Search results for: computer vision on embedded systems

Authors: F. Pottmeyer, M. Weispfenning, K. A. Weidenmann

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Continuous carbon fiber reinforced plastics (CFRP) exhibit a high application potential for lightweight structures due to their outstanding specific mechanical properties. Embedded metal elements, so-called inserts, can be used to join structural CFRP parts. Drilling of the components to be joined can be avoided using inserts. In consequence, no bearing stress is anticipated. This is a distinctive benefit of embedded inserts, since continuous CFRP have low shear and bearing strength. This paper aims at the investigation of the load bearing capacity after preinduced damages from impact tests and thermal-cycling. In addition, characterization of mechanical properties during dynamic high speed pull-out testing under different loading velocities was conducted. It has been shown that the load bearing capacity increases up to 100% for very high velocities (15 m/s) in comparison with quasi-static loading conditions (1.5 mm/min). Residual strength measurements identified the influence of thermal loading and preinduced mechanical damage. For both, the residual strength was evaluated afterwards by quasi-static pull-out tests. Taking into account the DIN EN 6038 a high decrease of force occurs at impact energy of 16 J with significant damage of the laminate. Lower impact energies of 6 J, 9 J, and 12 J do not decrease the measured residual strength, although the laminate is visibly damaged - distinguished by cracks on the rear side. To evaluate the influence of thermal loading, the specimens were placed in a climate chamber and were exposed to various numbers of temperature cycles. One cycle took 1.5 hours from -40 °C to +80 °C. It could be shown that already 10 temperature cycles decrease the load bearing capacity up to 20%. Further reduction of the residual strength with increasing number of thermal cycles was not observed. Thus, it implies that the maximum damage of the composite is already induced after 10 temperature cycles.

Keywords: composite, joining, inserts, dynamic loading, thermal loading, residual strength, impact

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Authors: Tsutomu Iwayama, Takayuki Hama

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Si ion implantation was widely used to synthesize specimens of SiO2 containing supersaturated Si and subsequent high temperature annealing induces the formation of embedded luminescent Si nanocrystals. In this work, the potentialities of excimer UV-light (172 nm, 7.2 eV) irradiation and rapid thermal annealing (RTA) to enhance the photoluminescence and to achieve low temperature formation of Si nanocrystals have been investigated. The Si ions were introduced at acceleration energy of 180 keV to fluence of 7.5 x 1016 ions/cm2. The implanted samples were subsequently irradiated with an excimer-UV lamp. After the process, the samples were rapidly thermal annealed before furnace annealing (FA). Photoluminescence spectra were measured at various stages at the process. We found that the luminescence intensity is strongly enhanced with excimer-UV irradiation and RTA. Moreover, effective visible photoluminescence is found to be observed even after FA at 900 oC, only for specimens treated with excimer-UV lamp and RTA. We also prepared specimens of Si nanocrystals embedded in a SiO2 by reactive pulsed laser deposition (PLD) in an oxygen atmosphere. We will make clear the similarities and differences with the way of preparation.

Keywords: Ion implantation, photoluminescence, pulsed laser deposition, rapid thermal anneal, Si nanocrystals

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Authors: Rita Garcia

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Intelligent Programming Tutors, IPTs, are supplemental educational devices that assist in teaching software development. These systems provide customized learning allowing the user to select the presentation pace, pedagogical strategy, and to recall previous and additional teaching materials reinforcing learning objectives. In addition, IPTs automatically records individual’s progress, providing feedback to the instructor and student. These tutoring systems have an advantage over Tutoring Systems because Intelligent Programming Tutors are not limited to one teaching strategy and can adjust when it detects the user struggling with a concept. The Intelligent Programming Tutor is a category of Intelligent Tutoring Systems, ITS. ITS are available for many fields in education, supporting different learning objectives and integrate into other learning tools, improving the student's learning experience. This study provides a comparison of the IPTs currently adopted by the educational community and will focus on the different teaching methodologies and programming languages. The study also includes the ability to integrate the IPT into other educational technologies, such as massive open online courses, MOOCs. The intention of this evaluation is to determine one system that would best serve in a larger ongoing research project and provide findings for other institutions looking to adopt an Intelligent Programming Tutor.

