Computer Engineering Research Articles

Optimized design of hardware accelerator based on machine learning

Hardware accelerators are playing an increasingly important role in the field of computer science and engineering, especially in accelerating deep learning and large-scale data processing. However, the traditional hardware accelerator design often relies on manual adjustment and experience accumulation, which is inefficient. Therefore, the use of machine learning technology to optimize hardware accelerator design has become a new trend. In this paper, a hardware accelerator optimization design method based on machine learning is proposed, including hardware design, algorithm optimization, system-level optimization, and deep learning model compression. Through the research and verification of the actual case, this research proves the effectiveness and feasibility of the hardware accelerator optimization design method based on machine learning. Compared with the traditional design method, this method can find better design scheme in shorter time and has higher performance prediction accuracy. Therefore, this method can significantly improve the design efficiency and performance of hardware accelerators. The results of this study are of great significance for promoting the development of hardware accelerator technology. It can promote scientific research and engineering applications in related fields to make greater progress, and promote the development of artificial intelligence, big data processing and other fields.

High Throughput Characterization of Organic Thin Film Transistors.

Automation is vital to accelerating research. In recent years,the application of self-driving labs to materials discovery and device optimization has highlighted many benefits and challenges inherent to these new technologies. Successful automated workflows offer tangible benefits to fundamental science and industrial scale-up by significantly increasing productivity and reproducibility all while enabling entirely new types of experiments. However, it's implemtation is often time-consuming and cost-prohibitive and necessitates establishing multidisciplinary teams that bring together domain-specific knowledge with specific skillsets in computer science and engineering. This perspective article provides a comprehensive overview of how the research group has adopted "hybrid automation" over the last 8 years by using simple automatic electrical testers (autotesters) as a tool to increase productivity and enhance reproducibility in organic thin film transistor (OTFT) research. From wearable and stretchable electronics to next-generation sensors and displays, OTFTs have the potential to be a key technology that will enable new applications from health to aerospace. The combination of materials chemistry, device manufacturing, thin film characterization and electrical engineering makes OTFT research challenging due to the large parameter space created by both diverse material roles and device architectures. Consequently, this research stands to benefit enormously from automation. By leveraging the multidisciplinary team and taking a user-centered design approach in the design and continued improvement of the autotesters, the group has meaningfully increased productivity, explored research avenues impossible with traditional workflows, and developed as scientists and engineers capable of effectively designing and leveraging automation to build the future of their fields to encourage this approach, the files for replicating the infrastructure are included, and questions and potential collaborations are welcomed.

An Analysis Study on the appropriateness of evaluation criteria of the University Admission Officer System by using Topic Modeling to the Student's School Cumulative Record: Focusing on the Computer Engineering Applicants

An Analysis Study on the appropriateness of evaluation criteria of the University Admission Officer System by using Topic Modeling to the Student's School Cumulative Record: Focusing on the Computer Engineering Applicants

IMPLEMENTATION OF THE TEACHING FACTORY MODEL TO ENHANCE COMPETENCE IN COMPUTER AND NETWORK ENGINEERING EXPERTISE AT SMK ACEH SELATAN

The teaching factory learning model has become an important strategy in enhancing students' expertise in Vocational High Schools (SMK) in South Aceh. This study aims to evaluate the effectiveness of this learning model in improving students' skills, particularly in the concentration of computer and network engineering expertise. The research shows that products, whether goods or services, in the teaching factory learning model play a crucial role as the main medium in delivering expertise to students. Block schedules are used to optimize resources efficiently, while Job Sheets regulate the sequence of materials to achieve the desired competencies. Supporting factors in the implementation of the teaching factory include support from school management, active student participation, and trust from the community as consumers of the products. However, some obstacles have also been identified, such as lack of socialization, suboptimal teaching staff, and limited facilities and infrastructure. Practical learning processes are conducted according to real work procedures, with a focus on student-centered learning, self-learning, and learning by doing principles. This allows students to learn through direct practice, not just theory. Thus, the teaching factory learning model at SMK in South Aceh contributes to enhancing students' expertise in the concentration of computer and network engineering.

The underlying potential of NLP for microcontroller programming education

AbstractThe trend for an increasingly ubiquitous and cyber‐physical world has been leveraging the use and importance of microcontrollers (C) to unprecedented levels. Therefore, microcontroller programming (CP) becomes a paramount skill for electrical and computer engineering students. However, CP poses significant challenges for undergraduate students, given the need to master low‐level programming languages and several algorithmic strategies that are not usual in “generic” programming. Moreover, CP can be time‐consuming and complex even when using high‐level languages. This article samples the current state of CP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of CP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8‐bit C and project‐based learning. While NLP tools emerge as strong candidates as students' C companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for CP education, building on both hands‐on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real‐world tasks that the C is designed to handle, rather than on low‐level coding. We also analyzed the specific impact of chatGTP in the context of a CP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in CP courses.

