Matrix inversion using multiple-input multiple-output adaptive filtering

A new approach for matrix inversion is introduced. The approach is based on vector representation of multiple-input multiple-output (MIMO) channel matrix, in which the channel matrix is described by a linear combination of channel vectors weighted by their respective system inputs. The MIMO system output is then fed into a bank of adaptive filters, wherein the response of a given adaptive filter is iteratively minimized to match its output to the given system input. In doing so, adaptive filters equalize the impact of respective channel vectors on the MIMO channel output, while simultaneously orthogonalizing themselves from all other channel vectors, forming the channel matrix inverse. The method demonstrates satisfactory convergence and accuracy in Monte Carlo simulations conducted with varying signal-to-noise ratios (SNRs) and matrix conditioning scenarios. The suggested approach, by virtue of its adaptable characteristics, can also be employed for time-varying linear equation systems

Smart brake pad early warning system: enhancing vehicle safety through real-time monitoring

A contributing factor to traffic accidents is brake pad failure, which diminishes braking system performance and extends braking distance. This work develops a prototype utilizing internet of things (IoT) to measure brake pad thickness, hence enhancing driver awareness through real-time monitoring. The system establishes the thickness detection threshold at 75% (3-4 mm) and 50% (5–6 mm) as a cautionary parameter. The thickness parameter employs an American wire gauge (AWG) 18 cable to connect to the ESP32 microcontroller. The web-based IoT monitoring interface employs Laravel. This method enables drivers to get prompt notifications regarding the decrease in brake pad thickness, hence permitting urgent preventative maintenance to mitigate the risk of accidents. The system underwent testing through friction at a rotational speed of 600 to 6,000 rpm. The test findings indicated that the sensor precisely measured the brake pad thickness with a prototype response time of a second. This system is suitable for implementation on old model vehicles that do not have an early warning system. The installation of this technology is anticipated to enhance driver knowledge of the state of the brake pads, hence potentially diminishing the danger of brake system failure caused by unmonitored pad wear

Classification and similarity detection of Indonesian scientific journal articles

The development of technology is accelerating in finding references to scientific articles or journals related to research topics. One of the sources of national aggregator services to find references is Garba Rujukan Digital (GARUDA), developed by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) of the Republic of Indonesia. The naïve Bayes method classifies articles into several categories based on titles and abstracts. The system achieves an F1-score of 98%, which indicates high classification accuracy, and the classification process takes less than 60 minutes. Article similarity detection is done using the cosine similarity method, and a similarity score of 0.071 reflects the degree of similarity between the title and the abstract that has been concatenated, while a score close to 1 indicates a higher similarity. Searching for similar scientific articles based on title and abstract, sort articles based on the results of the highest similarity score are the most similar articles, and generating article categories. The results of the research show that the proposed method significantly improves the classification and search processes in GARUDA, as well as accurate and efficient similarity detection

Field programmable gate array simulation and study on different multiplexer hardware for electronics and communication

Multiplexing is the technique of transmitting two or more separate signals concurrently using a single communication channel. Multiplexing enables the augmentation of communication channels and consequently the volume of data that may be transmitted. Communication networks utilize diverse multiplexing techniques. An input multiplexer amalgamates various network signals into a singular composite signal before transmission over a shared medium. The composite signal is broken back into its component signals by a demultiplexer, when it reaches its destination, allowing further operations to utilize them separately. The design of the hardware chip depends on the configuration of the multiplexer and demultiplexer in the communication system. The work is presented as a study of the digital logic design and simulation of the different configurations of the multiplexer hardware. The performance evaluation is carried out on the different series of Xilinx field programmable gate array (FPGA) such as Spartan-6, Spartan-3E, Virtex-5, and Virtex-6 with logically checked in Xilinx ISE waveform simulator software. The current analysis of the design and simulation of different configurations of the multiplexer design helps the designers to estimate the chip performance. The novelty of the work lies in its scalable and programmable architecture fitted for specific communication systems that assess performance based on latency, frequency, and power consumption that can be further linked with communication protocols