Keywords: computer education tools, integrated software development assistance, intelligent programming tutors, tutoring systems

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Authors: Trapond Hiransalee

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The technology of mobile devices has changed our daily lives. Since smartphone have become a multi-functional device, many people spend unnecessary time on them, and could be interrupted by inappropriate notifications such as unimportant messages from social media. Notifications from smartphone could draw people’s attention and distract them from their priorities and current tasks. This research investigated that if the users were notified by their surroundings instead of smartphone, would it create less distraction and keep their focus on the present task. The experiment was a simulation of a lamp and door notification. Notifications related to work will be embedded in the lamp such as an email from a colleague. A notification that is useful when going outside such as weather information, traffic information, and schedule reminder will be embedded in the door. The experiment was conducted by sending notifications to the participant while he or she was working on a primary task and the working performance was measured. The results show that the lamp notification had fewer interruption effects than the smartphone. For the door notification, it was simulated in order to gain opinions and insights on ambient notifications from participants. Many participants agreed that the ambient notifications are useful and being informed by them could lessen the usage of their smartphone. The results and insights from this research could be used to guide the design process of ambient notifications.

Keywords: HCI, interaction, interaction design, usability testing

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Authors: Cassandra Branham, Danielle Farrar

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As writing instructors and writing center administrators at a large research university with a significant population of English language learners (ELLs), we are interested in how peer feedback pedagogy can be effectively translated for writing center purposes, as well as how various modes of peer feedback can enrich the learning experiences of L1 and L2 writers in these spaces. Although peer feedback is widely used in classrooms and centers, instructor, student, and researcher opinions vary in respect to its effectiveness. We argue that peer feedback - traditional and digital, synchronous and asynchronous - is an indispensable element for both classrooms and centers and emphasize that it should occur with both L1 and L2 students to further develop an array of reading and writing skills. We also believe that further understanding of the best practices of peer feedback in such cross-cultural spaces, like the classroom and center, can optimize the benefits of peer feedback. After a critical review of the literature, we implemented an embedded tutoring program in our university’s writing center in collaboration with its First-Year Composition (FYC) program and Language Institute. The embedded tutoring program matches a graduate writing consultant with L1 and L2 writers enrolled in controlled-matriculation composition courses where ELLs make up at least 50% of each class. Furthermore, this program is informed by what we argue to be some best practices of peer feedback for both classroom and center purposes, including expectation-based training through rubrics, modeling effective feedback, hybridizing traditional and digital modes of feedback, recognizing the significance the body in composition (what we call writer embodiment), and maximizing digital technologies to exploit extended cognition. After conducting surveys and follow-up interviews with students, instructors, and writing consultants in the embedded tutoring program, we found that not only did students see an increased value in peer feedback, but also instructors saw an improvement in both writing style and critical thinking skills. Our L2 participants noted improvements in language acquisition while our L1 students recognized a broadening of their worldviews. We believe that both L1 and L2 students developed self-efficacy and agency in their identities as writers because they gained confidence in their abilities to offer feedback, as well as in the legitimacy of feedback they received from peers. We also argue that these best practices situate novice writers as experts, as writers become a valued and integral part of the revision process with their own and their peers’ papers. Finally, the use of iPads in embedded tutoring recovered the importance of the body and its senses in writing; the highly sensory feedback from these multi-modal sessions that offer audio and visual input underscores the significant role both the body and mind play in compositional practices. After beginning with a brief review of the literature that sparked this research, this paper will discuss the embedded tutoring program in detail, report on the results of the pilot program, and will conclude with a discussion of the pedagogical implications that arise from this research for both classroom and center.

Keywords: English language learners, peer feedback, writing center, writing classroom

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Bernardo Cedano, Ahmed H. Eltom, Bob Hay, Jim Glass, Raga Ahmed

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The deployment of smart grid devices such as smart meters and smart switches (SS) supported by a reliable and fast communications system makes automated distribution possible, and thus, provides great benefits to electric power consumers and providers alike. However, more research is needed before the full utility of smart switch data is realized. This paper presents new automated switching techniques using SS within the electric power grid. A concise background of the SS is provided, and operational examples are shown. Organization and presentation of data obtained from SS are shown in the context of the future goal of total automation of the distribution network. The description of application techniques, the examples of success with SS, and the vision outlined in this paper serve to motivate future research pertinent to disturbance analysis automation.