Could the utilization of Empathy Maps and Personas enhance the requirement elicitation process

There is a growing interest in the use of Design Thinking (DT) to enrich requirements elicitation processes. We carried out a quasi-experiment to compare the Effectiveness of Brainstorming sessions in terms of the number of ideas of requirements generated, and the Identified Stakeholders, complementing the Brainstorming sessions with two of the most widely used DT techniques: Empathy Maps and Personas. Therefore, we consider three treatments: Personas + Brainstorming, Empathy Maps + Brainstorming, and Brainstorming alone (control group). The quasi-experiment was carried out with 74 students enrolled in the Bachelor of Computer Engineering course at the Universitat Politècnica de València in 2021. The results reveal a statistically significant effect on Identified Stakeholders when using Empathy Maps. Descriptive analysis also shows an increase in Identified Stakeholders when using Personas, and in the Effectiveness of Brainstorming sessions when used together with Empathy Maps or Personas. There is also a variation in the type of ideas, with the number of functional ideas being higher, and that of non-functional ideas being lower when Empathy Maps are used. These results seem to indicate that Brainstorming sessions are improved when complemented with Empathy Maps or Personas techniques. Nonetheless, we still lack enough evidence to recommend either Personas or Empathy Maps, necessitating further validation.

Digital twin technology training and research in health higher education: a review

Healthcare strives to ensure overall physical, mental, and emotional well-being for individuals while managing limited resources efficiently. Digital technologies can offer cost reduction, improved user experience, and expanded capacity. In addition, modern automation technologies, which were implemented in industrial control systems or industrial automation control systems, are essential for ensuring the availability of societies’ critical cyber-physical systems (CPSs) and the services they provide, such as healthcare. This narrative literature review produces information that can be applied when planning and implementing an interdisciplinary biomedical and health informatics (BMHI) master’s education focused on the challenges of digitalization in the health sector. The review results that virtual human twins (VHTs) are revolutionizing healthcare by addressing people’s complex medical problems with real-time monitoring and precision care while digital twin (DT) technology can make the hospital’s operational processes resilient and efficient. Thus, future BMHI education must address these technologies with a multidisciplinary approach, including computer science, information science, engineering, basic sciences, health sciences, socio-behavioral sciences, and ethical, legal, and policy aspects. Collected and cumulative data is essential for cognitive DTs. A prerequisite for this data is information sharing between different CPSs. Better information sharing and the development of scalable cognitive DTs and VHTs, the provision of critical services, quality, and cost-effectiveness, as well as health, safety, and resilience, will improve. Similarities between peoples’ health information exchange and information needed for ensuring the resilience of CPSs exist. Since humans are in many ways more complex than CPSs, security engineers have a lot to learn from VHTs in maintaining the resilience of CPSs. Cross-sectoral research and cooperation with different disciplines are essential for the progress of both human health and the resilience of CPSs. Along with interdisciplinary research cooperation, educational cooperation should also be intensified.

Unleashing ChatGPT's impact in higher education: Student and faculty perspectives

As Chat Generative Pre-trained Transformer (ChatGPT) gains traction, its impact on post-secondary education is increasingly being debated. This qualitative study explores the perception of students and faculty members at a research university in Canada regarding ChatGPT's use in a post-secondary setting, focusing on how it could be incorporated and what ways instructors can respond to this technology. We present the summary of a discussion that took place in a 2-hour focus group session with 40 participants from the computer science and engineering departments, and highlight issues surrounding plagiarism, assessment methods, and the appropriate use of ChatGPT. Findings suggest that students are likely to use ChatGPT, but there is a need for specific guidelines, more classroom assessments, and mandatory reporting of ChatGPT use. The study contributes to the emergent research on ChatGPT in higher education and emphasizes the importance of proactively addressing challenges and opportunities associated with ChatGPT adoption and use. The novelty of the study involves capturing the perspectives of students and faculty members. This paper aims to provide a more refined understanding of the complex interplay between AI chatbots and higher education that will help educators navigate the rapidly evolving landscape of AI-driven education.

INNOVATIVE HOSE GAMMA FLAW DETECTOR “RID 50W”

An innovative model of a portable class hose gamma flaw detector “RID 50W” is presented, equipped with environmentally friendly tungsten components: a biological protection unit, a shutter assembly and a multi-link source holder for working with emitters based on 75Se and 192Ir. The design features of the radiation head model, the principles of release and shutdown of the radiation beam in the “RID 50W” apparatus, issues of optimization, synthesis, and computer engineering of critical components are considered, and a quantitative assessment of the technical parameters and characteristics of the equipment is presented. The exposure dose rate of the diffusion flux of penetrating gamma radiation from a working emitter based on 192Ir was calculated in the Geant4 software package, followed by practical verification using standard dosimetric monitoring tools. Conclusions based on the results of verification of the obtained data are presented. The design and operating principle of a patented laser sight designed to expand the functionality of a hose gamma flaw detector during operation, including in critical conditions of structural constraint, is also considered.

