accommodate propellers up to 25 centimeters in diameter. The code computer allowed for the motor speed to be increased or decreased by 1% per second. Outputs that are measured are thrust, motor rpm, amperes, voltage, and motor temperature. These data are exported as a .CVS file and can be imported into a graphing program for data analysis.

Keywords: Arduino, Laboratory Project, Test stand, Propeller

Procedia PDF Downloads 219

Authors: Chien-Wei Hung, Fu-Hau Hsu, Chuan-Sheng Wang, Chia-Hao Lee

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Nowadays, the abuse of keyloggers is one of the most widespread approaches to steal sensitive information. In this paper, we propose an On-Screen Prompts Approach to Keyloggers (OSPAK) and its analysis, which is installed in public computers. OSPAK utilizes a canvas to cue users when their keystrokes are going to be logged or ignored by OSPAK. This approach can protect computers against recoding sensitive inputs, which obfuscates keyloggers with letters inserted among users' keystrokes. It adds a canvas below each password field in a webpage and consists of three parts: two background areas, a hit area and a moving foreground object. Letters at different valid time intervals are combined in accordance with their time interval orders, and valid time intervals are interleaved with invalid time intervals. It utilizes animation to visualize valid time intervals and invalid time intervals, which can be integrated in a webpage as a browser extension. We have tested it against a series of known keyloggers and also performed a study with 95 users to evaluate how easily the tool is used. Experimental results made by volunteers show that OSPAK is a simple approach.

Keywords: authentication, computer security, keylogger, privacy, information leakage

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Authors: Francisca Onaolapo Oladipo, Peter Afam Ugwu

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Records of user activities which are valuable for forensic investigation purposes are provided by web browsers -these records in most cases are not in visual formats that are easily understood, thereby requiring some extra processes. This paper describes the implementation of a software tool for client-side web history visualization providing suitable forensic evidence for investigative purposes. Visual C#, Perl and gnuplot were deployed on Windows Operating System (OS) environment to implement the system and the resulting tool parses and transforms a web browser history into a visual format that enables an investigator to quickly and efficiently explore, understand, and interpret the user online activities in the context of a specific investigation. The system was tested using two forensic cases: the client-side web history files generated by Mozilla Firefox browser was extracted using MozillaHistoryView utility, then parsed and visualized using bar and stacked column charts. From the visual representation, results of user web activities across various productive and non-productive websites were obtained.

Keywords: history, forensics, visualization, web activities

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: Mohammad Hemmati, Mohammad Taban, Ali Yasini

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One of the factors that can play an important role in increasing productivity is the optimal use of information technology, which in this area today has a significant role to play in computer systems of office automation in organizations and companies. Therefore, this research has been conducted with the aim of investigating the effect of the relationship between computerized systems of office automation and the productivity of employees in the municipality of Ilam city. The statistical population of this study was 110 people. Using Cochran formula, the minimum sample size is 78 people. The present research is a descriptive-looking research in terms of the type of objective view. A questionnaire was used to collect data. To assess the reliability of variables, Cornbrash’s alpha coefficient was used, which was equal to 0.85; SPSS19 and Pearson test were used to analyze the data and test the hypothesis of the research. In this research, three hypotheses of the relationship between office automation with efficiency, performance, and effectiveness were investigated. The results showed a direct and positive relationship between the office automation system and the increase in the efficiency, effectiveness, and efficiency of employees, and there was no reason to reject these hypotheses.

Keywords: efficiency, performance, effectiveness, automation

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: S. P. Sharma, Som Nath Saha

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This paper deals with the analytical investigation of thermal and thermohydraulic performance of double flow solar air heaters with corrugated and flat plate absorber. A mathematical model of double flow solar air heater has been presented, and a computer program in C⁺⁺ language is developed to estimate the outlet temperature of air for the evaluation of thermal and thermohydraulic efficiency by solving the governing equations numerically using relevant correlations for heat transfer coefficients. The results obtained from the mathematical model is compared with the available experimental results and it is found to be reasonably good. The results show that the double flow solar air heaters have higher efficiency than conventional solar air heater, although the double flow corrugated absorber is superior to that of flat plate double flow solar air heater. It is also observed that the thermal efficiency increases with increase in mass flow rate; however, thermohydraulic efficiency increases with increase in mass flow rate up to a certain limit, attains the maximum value, then thereafter decreases sharply.