Keywords: disturbance automation, electric power grid, smart grid, smart switches

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Authors: Ramesh Kumar Behera, Md. Izhar Hassan

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Accident investigation and analysis are useful to identify deficiencies in plant, process, and management practices and formulate preventive strategies for injury elimination. In India and other parts of the world, industrial accidents are investigated to know the causes and also to fulfill legal compliances. However, findings of investigation are seldom used appropriately to strengthen Occupational Safety and Health (OSH) in expected lines. The mineral rich state of Odisha in eastern coast of India; known as a hub for Iron and Steel industries, witnessed frequent accidents during 2005-2009. This article based on study of 982 fatal ‘factory-accidents’ occurred in Odisha during the period 2001-2016, discusses the ‘turnaround-story’ resulting in reduction of fatal accident from 122 in 2009 to 45 in 2016. This paper examines various factors causing incidents; accident pattern in steel and chemical sector; role of climate and harsh weather conditions on accident causation. Software such as R, SQL, MS-Excel and Tableau were used for analysis of data. It is found that maximum fatality is caused due to ‘fall from height’ (24%); steel industries are relatively more accident prone; harsh weather conditions of summer increase chances of accident by 20%. Further, the study suggests that enforcement of partial work-restriction around lunch time during peak summer, screening and training of employees reduce accidents due to fall from height. The study indicates that learning from accident investigation and analysis can be used as a method to reduce work related accidents in the journey towards ‘Vision Zero’.

Keywords: accident investigation and analysis, fatal accidents in India, fall from height, vision zero

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

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Authors: R. Karmouch

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A thin gold metal layer was deposited on the top of silicon oxide films containing embedded Si nanocrystals (Si-nc). The sample was annealed in gas containing nitrogen, and subsequently characterized by photoluminescence. We obtained 3-fold enhancement of photon emission from the Si-nc embedded in silicon dioxide covered with a Gold layer as compared with an uncovered sample. We attribute this enhancement to the increase of the spontaneous emission rate caused by the coupling of the Si-nc emitters with the surface plasmons (SP). The evolution of PL emission with laser irradiated time was also collected from covered samples, and compared to that from uncovered samples. In an uncovered sample, the PL intensity decreases with time, approximately with two decay constants. Although the decrease of the initial PL intensity associated with the increase of sample temperature under CW pumping is still observed in samples covered with a gold layer, this film significantly contributes to reduce the permanent deterioration of the PL intensity. The resistance to degradation of light-emitting silicon nanocrystals can be increased by SP coupling to suppress the permanent deterioration. Controlling the permanent photodeterioration can allow to perform a reliable optical gain measurement.

Keywords: photodeterioration, silicon nanocrystals, ion implantation, photoluminescence, surface plasmons

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Authors: Ebuka Valentine Iroha, Tsunemi Watanabe, Satoshi Tsuchiya

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Project management practices play an important role in construction project performance and are one of project managers' essential key performance indicators. Previous studies have explored the poor performance of the construction industry, with project delays and cost overruns identified to contribute largely to numerous abandoned projects. These challenges are attributed to insufficient project management practices and a lack of utilization of project managers. The actual causes of inadequate project management practices and underutilization of project managers have been rarely discussed. This study tends to bridge the gap by identifying and assessing the actual causes of insufficient project management practices and underutilization of project managers. This study differs from past studies investigating the causes of poor performance by using institutional analysis methods to identify and analyze the factors influencing project management practices and proper utilization of project managers. Based on a comprehensive literature review, this study identified some factors embedded in the construction industry that influence the institutional environment and weaken the laws and regulations. These factors were used as the basis for semi-structured interview questions to investigate their impacts on project management practices and project managers. The data collected were coded into a four-level framework for institutional analysis. This method was used to analyze the interrelationships between the identified embedded factors, institutional laws and regulations, and construction organizations to understand how these influences result in the underutilization of project managers. The study found that the underutilization of project managers consists of two subsystems, including underutilization and lowering commitment. The first subsystem, corruption, political influence, religious and tribal discrimination, and organizational culture, were found to affect the institutional structure. These embedded factors weaken the industry’s governance mechanism, forcing project managers to prioritize corrupt practices over project demands. The ineffectiveness of the existing laws and regulations worsens the situation, supporting unfair working conditions and contributing to the underperformance of project managers. This situation leads to the development of the second subsystem, which is characterized by a lack of opportunities for career development and minimal incentives within construction organizations. The findings provide significant potential for addressing systemic challenges in the construction industry, particularly the underutilization of project managers and enhancing organizational support measures to improve project management practices and mitigate the adverse effects of corruption.