Visualización del estado de la educación de la Ingeniería en Computación en el ámbito nacional y en la Universidad Autónoma Metropolitana

The objective of this research is to design an integral methodological framework for the design of digital learning objects at the Autonomous Metropolitan University, Campus Azcapotzalco (uam-a). In a first stage, complex networks were used as information visualization tools to understand the context of teaching in Computer Engineering. The analy-sis was carried out for the national level and for the four computer engineering degrees taught at uam.Among the results, some characteristics of the teaching of Computer Engineering at the national level were esta-blished, highlighting the centralization of the offer and enrollment in Mexico City and its metropolitan area. Coinciden-ces and differences between the approaches of the four computer engineering degrees offered by uam and the profiles requested by employers were also found. It is expected that this information will benefit the implementation of virtual education programs at uam

Conversational and generative artificial intelligence and human–chatbot interaction in education and research

AbstractArtificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

Pelatihan Jaringan Komputer di SMKN 5 Surabaya

Computer network training at SMKN 5 Surabaya is an activity that aims to provide students with knowledge and skills in the field of computer networks. This training is usually given to students majoring in Computer and Network Engineering (TKJ). The material taught in computer network training at SMKN 5 Surabaya includes: Introduction to computer networks, network topology, network addressing, implementation of the training using a blended learning approach, which combines theoretical and practical learning. This method has proven effective in improving students' conceptual understanding and technical skills. (Garrison and Vaughan, 2008) state that this approach is able to answer challenges in vocational education by combining the advantages of face-to-face and online learning. In addition, collaboration with industry through internship programs provides students with valuable practical experience, which strengthens their readiness to enter the world of work. (Anderson and Solis 2018) reveal that industry involvement can increase the relevance and quality of vocational education. However, this research also identifies several challenges that need to be overcome to ensure sustainability and improve the quality of training. One of the main challenges is limited resources, especially in terms of equipment and software which are always evolving. Continued investment and professional training for teachers is key to overcoming this challenge. (Smith and Ragan 2005) emphasize the importance of professional development for teachers in maintaining educational quality. With continued commitment from all parties, computer network training at SMKN 5 Surabaya is expected to continue to provide significant benefits for students and industry.

Extrusion-Based Bioprinting in a Cost-Effective Bioprinter

Three-dimensional (3D) bioprinting has emerged as a revolutionary approach in the life sciences, combining multiple disciplines such as computer engineering, materials science, robotics, and biomedical engineering. This innovative technology enables the production of cellular constructs using bio-inks, and differs from conventional 3D printing by incorporating living cells. The present work addresses the conversion of a commercial thermoplastic 3D printer into a low-cost bioprinter. The modification addresses the challenges of the high cost of commercial bioprinters, limited adaptability, and specialized personnel requirements. This modification uses an extrusion-based bioprinting method that is particularly popular in research due to its viscosity tolerance and versatility. The individual steps, including replacing the extruder with a syringe pump, rebuilding the electronic motherboard, and configuring the firmware, are explained in detail. The work aims at providing access to bioprinting technology so that laboratories with modest resources can take advantage of the immense potential of this technology. This modification resulted in improved resolution, allowing submicron movements, which is comparable to some of the commercially available bioprinters. The accuracy of the modified printer was validated using hydrogel bioprinting tests, suggesting that it is suitable for broader applications in regenerative medicine.

Symposium 2: An education model for customized and flexible networked learning courses for working engineers

The area of computer and electrical engineering is under constant evolution which leads to lifelong learning being an important aspect for being a long term successful working professional. This work presents an education model for creating customized and flexible courses at advanced level for these working professionals. We have approached this work from the educator's point of view and the focus will be on the teaching model and our results from implementing the model during the last two years. Including how we created these need and trend-based education offerings, the course execution inspired with micro-learning and flipped-classroom pedagogics, and our work with creating win-win possibilities within the courses for the working professionals and the companies they work for. Finally, we will present our experiences and lessons learnt, ending with a plan for our upcoming courses and our refined model in our ongoing future work.

Computer and Information Science and Engineering—Core Programs (NSF)

Computer and Information Science and Engineering—Core Programs (NSF)

Digital skills in the use of artificial intelligence tools for the formulation of formative research projects from the TECSIS Research Seminar.

