Artificial intelligence (AI) can revolutionize the development industry, primarily electrical and electronics engineering. By automating recurring duties, AI can grow productivity and efficiency in creating. For instance, AI can research constructing designs, discover capability troubles, and generate answers, reducing the effort and time required for manual analysis. AI also can be used to optimize electricity consumption in buildings, which is a critical difficulty in the construction enterprise. Via machines gaining knowledge of algorithms to investigate electricity usage patterns, AI can discover areas wherein power may be stored and offer guidelines for enhancements. This can result in significant value financial savings and reduced carbon emissions. Moreover, AI may be used to improve the protection of creation websites. By studying statistics from sensors and cameras, AI can locate capacity dangers and alert workers to take suitable action. This could help save you from injuries and accidents on production sites, lowering the chance for workers and enhancing overall safety in the enterprise. The impact of AI on electric and electronics engineering productivity inside the cre
A Project Oriented Problem Based Learning (POPBL) has been introduced to the first year students in the Analog Electronics (BEL10203) course at the Faculty of Electrical and Electronic Engineering, UTHM. The aim is to design an electronic circuit using transistors and diodes that can function as electronic appliances with low cost, low power consumption, and has the features of smart and portable. The total of 143 students were divided into groups and assigned to setup an electronic based company that will be manufacturing the electronic product. Each group had to conduct their regular meetings and develop different kind of products with their creativity. The overall evaluation is divided for both lecturer and peer assessment which carried 20% of their course work. The assessment covered 60% of evaluation for the group management, attitude, progress presentation, report writing while another 40% for the functionality and features of their product. As a result, the POPBL session has increased the student’s ability to analyze and design an analog circuit using various kinds of transistors and diodes. They also gained practical understanding on transistor and diode operation. The POPBL not only expanded their experience in using software tools for circuit design and simulation, but also developed greater awareness to conduct professional presentation and technical report. They also learned to work as professional, keen to ethical responsibilities and committed to the group. The analysis conducted has shown that 95% of the students agreed that the problem given helped them understands better the course syllabus and developed a good problem solving skills.
Many electrical and electronic engineering program has been developed to educate, train and groom future electrical and electronic engineers. Students that are not able to understand, visualize and relate real application with theoretical concept will find it difficult to cope with the program. Thus the intention of this paper is to demonstrate the approach of Engineering is fun with Concept- Design-Implement-Operate (CDIO) concept embedded in Final Year Project (FYP) Electrical Engineering Diploma Program at the Faculty of Electrical Engineering Universiti Teknologi MARA (UiTM) Pasir Gudang Campus. FYP are being conducted for two semesters. The first semester is where students will search and choose their own topic. Furthermore, students have to present and propose the topics they have chosen in the same semester and lecturers will access their communication skills. The prototype will be built during the second semester and it will test the student's psychomotor skills. This paper will show an example of one student FYP product prototype called The Evolution of Mastermind Boardgame pertaining to the embedded CDIO concepts. It is hoped from this work, student's understanding can be strengthen in order to develop students that fully equipped with strong background of electrical and electronic engineering concept.
Usage of mathematics in different flow of electrical and electronic engineering is outstanding by everybody. In this paper, a good number of examples of applications of mathematics in electrical and electronic engineering have designed. The motivation behind this paper is to relate mathematics to electrical and electronic engineering subject. Numerous electrical and electronic engineering students think that it’s hard to tackle electrical and electronic engineering problems which require mathematics a considerable measure. It is not possible to investigation of current, voltage, electric LR and RC circuit, electromagnetic fields, designing and analyzing circuits without utilization of scientific instruments of trigonometry, Calculus, Geometry and Differential Equation.
Outstanding academic achievement in the field of higher education is a source of pride for the university. The success of the university is measured not only by academic performance but also by the quality of graduates produced. In Malaysia, three major categories in higher learning are identified: public, private, and foreign-branch universities. All engineering programs follow the requirements set by the Engineering Accreditation Council (EAC) on behalf of the Board of Engineers Malaysia (BEM). The programme educational objectives (PEOs) make up one of the elements that needs assessment for ensuring its continuity in line with the university’s mission and vision. A PEO comparative study on selected reputable electrical and electronic (EE)-engineering department universities was carried out based on the mapping of PEO attribute keywords. These attributes were then classified into either cluster, sharing, or uniqueness groups. The study compared the relevancy of each PEO statement suggested by stakeholders and other interested parties. The results from the PEO comparative study suggested that attributes on competency, ethics, professionalism, and leadership are given high priorities. However, the increase in demand for entrepreneurship-, multidisciplinary-, and soft skills should also be considered when reviewing the institution’s engineering curriculum. The uniqueness of such attributes will distinguish the EE-engineering graduates’ profession, marketability, and employability. PEO statements reflect the credibility and sustainability of a well-balanced graduate equipped with the right knowledge, skills, and values.
<i> Journal of Electrical and Electronic Engineering (JEEE)</i>, a peer-reviewed open access journal published bimonthly in English-language, aims to foster a wider academic interest in electrical and electronics engineering, including its intersection with physics. The journal publishes original research papers, with emphasis on theoretical and experimental work. Contributions that are fundamental to the development of electrical and electronic engineering and its applications are accepted. Generally, review articles on some topic of special current interest will be published.
The term mechatronics was ‘invented’ by a Japanese engineer in 1969, as a combination of ‘mecha’ from mechanisms and ‘tronics’ from electronics. The word now has a wider meaning, being used to describe a philosophy in engineering technology in which there is a co-ordinated, and concurrently developed, integration of mechanical engineering with electronics and intelligent computer control in the design and manufacture of products and processes. As a result, mechatronic products have many mechanical functions replaced with electronic ones. This results in much greater flexibility, easy redesign and reprogramming, and the ability to carry out automated data collection and reporting
modern society needs a completely new model of the education system, because in order to train highly qualified specialists it is very important to teach them to correctly interact with various sources of information, analyze and effectively use them subsequently. The development of a fundamentally new model, which will be based on the use of information and communication technologies, will significantly improve the qualifications of personnel and bring modern enterprises to a new level. Using the example of the subject electrical and electronic engineering, the article provides guidelines for designing classes using interactive methods.
In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.
This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.
Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies
Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.
Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques th
The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-
Context: The use of standards is considered a vital part of any engineering discipline. So one could expect that standards play an important role in Requirements Engineering (RE) as well. However, little is known about the actual knowledge and use of RE-related standards in industry. Objective: In this article, we investigate to which extent standards and related artifacts such as templates or guidelines are known and used by RE practitioners. Method: To this end, we have conducted a questionnaire-based online survey. We could analyze the replies from 90 RE practitioners using a combination of closed and open-text questions. Results: Our results indicate that the knowledge and use of standards and related artifacts in RE is less widespread than one might expect from an engineering perspective. For example, about 47% of the respondents working as requirements engineers or business analysts do not know the core standard in RE, ISO/IEC/IEEE 29148. Participants in our study mostly use standards by personal decision rather than being imposed by their respective company, customer, or regulator. Beyond insufficient knowledge, we also found cultural and organizational factors impeding the
Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and pr
For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze
Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.
Model-Based Systems Engineering aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and workpackage based on the model. Once such a model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema needs to be designed, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modelling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems. The schema and modelling approach are designed to answer questions such as what happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? Or if a condition cannot be met, which alternative routes can be established to reach a certain state of the system. We build on the use case of qualification and operations of a small
By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.