Keywords: construction industry, project management, poor performance, embedded factors, project managers underutilization

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Authors: Shiming Li, Qingwang Liu, Honggan Wu, Jianbing Zhang

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Plantation forests are useful for timber production, recreation, environmental protection and social development. Stands height is an important parameter for the estimation of forest volume and carbon stocks. Although lidar is suitable technology for the vertical parameters extraction of forests, but high costs make it not suitable for operational inventory. With the development of computer vision and photogrammetry, aerial photos from unmanned aerial system can be used as an alternative solution for height mapping. Structure-from-motion (SfM) photogrammetry technique can be used to extract DSM and DEM information. Canopy height model (CHM) can be achieved by subtraction DEM from DSM. Our result shows that overlapping aerial photos is a potential solution for plantation forests height mapping.

Keywords: forest height mapping, plantation forests, structure-from-motion photogrammetry, UAS

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Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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Authors: Leena Korpinen, Rauno Pääkkönen, Fabriziomaria Gobba

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This paper is based on a large questionnaire study. The paper presents how all participants and subgroups (upper- and lower-level white-collar workers) answered the question, “Have you had an ache, pain, or numbness, which you associate with desktop computer use, in the different body parts during the last 12 months?’ 14.6% of participants (19.4% of women and 8.2% of men) reported that they had often or very often physical symptoms in the neck. Even if our results cannot prove a causal relation of symptoms with computer use, show that workers believe that computer use can influence their wellbeing: This is important when devising treatment modalities to decrease these physical symptoms.

Keywords: ergonomics, work, computer, symptoms

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Fisayo G. Ojo, Shamala K. Subramaniam, Zuriati Ahmad Zukarnain

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As technology is advancing and wireless applications are becoming dependable sources, while the physical layer of the applications are been embedded into tiny layer, so the more the problem on energy efficiency and consumption. This paper reviews works done in recent years in wireless applications and distributed computing, we discovered that applications are becoming dependable, and resource allocation sharing with other applications in distributed computing. Applications embedded in distributed system are suffering from power stability and efficiency. In the reviews, we also prove that discrete event simulation has been left behind untouched and not been adapted into distributed system as a simulation technique in scheduling of each event that took place in the development of distributed computing applications. We shed more lights on some researcher proposed techniques and results in our reviews to prove the unsatisfactory results, and to show that more work still have to be done on issues of energy efficiency in wireless applications, and congestion in distributed computing.

Keywords: discrete event simulation (DES), distributed computing, energy efficiency (EE), internet of things (IOT), quality of service (QOS), user equipment (UE), wireless mesh network (WMN), wireless sensor network (wsn), worldwide interoperability for microwave access x (WiMAX)

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Authors: Serzhan Ashirov, Dana Nour, Rafat Rob, Khaled Alotaibi

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There has been a fast growth in the introduction and use of communications, information, monitoring, and sensing technologies. The new technologies are making their way to the Industrial Control Systems as embedded in products, software applications, IT services, or commissioned to enable integration and automation of increasingly global supply chains. As a result, the lines that separated the physical, digital, and cyber world have diminished due to the vast implementation of the new, disruptive digital technologies. The variety and increased use of these technologies introduce many cybersecurity risks affecting cyber-resilience of the supply chain, both in terms of the product or service delivered to a customer and members of the supply chain operation. US department of energy considers supply chain in the IR4 space to be the weakest link in cybersecurity. The IR4 identified the digitization of the field devices, followed by digitalization that eventually moved through the digital transformation space with little care for the new introduced cybersecurity risks. This paper will examine the best methodologies for securing the electrical substations from cybersecurity attacks due to supply chain risks, and due to digitization effort. SCADA systems are the most vulnerable part of the power system infrastructure due to digitization and due to the weakness and vulnerabilities in the supply chain security. The paper will discuss in details how create a secure supply chain methodology, secure substations, and mitigate the risks due to digitization

