Non-Taylor series based positioning method for location based services

Location Based Services (LBS) has gained increasing popularity in major cities. Due to blocking from man-made structures, the existing Global Positioning System (GPS) could not satisfy LBS applications, especially in street canyon and indoor surroundings. This has lead to the development of Assisted GPS (A-GPS) which can provide better service availability and accuracy gain. In the conventional positioning method, Taylor series expansion is applied to solve non-linear distance equations. This method requires an initial estimation of A-GPS receiver’s position. This paper investigates the positioning method for LBS based on hybrid E-OTD/GNSS. The proposed positioning method is non-Taylor series based. Therefore, it involves less complicated mathematical expansion and substitution. A flexible LBS positioning tool is developed which can generate position information in convenient way. It supports both Taylor series and non-Taylor series based positioning methods. The obtained results showed that the proposed non-Taylor series based positioning method can achieve better positioning accuracy

Alleviation of stale information in weak cache coherence for wireless www

In this paper, we identify the client polling as a cache coherence mechanism that best satisfies the requirements and improves the performance of web services over wireless networks. However, this mechanism suffers from degradation of client satisfaction by potentially sending stale information. Therefore, we propose a conceptual framework, Multilevel Pre-fetching (MLP), to alleviate the stale information forwarded by the web cache to the clients. The MLP is responsible for improving the level of freshness among the cached objects by pre-fetching the most frequently accessed objects

The socio-psychological problems among Malay youth around flat housing in Kuala Lumpur: a case study

The purpose of this study is to investigate the socio-psychological problems among Malay youth around flat housing (low cost housing) in Kuala Lumpur. This qualitative study carried out interview on five Malay males as respondents aged 15-25 in four flat housing areas around Kuala Lumpur. The results from content analysis show that the factors that contribute to social problems in that area are the influence from the surrounding, low self-control and lack of attention from the parents. Factors that contribute to social problems include negative influence from friends, ignorant society and poverty while low self control is driven by a high negative desire and has no 'consciousness' to retreat from it. Parents fail to mould a happy family because of their ignorance to the needs of their children and too busy with daily work. The psychological applications concluded in the discussion and suggestion to overcome the social problems in the flat housing area is included for the purpose of further studies

A design of compact ultra wideband coupler for butler matrix

A design of 3 dB compact ultra wideband (UWB) coupler for a 4 × 4 Butler Matrix, which is operating between 3 and 11 GHz frequency range, is presented in this paper. The proposed design uses multilayer technology that allows having a compact and broad bandwidth coupler. The design uses elliptically shaped broadside coupled strips and a slot created in a common ground plane of two dielectric substrates with the same shape. The design then being fabricated on 0.8 mm thick FR4 substrates and it occupies the dimension of 30 mm × 15 mm. The simulation and experimental results show a good performance in terms of bandwidth, which covers the entire UWB operation. In addition, detailed analysis of air gap effect for S-parameters and phase difference of the designed coupler is presented in this study

Active video games for improving health-related physical fitness in older adults: a systematic review and meta-analysis

BackgroundThe global population is experiencing a rapid rise in the quantity and percentage of older people. In an effort to enhance physical activity among older adults, active video games (AVGs) are being suggested as a compelling alternative and are currently under scrutiny to evaluate their efficacy in promoting the health of older people.ObjectiveThis review aims to synthesize current studies and formulate conclusions regarding the impact of AVGs on the health-related physical fitness of older adults.MethodsSeven databases (PubMed, Web of Science, SCOPUS, SPORTDiscus, EMBASE, MEDLINE, and CINAHL) were searched from inception to January 21, 2024. Eligible studies included randomized controlled trials examining the effect of AVGs compared to control conditions on health-related physical fitness outcomes in older adults. The methodological quality of the included trials was assessed using the PEDro scale, and the certainty of evidence was evaluated using the GRADE approach. A random-effects model was used to calculate effect sizes (ES; Hedge’s g) between experimental and control groups.ResultsThe analysis included 24 trials with a total of 1428 older adults (all ≥ 60 years old). Compared to controls, AVGs produced significant increases in muscular strength (moderate ES = 0.64–0.68, p < 0.05) and cardiorespiratory fitness (moderate ES = 0.79, p < 0.001). However, no significant effects were found for body composition (trivial ES = 0.12–0.14; p > 0.05) and flexibility (trivial ES = 0.08; p = 0.677). The beneficial effects of AVGs were greater after a duration of ≥ 12 vs. < 12 weeks (cardiorespiratory fitness; ES = 1.04 vs. 0.29, p = 0.028) and following ≥ 60 minutes vs. < 60 minutes of session duration (muscular strength; ES = 1.20–1.24 vs. 0.27–0.42, p < 0.05).ConclusionAVGs appear to be an effective tool for enhancing muscular strength and cardiorespiratory fitness in older adults, although their impact on improving body composition and flexibility seems limited. Optimal improvement in cardiorespiratory fitness is associated with a longer duration of AVGs (≥ 12 weeks). Moreover, a session duration of ≥ 60 minutes may provide greater benefits for the muscular strength of older adults.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=482568, identifier CRD42023482568

A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance

The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement

Intelligent Massive MIMO Systems for Beyond 5G Networks: An Overview and Future Trends

Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output (CM-MIMO) systems. This introduces the concept of intelligent massive MIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO-enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping

A low profile, dual-band, dual polarized antenna for indoor/outdoor wearable application

A planar, low-profile, dual-band and dual-polarized antenna on a semi-flex substrate is proposed in this paper. The antenna is fabricated on Rogers substrate with a thickness of 3.04 mm and sized at 70.4 × 76.14 × 3.11 mm3 ( 0.37λ0 × 0.40λ 0× 0.016 λ 0 ) only. The circular polarization property is enabled in the global navigation satellite system (GNSS) L1/E1 (lower) band by introducing a complementary split ring resonator on the antenna patch. Meanwhile, the antenna operates in the second (upper) 2.45 GHz WLAN band is enabled by etching a U-shaped slot on its ground plane. This simultaneous, dual-band and dual-polarized operation enables the proposed antenna to be applied in the indoor/outdoor wearable application. To isolate the antenna against the influence of the human body, a multiband artificial magnetic conductor (AMC) plane is added on the reverse side of the dual-band radiator. Comparison of the antenna without AMC in free space and when evaluated on the chest of a human body backed by AMC showed improved gain; from 3-5.1 dBi in the lower band, and from 1.53-5.03 dBi in the upper band. Besides that, the front-to-back ratio of the AMC backed monopole antenna also improved from 11-21.88 dB and from 2.5-24.5 dB in the GNSS and WLAN bands, respectively. Next, the specific absorption rate (SAR) of the monopole antenna with and without the AMC plane is assessed. Evaluation results indicate that the maximum SAR value decreased by up to 89.45% in comparison with the antenna without AMC in the lower band. This indicates the effectiveness of the AMC array in increasing gain and FBR, besides reducing EM absorption in the human body

Prediction of energy consumption in campus buildings using long short-term memory

In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings