International Journal of Electrical and Computer Engineering (IJECE)
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Application of satisfiability problem solvers for assessing the strength of hash algorithms
Get PDFThis article presents a methodology for assessing the strength of cryptographic algorithms and provides experimental data obtained from studying the cryptographic strength of the developed hash function HBC-256 using modern satisfiability problem (SAT) solvers. Various SAT solvers implementing the conflict-driven clause learning (CDCL) algorithm, based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, were used to conduct the cryptanalysis of the HBC-256 hash function. The most effective was the parallel SAT solver Parkissat, and thus it was used for more in-depth research. A series of experiments were conducted to determine how resistant the HBC-256 hashing algorithm is to preimage attacks for one, two, three, and four rounds. For this purpose, four sets of files were prepared using special propositional encoding tools, each set including 30 files in the standard of center for discrete mathematics and theoretical computer sciences (DIMACS) format. These files contain Boolean formulas in conjunctive normal form (CNF), used as input for modern SAT solvers. To obtain more accurate time measurements, the same experiment was repeated multiple times, after which the average time was determined. The results of this study show that SAT solvers encounter significant difficulties when attempting to solve the preimage search problem for the full-round version of the HBC-256 hash function, even when only 30 bits of the original message are unknown
Notice of Retraction Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks
No full textNotice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijece@iaesjournal.com.-----------------------------------------------------------------------Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks
Enhancing linear quadratic regulator and proportional-integral linear quadratic regulator controllers for photovoltaic systems
Get PDFThis article introduces the linear quadratic regulator (LQR) control and the hybrid linear quadratic regulator proportional-integral (LQR-PI) control, both applied to a photovoltaic system coupled with a DC-DC boost converter. The converter outputs direct current electrical energy to power direct loads. Two robust control correctors, based on the LQR and LQR-PI methods, are designed to enhance the static and dynamic performance of the PV DC-DC boost system. These controllers aim to minimize oscillations and overshoots while ensuring stability across varying solar conditions, thereby optimizing operation around the maximum power point. The disturb and observe maximum power point tracking (MPPT) technique, integrated with the LQR and LQR-PI controllers, ensures system functionality under disturbances. The novelty of this work lies in the development of a MATLAB control block diagram capable of regulating the reference voltage provided by the perturb and observe (P&O) MPPT algorithm. MATLAB simulations demonstrate the robustness and high performance of the LQR and LQR-PI controllers, validating the efficacy of this boost converter control strategy
Name privacy on named data networking: a survey and future research
Get PDFInformation-centric networking (ICN) has gained significant interest in recent years, attracting both academic and industry, it represents a paradigm shift and moving away from the host-based IP networks that dominate today landscape. As ICN technology matures and advances towards real-world deployment, the importance of addressing security and privacy concerns has grown exponentially. The ICN paradigm is deliberately designed to encompass numerous security and privacy features, including but not limited to provenance and privacy. These features, which are often lacking in the host-centric paradigm, inherently form a core aspect of ICN. Nevertheless, due to its relatively recent emergence, the ICN paradigm also presents a range of unresolved privacy challenges. This paper offers a comprehensive survey of the existing literature on privacy primarily focuses on major domains name privacy. We delve into the fundamental principles of existing research and evaluate the limitations of proposed methodologies. In name privacy, we also explore strategies to preserve name privacy. We have identified future research directions and highlighted ongoing challenges in the pursuit of enhancing ICN privacy
Structure of quaternion-type algebras and a post-quantum signature algorithm
Get PDFAlgebraic digital signature algorithms with a commutative hidden group, which are based on the computational difficulty of solving large systems of power equa- tions, are promising candidates for post-quantum cryptoschemes, especially in securing applications like the internet of things (IoT) and other information tech- nologies. Associative finite non-commutative algebras are used as an algebraic support of the said algorithms. Among such algebras, finite quaternion-type al- gebras have been identified as strong candidates for providing algebraic support. This paper investigates the decomposition of these algebras into commutative subrings and explores their multiplicative groups, which can serve as poten- tial hidden groups. The analysis reveals the existence of three distinct types of subrings, with derived formulas for the number of subrings and the orders of their multiplicative groups. These findings align with previous studies on four- dimensional algebras defined by sparse basis vector multiplication tables. Using the finite quaternion-type algebras as algebraic support, a novel post-quantum signature algorithm characterized in using two mutually non-commutative hid- den groups has been developed
Enhancing cybersecurity awareness strategies in organization using Delphi technique
Get PDFCybersecurity concerns were once primarily perceived as technical issues, prompting many organizations to prioritize investments in security technologies. However, it has become increasingly evident that cybersecurity is not solely a technical matter. In fact, a significant number of cybersecurity breaches arise from users' lack of awareness about secure technological practices. This research aims to develop a cybersecurity awareness strategy using the Delphi technique over three rounds, involving 15 cybersecurity experts. The findings indicate a consensus among experts that cybersecurity awareness training is an effective strategy to enhance an organization's overall cybersecurity posture. However, the true essence of cybersecurity lies in fostering secure technology usage practices among all users within the organization. To address this, the researcher developed systematic training content for cybersecurity awareness, which was evaluated and refined by experts using the Delphi technique to ensure its effectiveness in promoting genuine cybersecurity awareness
Augmented reality learning media for electrical motor: case study in electrical engineering education
Get PDFThe impact of augmented reality (AR) learning tools and students' critical thinking abilities on learning outcomes in electrical engineering education is the focus of this study. The study explores the ways in which these factors, both independently and in combination, influence student performance. Findings reveal that AR-based learning materials significantly enhance understanding and retention across self-directed and guided learning models. Critical thinking skills emerge as a key determinant of success, with students exhibiting strong critical thinking consistently outperforming peers with lower-level skills, regardless of the instructional model. The study also highlights variations in AR tools' effectiveness depending on the learning model and students’ critical thinking abilities. Guided learning with AR tools benefits all students, while self-directed AR tools prove most effective for those with advanced critical thinking skills. Students with lower critical thinking abilities face challenges in navigating less structured AR environments. These results underscore the importance of fostering critical thinking and adopting tailored strategies when integrating AR technology into engineering education. By considering both the learning model and critical thinking levels, educators can optimize AR’s potential to enhance student learning outcomes in technical fields
EvalBERT: a novel framework for assisted descriptive answers and C programming evaluation
Get PDFManual assessment of descriptive answers is often time-consuming, error-prone, and subject to bias. While artificial intelligence (AI) has made significant strides, current automated evaluation methods typically rely on simplistic metrics like word counts or predefined terms, which lack a deeper understanding of the content and are highly dependent on curated datasets. As demand for automated grading systems increases, there is a growing need to evaluate not only descriptive answers but also code-based responses. This study addresses these challenges by applying natural language processing (NLP) and deep learning (DL) techniques, testing three baseline models: multinomial Naïve bayes (MNB), bidirectional long short-term memory (Bi-LSTM), and bidirectional encoder representations from transformers (BERT). We propose EvalBERT, a BERT-based model fine-tuned with domain-specific academic corpora using computer processing unit (CPU) acceleration. EvalBERT automates grading for both descriptive and C programming exams, offering features like readability statistics and error detection. Experimental results show that EvalBERT achieves 94.86% accuracy, outperforming other models by 1.22 percentage points, with training time reduced by half. Additionally, EvalBERT is the first model pre-trained with academic corpora for this purpose. An interactive user interface, E-Pariksha, was also developed for administering and taking exams online. EvalBERT provides precise assessments, enabling educators to better evaluate student performance and offer more detailed feedback
Greenhouse gas reduction system for engines using electrolyte technology
Get PDFThis research focuses on developing a system to reduce greenhouse gas emissions in internal combustion vehicle engines using electrolyte technology and embedded programming on an electronic board via the OBI protocol. The main objectives are to create a prototype, apply it in real-world scenarios, evaluate its efficiency, and facilitate technology transfer. The system, designed to reduce greenhouse gases from vehicles, consists of a Bluetooth on-board diagnostics (OBD) scanner connected to the electronic control unit (ECU). This scanner transmits data to an embedded microcontroller through a Bluetooth module. The microcontroller, which includes software for controlling oxygen measurement and production, operates to decrease greenhouse gas emissions. The results show that the electronic device, IC ELM327, decodes OBD into RS232, processes the oxygen output from the exhaust pipe using embedded programming on the Arduino Uno-R3 microprocessor, and controls the oxygen production unit with electrolyte technology. The system adds 9.82% oxygen to the exhaust and reduces carbon monoxide by 21.04% and carbon dioxide by 13.86%. Additionally, the technology transfer received high satisfaction with a mean score of 4.61, indicating efficient technology dissemination
Devanagari optical character recognition of printed text
Get PDFHundreds of native languages and scripts are making their way on digital platform to sustain in multiple data formats. Optical character recognition (OCR) is one such dimension where the low resource languages are yet to find their stability. Devanagari OCR is one such low resource script problem to be dealt with, though it is the fourth widely used global script. Recent works carried on OCR have focused on word level approach and face challenges of spiraling complexity as language alphabet set size crosses hundreds. Most of these OCR works are done in constrained environment, with huge datasets and large computational resources. As a result, effective benchmark evaluation of the works against one another on defined metrics is scarce. Aim here is to explore character level Devanagari OCR with printed text images as input. Pattern recognition (PR) principles for diacritic classification and convolutional neural network (CNN) for base character classification are used. word error rate (WER) of 24.47% is attained. However, the training dataset complexity is reduced by 4.35 times. The ten multi class models, training time range from 45 minutes to 2.5 hours. Further the models can be trained in parallel to complete the training process in 3-4 hours. Thus, the approach used for text classification facilitates the Devanagari OCR solution to be offered in off-the-shelf computing devices