Keywords: cybersecurity, supply chain methodology, secure substation, digitization

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Authors: Elke Mackensen

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Systems Engineering plays a key role during industrial product development of complex technical systems. The need for systems engineers in industry is growing. However, there is a gap between the industrial need and the academic education. Normally the academic education is focused on the domain specific design, implementation and testing of technical systems. Necessary systems engineering expertise like knowledge about requirements analysis, product cost estimation, management or social skills are poorly taught. Thus, there is the need of new academic concepts for teaching systems engineering skills. This paper presents a project-orientated training concept to prepare students from different technical degree programs for systems engineering activities. The training concept has been initially implemented and applied in the industrial engineering master program of the University of Applied Sciences Offenburg.

Keywords: educational systems engineering training, requirements analysis, system modelling, SysML

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Authors: Mark Andrew

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Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation.

Keywords: forecasting, technology futures, uncertainty, complexity

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Authors: Iliya Musa Adamu

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This paper seeks to develop an expert system that would troubleshoot computer network and evaluate the network system performance so as to reduce the workload on technicians and increase the efficiency and effectiveness of solutions proffered to computer network problems. The platform of the system was developed using ASP.NET, whereas the codes are implemented in Visual Basic and integrated with SQL Server 2005. The knowledge base was represented using production rule, whereas the searching method that was used in developing the network troubleshooting expert system is the forward-chaining-rule-based-system. This software tool offers the advantage of providing an immediate solution to most computer network problems encountered by computer users.

Keywords: expert system, forward chaining rule based system, network, troubleshooting

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Authors: Aleksei Samarin, Artyom Nazarenko, Valentin Malykh

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Image enhancement is becoming more and more popular, especially on mobile devices. Nowadays, it is a common approach to enhance an image using a convolutional neural network (CNN). Such a network should be of significant size; otherwise, a possibility for the artifacts to occur is overgrowing. The existing large CNNs are computationally expensive, which could be crucial for mobile devices. Another important flaw of such models is they are poorly interpretable. There is another approach to image enhancement, namely, the usage of predefined filters in combination with the prediction of their applicability. We present an approach following this paradigm, which outperforms both existing CNN-based and filter-based approaches in the image enhancement task. It is easily adaptable for mobile devices since it has only 47 thousand parameters. It shows the best SSIM 0.919 on RANDOM250 (MIT Adobe FiveK) among small models and is thrice faster than previous models.

Keywords: universal filter, image enhancement, neural networks, computer vision

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Authors: Mojtaba Hakimi-Moghaddam

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Positive realness as the most important property of driving point impedance of passive electrical networks appears in the control systems stability theory in 1960’s. There are three important subsets of positive real (PR) systems are introduced by researchers, that is, loos-less positive real (LLPR) systems, weakly strictly positive real (WSPR) systems and strictly positive real (SPR) systems. In this paper, definitions, properties, lemmas, and theorems related to family of positive real systems are summarized. Properties in both frequency domain and state space representation of system are explained. Also, several illustrative examples are presented.

Keywords: real rational matrix transfer functions, positive realness property, strictly positive realness property, Hermitian form asymptotic property, pole-zero properties

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Authors: Evgeny Kozubenko, Dmitry Shaposhnikov, Mikhail Petrushan

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This work is focused on the study of the binocular characteristics of the phenomenon of perisaccadic suppression in humans when perceiving visual objects. This phenomenon manifests in a decrease in the subject's ability to perceive visual information during saccades, which play an important role in purpose-driven behavior and visual perception. It was shown that the impairment of perception of visual information in the post-saccadic time window is stronger (p < 0.05) in the ipsilateral eye (the eye towards which the saccade occurs). In addition, the observed heterogeneity of post-saccadic suppression in the contralateral and ipsilateral eyes may relate to depth perception. Taking the studied phenomenon into account is important when developing ergonomic control panels in modern operator systems.