Geoinformation system for monitoring forest fires and data encryption for low-orbit vehicles

This article discusses two important aspects of unmanned aerial vehicles (UAVs): forest fire monitoring and data security for low-orbit vehicles. The first part of the article is devoted to the development of a geographic information system (GIS) for monitoring and forecasting the spread of forest fires. The system uses intelligent processing of aerospace data obtained from UAVs to timely detect fires, determine their characteristics and forecast the dynamics of development. The second part of the article focuses on the problem of high-speed encryption of data transmitted from low-orbit aircraft. An effective encryption algorithm is proposed that ensures high data processing speed and reliable protection of information from unauthorized access. The article presents the results of modeling and analysis of the effectiveness of the proposed solutions

Detection of android malware with deep learning method using convolutional neural network model

Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware

Machine learning model approach in cyber attack threat detection in security operation center

The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%)

Power of analytic tools in Oxygen Forensic® Detective based on NIST cybersecurity framework

The National Institute of Standards and Technology (NIST) cybersecurity framework is a systematic approach for assessing and improving cybersecurity procedures in digital investigations. Oxygen Forensic® Detective is a digital forensic software that integrates multiple analytic tools to assist investigators in extracting valuable insights from digital evidence. The analytic tools, including timeline, social graph, image categorization, facial categorization, maps, data search, key evidence, optical character recognition, statistics, and translation, assist investigators in thoroughly analyzing digital artifacts, establishing connections, and accurately classifying images with precision and effectiveness. By incorporating these analytical resources into Oxygen Forensic® Detective, a comprehensive strategy is established to effectively combat cyber threats. The NIST cybersecurity framework is incorporated into the tool, offering a methodical approach to identifying and reducing cybersecurity risks. Law enforcement agencies can enhance the productivity and effectiveness of their forensic methodologies by implementing these advanced technologies. This can result in successful prosecutions and improved cybersecurity practices.  Overall, the utilization of analytical tools in criminological inquiries has experienced a substantial rise in the contemporary digital era

Retraction Notice: Quay crane assignment in container terminals using a genetic algorithm

Ports are essential for international trade, connecting production areas to consumer markets. However, port operations often face delays, disrupting supply chains and increasing transit times. To address this, mathematical modeling, particularly through genetic algorithms, offers a solution for optimizing processes like container unloading. This paper presents a model predicting and optimizing unloading times by considering factors such as crane types, schedules, and environmental conditions. Focusing on the Casablanca port, the model addresses scheduling for two gantry and two mobile cranes, treating each bay as a unique task handled by one crane type to avoid conflicts. Using genetic algorithms, the goal is to create efficient schedules that minimize waiting times and maximize crane utilization. The expected outcome is a detailed timetable enabling effective gantry crane use or simultaneous multi-crane operations, enhancing unloading efficiency. This approach can be adapted to other ports with similar challenges, highlighting the model's broader applicability

Arowana cultivation water quality forecasting with multivariate fuzzy timeseries and internet of things

Water quality plays a crucial role in the growth and survival of arowana fish, with imbalances in key parameters (pH, temperature, turbidity, dissolved oxygen, and conductivity) leading to increased mortality rates. While previous studies have introduced various monitoring models using Arduino IDE and intrinsic approaches, they lack predictive capabilities, leaving cultivators unable to take proactive measures. To address this gap, this study develops a predictive model integrating the internet of things (IoT) with a fuzzy time series (FTS) algorithm. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate of 0.01704%, outperforming decision tree (0.13410%), FTS-multivariate T1 (0.88397%), and linear regression (20.91791%). These findings confirm that FTS-multivariate T2 not only accurately predicts water quality but also significantly reduces the mean absolute percentage error, providing a robust solution for sustainable arowana aquaculture