Based on experimental measurements conducted on many different practical wireless communication systems, ambient noise has been shown to be decidedly non-Gaussian owing to impulsive phenomena. However, most multiuser detection techniques proposed thus far have considered Gaussian noise only. They may therefore suffer from a considerable performance loss in the presence of impulsive ambient noise. In this paper, we consider a large-scale multiuser multiple-input multiple-output system in the presence of non-Gaussian noise and propose a genetic algorithm (GA) based detector for large-dimensional multiuser signal detection. The proposed algorithm is more robust than linear multi-user detectors for non-Gaussian noise because it uses a multi-directional search to manipulate and maintain a population of potential solutions. Meanwhile, the proposed GA-based algorithm has a comparable complexity because it does not require any complicated computations (e.g., a matrix inverse or derivation). The simulation results show that the GA offers a performance gain over the linear minimum mean square error algorithm for both non-Gaussian and Gaussian noise.

To compute the mean and variance of component-based reliability software, we focused on path-based reliability analysis. System reliability depends on the transition probabilities of components within a system and reliability of the individual components as basic input parameters. The uncertainty in these parameters is estimated from the test data of the corresponding components and arises from the software architecture, failure behaviors, software growth models etc. Typically, researchers perform Monte Carlo simulations to study uncertainty. Thus, we considered a Markov chain Monte Carlo (MCMC) simulation to calculate uncertainty, as it generates random samples through sequential methods. The MCMC approach determines the input parameters from the probability distribution, and then calculates the average approximate expectations for a reliability estimation. The comparison of different techniques for uncertainty analysis helps in selecting the most suitable technique based on data requirements and reliability measures related to the number of components.

This paper proposes a method to find a tree with the maximum number of terminals that can be connected by a given length when numerous terminals distributed in a cluster form are given to the Euclidean plane R2 with several constraints. First constraint is that a given terminal is distributed in a cluster form, second is that a given length cannot connect all terminals in the tree, and third is that there is no curved connection between each terminal. This paper proposes a method to establish more efficient interconnections within terminals distributed in a cluster form by improving a randomly distributed memetic genetic algorithm. The construction of interconnections has been extensively used in design-related fields, from networking to architecture. Additionally, in real life, the construction of interconnections is mostly distributed in the form of clusters. Therefore, the heuristic algorithm proposed in this paper can be effectively utilized in real life and is expected to provide various cost savings.

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

The goal of pattern mining is to identify novel patterns in a database. High utility itemset mining (HUIM) is a research direction for pattern mining. This is different from frequent itemset mining (FIM), which additionally considers the quantity and profit of the commodity. Several algorithms have been used to mine high utility itemsets (HUIs). The original BPSO algorithm lacks local search capabilities in the subsequent stage, resulting in insufficient HUIs to be mined. Compared to the transfer function used in the original PSO algorithm, the V-shaped transfer function more sufficiently reflects the probability between the velocity and position change of the particles. Considering the influence of the acceleration factor on the particle motion mode and trajectory, a nonlinear acceleration strategy was used to enhance the search ability of the particles. Experiments show that the number of mined HUIs is 73% higher than that of the original BPSO algorithm, which indicates better performance of the proposed algorithm.

News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.

In modern society, as many data are used for research or commercial purposes, the value of data is gradually increasing. In related fields, research is being actively conducted to collect valuable data, but it is difficult to collect proper data because the value of collection is determined according to the performance of existing sensors. To solve this problem, a method to effectively reduce noise has been proposed, but there is a point in which performance is degraded due to damage caused by noise. In this paper, a device capable of collecting time series data was designed to correct such data noise, and a correction technique was performed by giving an error value based on the representatively collected ultrafine dust data, and then comparing before and after Compare performance. For the correction method, Kalman, LPF, Savitzky-Golay, and Moving Average filter were used. Savitzky-Golay filter and Moving Average Filter showed excellent correction rate as an experiment. Through this, the performance of the sensor can be supplemented and it is expected that data can be effectively collected.

The recommendation system applied in museums has been widely adopted owing to its advanced technology. However, it is unclear which recommendation is suitable for indoor museum guidance. This study evaluated a recommender system based on social-filtering and statistical methods applied to actual museum databases. We evaluated both methods using two different datasets. Statistical methods use collective data, whereas social methods use individual data. The results showed that both methods could provide significantly better results than random methods. However, we found that the trip time length and the dataset's sizes affect the performance of both methods. The social-filtering method provides better performance for long trip periods and includes more complex calculations, whereas the statistical method provides better performance for short trip periods. The critical points are defined to indicate the trip time for which the performances of both methods are equal.

Monolithic three-dimensional (M3D) logics such as M3D-NAND, M3D-NOR, M3D-buffer, M3D 2×1 multiplexer, and M3D D flip-flop, consisting of modularized M3D inverters (M3D-INVs), have been proposed. In the previous M3D logic, each M3D logic had to be designed separately for a standard cell library. The proposed M3D logic is designed by placing modularized M3D-INVs and connecting interconnects such as metal lines or monolithic inter-tier-vias between M3D-INVs. The electrical characteristics of the previous and proposed M3D logics were simulated using the technology computer-aided design and Simulation Program with Integrated Circuit Emphasis with the extracted parameters of the previously developed LETI-UTSOI MOSFET model for n- and p-type MOSFETs and the extracted external capacitances. The area, propagation delay, falling/rising times, and dynamic power consumption of the proposed M3D logic are lower than those of previous versions. Despite the larger space and lower performance of the proposed M3D logic in comparison to the previous versions, it can be easily designed with a single modularized M3D-INV and without having to design all layouts of the logic gates separately.