Keywords: eye movement, natural vision, saccadic suppression, visual perception

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Authors: Maropeng Maponya, Mawethu Nhlabathi

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Euphemistic expressions, as part of showing respect and ubuntu, are naturally embedded in the African Languages. These expressions are solely used to soothe the impact which dysphemistic words may have on an individual or the society at large. Conversely, the script producers of one of the well-known soap operas in South Africa, The Queen–Mzansi, seem to have turned a blind eye on that, mostly when they use dysphemistic reference to human genitals. As a result, such practice tends to deteriorate the ethicality of the African languages and the beliefs held by African society in general. They also give less meaning to the promotion of African language concepts. This paper is aimed at explaining and analyzing the impact of dysphemism on language growth, basing the argument on the fact that subtitled texts in the soap opera never reflect the actual dysphemistic sourced text uttered by the character/s. This is a clear indication that the production crew of this soap opera is aware of the impact that these utterances may have on society, yet they do not mind the characters saying them as is in African Languages whilst euphemizing them through English subtitles. The paper adopted a descriptive qualitative method with an embedded case study in it, whereby dysphemistic clips from three characters of the soap opera were selected and analyzed.

Keywords: euphemism, dysphemism, soap opera, The Queen

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Authors: Peter Tabatt, Christian Siemers

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Using FPGA devices to improve the behavior of time-critical parts of embedded systems is a proven concept for years. With reconfigurable FPGA devices, the logical blocks can be partitioned and grouped into static and dynamic parts. The dynamic parts can be reloaded 'on demand' at runtime. This work uses cellular automata, which are constructed through compilation from (partially restricted) ANSI-C sources, to determine the suitability of various metrics for optimal partitioning. Significant metrics, in this case, are for example the area on the FPGA device for the partition, the pass count for loop constructs and communication characteristics to other partitions. With successful partitioning, it is possible to use smaller FPGA devices for the same requirements as with not reconfigurable FPGA devices or – vice versa – to use the same FPGAs for larger programs.

Keywords: reconfigurable FPGA, cellular automata, partitioning, metrics, parallel computing

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Boudjedra Abderrahim, Toumi Salah, Boutalbi Mostefa, Frihi Mohammed

Abstract:

Nowadays, using the advancement of technology in semiconductor device fabrication, many transistors can be integrated to a single chip (VLSI). Although the growth chip density potentially eases systems-on-chip (SoCs) integrating thousands of processing element (PE) such as memory, processor, interfaces cores, system complexity, high-performance interconnect and scalable on-chip communication architecture become most challenges for many digital and embedded system designers. Networks-on-chip (NoCs) becomes a new paradigm that makes possible integrating heterogeneous devices and allows many communication constraints and performances. In this paper, we are interested for good performance and low area for implementation and a behavioral modeling of network on chip mesh topology design using VHDL hardware description language with performance evaluation and FPGA implementation results.

Keywords: design, implementation, communication system, network on chip, VHDL

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Authors: Belkacem Benadda, Adil Benabdellah, Boutheyna Souna

Abstract:

The Internet of Things (IoT) is an unprecedented creation. Electronics objects are now able to interact, share, respond and adapt to their environment on a much larger basis. Actual spread of these modern means of connectivity and solutions with high data volume exchange are affecting our ways of life. Accommodation is becoming an intelligent living space, not only suited to the people circumstances and desires, but also to systems constraints to make daily life simpler, cheaper, increase possibilities and achieve a higher level of services and luxury. In this paper we are as Internet access, teleworking, consumption monitoring, information search, etc.). This paper addresses the design and integration of a smart home, it also purposes an IoT solution that allows smart power consumption based on measurements from power-grid and deep learning analysis.

Keywords: array sensors, IoT, power grid, FPGA, embedded

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Authors: Min Chen, Hui Lin

Abstract:

VGEs are a kind of typical web- and computer-based geographic environment, with aims of merging geographic knowledge, computer technology, virtual reality technology, network technology, and geographic information technology, to provide a digital mirror of physical geographic environments to allow users to ‘feel it in person’ by a means for augmenting the senses and to ‘know it beyond reality’ through geographic phenomena simulation and collaborative geographic experiments. Many achievements have appeared in this field, but further evolution should be explored. With the exploration of the conception of VGEs, and some examples, this article illustrated the role of VGEs and their contribution to currently GIScience. Based on the above analysis, questions are proposed for discussing about the future way of VGEs.

Keywords: virtual geographic environments (VGEs), GIScience, virtual reality, geographic information systems

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Authors: Serhat Tüzün, Tufan Demirel

Abstract:

Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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