Keywords: corrugated absorber, double flow, solar air heater, thermos-hydraulic efficiency

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Shweta Pinjarkar, Shrutika Yawale, Mayuri Patil, Reshma Adagale

Abstract:

Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this, demonstrates the face recognition system in android device using eigenface. The system can be used as the base for the development of the recognition of human identity. Test images and training images are taken directly with the camera in android device.The test results showed that the system produces high accuracy. The goal is to implement model for particular face and distinguish it with large number of stored faces. face recognition system detects the faces in picture taken by web camera or digital camera and these images then checked with training images dataset based on descriptive features. Further this algorithm can be extended to recognize the facial expressions of a person.recognition could be carried out under widely varying conditions like frontal view,scaled frontal view subjects with spectacles. The algorithm models the real time varying lightning conditions. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application. Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this , demonstrates the face recognition system in android device using eigenface. The system can be used as the base for the development of the recognition of human identity. Test images and training images are taken directly with the camera in android device.The test results showed that the system produces high accuracy. The goal is to implement model for particular face and distinguish it with large number of stored faces. face recognition system detects the faces in picture taken by web camera or digital camera and these images then checked with training images dataset based on descriptive features. Further this algorithm can be extended to recognize the facial expressions of a person.recognition could be carried out under widely varying conditions like frontal view,scaled frontal view subjects with spectacles. The algorithm models the real time varying lightning conditions. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application.

Keywords: face detection, face recognition, eigen faces, algorithm

Procedia PDF Downloads 361

Authors: Edwin Forero-Garcia, Jaime Vitola, Brayan Cardenas, Johan Casagua

Abstract:

This research article presents the integration of electronic and computer systems, which developed an ultrasonic signal processing system that performs the capture, adaptation, and analog-digital conversion to later carry out its processing and visualization. The capture and adaptation of the signal were carried out from the design and implementation of an analog electronic system distributed in stages: 1. Coupling of impedances; 2. Analog filter; 3. Signal amplifier. After the signal conditioning was carried out, the ultrasonic information was digitized using a digital microcontroller to carry out its respective processing. The digital processing of the signals was carried out in MATLAB software for the elaboration of A-Scan, B and D-Scan types of ultrasonic images. Then, advanced processing was performed using the SAFT technique to improve the resolution of the Scan-B-type images. Thus, the information from the ultrasonic images was displayed in a user interface developed in .Net with Visual Studio. For the validation of the system, ultrasonic signals were acquired, and in this way, the non-invasive inspection of the structures was carried out and thus able to identify the existing pathologies in them.

Keywords: acquisition, signal processing, ultrasound, SAFT, HMI

Procedia PDF Downloads 107

Authors: Lai Junzhe, Wang Lihao, Burra Venkata Durga Kumar

Abstract:

In recent years, due to today's global technological advances, big data and artificial intelligence technologies have been widely used in various industries and fields, playing an important role in reducing costs and increasing efficiency. Among them, artificial intelligence has derived another branch in its own continuous progress and the continuous development of computer personnel, namely distributed artificial intelligence computing systems. Distributed AI is a method for solving complex learning, decision-making, and planning problems, characterized by the ability to take advantage of large-scale computation and the spatial distribution of resources, and accordingly, it can handle problems with large data sets. Nowadays, distributed AI is widely used in military, medical, and human daily life and brings great convenience and efficient operation to life. In this paper, we will discuss three areas of distributed AI computing systems in vision processing, blockchain, and smart home to introduce the performance of distributed systems and the role of AI in distributed systems.

Keywords: distributed system, artificial intelligence, blockchain, IoT, visual information processing, smart home

Procedia PDF Downloads 113

Authors: Diane Vassallo., Leonard Busuttil

Abstract:

Recent educational developments have seen increasing attention attributed to Computational Thinking (CT) and its integration into primary and secondary school curricula. CT is more than simply being able to use technology but encompasses fundamental Computer Science concepts which are deemed to be very important in developing the correct mindset for our future digital citizens. The case study presented in this article explores the journey of a Maltese secondary school teacher in his efforts to plan, develop and integrate CT within the context of a local classroom. The teacher participant was recruited from the Malta EU Code week summer school, a pilot initiative that stemmed from the EU Code week Team’s Train the Trainer program. The qualitative methodology involved interviews with the participant teacher as well as an analysis of the artefacts created by the students during the lessons. The results shed light on the numerous challenges and obstacles that the teacher encountered in his integration of CT, as well as portray some brilliant examples of good practices which can substantially inform further research and practice around the integration of CT in classroom practice.

Keywords: computational thinking, digital citizens, digital literacy, technology integration

Procedia PDF Downloads 154