Artificial intelligence (AI) has promoted a change in the way research and innovation (R&D) is performed bringing new perspectives, automating time and routine tasks contributing to the generation of knowledge. To take full advantage of the potential of AI, it is proposed to develop strategic digital skills for the use of artificial intelligence tools in the formulation of formative research projects from the TECSIS Research Seminar. The methodology corresponds to a qualitative research with a descriptive analytical approach with a cross-sectional approach in four stages: analysis, design, evaluation and dissemination. The population under study includes the students of the special programs of Computer Engineering and Systems Technology of the University of Caldas. The expected result is the characterization of strategic digital skills of use for the formulation of formative research projects. This project will contribute in the development of a methodological route that exposes in a practical and applied way the strategic digital skills of use of AI tools for the formulation of formative research projects for the special programs of the University.

Commands of Synthetic Biology to Modernize and Re-design the Biological Systems

The scope of synthetic biology continues to expand and has encompassed a huge number of biological features. Its scope starts from scratch, enabling the de novo synthesis of biological systems. It has re-designed the biological systems and empowered the production of synthetic genes, RNA, DNA and proteins by undertaking the control of pathways involved in genetic regulation. It has increased the production of nano-scale RNA architectures and synthetic biological circuits which either have therapeutic or other productive uses. Furthermore, advancements in synthetic biology have enabled the generation of diversity through methods such as epPCR and site-directed mutagenesis, allowing for the creation of complex genetic variations. Additionally, synthetic biology intersects with computer engineering to design functional biological devices and circuits, utilizing computational analysis to guide the design process. Moreover, ethical and regulatory considerations are paramount in synthetic biology, with careful examination required to address dual-use concerns, environmental impacts, and issues of social justice and equitable access to benefits. As synthetic biology continues to advance, it presents opportunities to address pressing challenges in fields ranging from medicine and agriculture to environmental conservation and beyond. Thus, the fusion of synthetic biology with other scientific disciplines holds promise for transformative innovation and societal benefit. The present discussion enlightened the core of generating complex biological systems and has given a brief overview on the fusion of synthetic biology with other fields of science.Keywords: Biological Systems; Genetic Regulation; Synthetic biology circuits

Analisis Klasifikasi Konsentrasi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor

Hamzanwadi University located in West Nusa Tenggara boasts a Faculty of Engineering, which plays a pivotal role in delivering top-notch higher education. This faculty offers four highly coveted programs—Informatics Engineering, Information Systems, Computer Engineering, and Environmental Engineering—that attract a significant number of students. The increase in enrollment in these programs underscores the faculty's success, particularly owing to the promising job opportunities in these fields. Nevertheless, students often encounter intricate challenges when selecting their area of specialization that resonates with their interests and capabilities. In addressing this concern, the Faculty of Engineering at Hamzanwadi University provides diverse concentration options like Data Science, RPL, and Multimedia. To aid students in making informed decisions regarding their study concentration, this study employs the K-Nearest Neighbor (KNN) algorithm to analyze the classification of student concentrations. This research adopts an experimental approach and utilizes data collection methods such as observation, interviews, and surveys. The dataset comprises seven attributes including NIM, Gender, GPA, Data Science Course Grade, RPL Course Grade, and Multimedia Course Grade, processed using the KNN algorithm through Google Colab. The research outcomes reveal that with k=2 and 8-fold cross-validation, the achieved accuracy stands at 67%.

Effectiveness of cooperative, collaborative, and interdisciplinary learning guided by software development in Spanish universities

The European Higher Education Area (EHEA) proposes to enhance active learning and student protagonism in order to improve academic performance. In this sense, different methodologies are emerging to create scenarios for self-regulation of their learning. In this study the cooperative, collaborative and interdisciplinary learning methodologies were compared in Spanish universities. The main objectives were to evaluate their effects in higher education and to explore the relationship between perceived group cooperation and self-perceived ability to work in a group, differences between educational Spanish contexts, educational methodologies and gender. To this end, a quasi-experimental design was carried out. Data analysis included the descriptive metrics, correlations and analysis of variance to evaluate the differences among pedagogical methods, their effects on cooperative learning, teamwork outcomes and gender differences, comprising a total of 229 students in Spain from Psychology, Early Childhood Education, Primary Education and Computer Engineering completed the two questionnaires. Results showed that the highest correlation between perceived cooperative activity and self-perceived ability to work in a group was found among computer science students, especially among women, suggesting that the interdisciplinary learning focused on software tool development may be the most effective methodology to improve teamwork and cooperative learning outcomes. Despite these findings concern only to Spanish universities, limiting the generalizability of results, the interdisciplinary methodology seems promising for improving both teaching quality and teamwork skills. The learning methodologies of interdisciplinary projects may therefore need to be implemented within the framework of cooperative and collaborative methodologies.

Methods and algorithms of optimization in computer engineering: review and comparative analysis

Introduction: The main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence. Methods: One of its most relevant areas has been highlighted - the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter” - the biological media of the human body - is considered. Results: Through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions - the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function - the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal.Conclusions: A comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented