Abstract Multi‐sensor networks often encounter challenges such as inconsistent sampling times among local sensors and data loss during transmission. To address these issues, this paper employs a data loss compensation strategy to reconstruct missing data information. It designs the state estimation of local sensors utilising iterative state equations, leveraging multistep prediction techniques to estimate sensor states at unsampled points, thereby transforming the asynchronous sensor network system into a synchronous one. Furthermore, the projection theorem is applied to determine the fusion weights of local sensors, grounded on the principle of square‐averaging significance. Ultimately, data information pertaining to the same target is fused through arithmetic averaging, guided by distance correlation. Simulation outcomes demonstrate that the proposed algorithm balances estimation accuracy with communication overhead, achieved by designing an optimal number of communication iterations.
The multiple energy power plant-based microgrids (MEPPBM) gradually incorporates multiple energy sources such as solar, wind, and battery energy storage, ensuring reliable security & protection has become a paramount challenge. Conventional fault detection methods often fail to address the unique dynamics of these MEPPBM, leading to delays in fault detection and classification. The need for cutting-edge protection schemes that can operate efficiently in such environments is paramount to maintaining system stability and avoiding potential damage. The main objective of this research is to design such protection and security schemes which detect, classify, and locate faults with high accuracy and rapidly with very low computational burden. Therefore, the paper presents a robust security & protection method for modern MEPPBM, employing a hybrid methodology using the Unscented Kalman Filter (UKF) and Particle Filter (PF) algorithms. The UKF is employed for accurate state estimation of the current & voltage signal from faulty bus. While the PF is employed to calculate fault detection & classification indices named PF based residuals (PFBR) and PF-based Harmonic distortion (PFBHD) from UKF-estimated current signal. Then, the Fault section identification index named PF-computed reactive power (PFCRP) is generated from UKF estimated current & voltage signals. Extensive simulations are performed IEC 61850 microgrid test bed using MATLAB/Simulink 2023b software. The presented scheme effectively detects both high-impedance faults (HIF) and solid faults with a remarkable 99.9% accuracy in under 4 milliseconds. Furthermore, the scheme offers low computational burden, making it highly appropriate & efficient for real-time applications in modern MEPPBM.
Abstract In the field of underwater target detection, the passive sonar is an important means of long‐distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi‐source heterogeneous information with obvious spatio‐temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi‐head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate‐layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.
Harshvardhan Kumar, Ankit Kumar Pandey, Chu-Hsuan Lin
This work presents the high-performance Si/SiO<sub>2</sub> distributed Bragg reflector (DBR)-integrated lateral germanium (Ge) <italic>p-i-n</italic> photodetectors (PDs) for atmospheric gas sensing and fiber-optic telecommunication networks in the short-wave infrared (SWIR) regime. In addition, this study also proposes an optoelectronic compact small-signal noise equivalent circuit model (SSNECM) of the designed device to compute the noise performance at the detectors’ output. Various figure-of merits including current under dark and illumination, responsivity, detectivity, bandwidth, and the noise of the proposed device are estimated at the room temperature (RT) for an incident optical power of <inline-formula> <tex-math notation="LaTeX">${0}.{5} {\mu }{W}$ </tex-math></inline-formula>. Furthermore, the impact of width and height scaling on dark current, responsivity, and bandwidth are investigated to optimize the proposed device. The validation of the proposed model is done by comparing various parameters including dark current, responsivity, and detectivity of the designed device with other Ge PDs. The estimated results show the reduced <italic>trade-off</italic> between responsivity and bandwidth of the designed device. At <inline-formula> <tex-math notation="LaTeX">${\lambda }=1550 nm$ </tex-math></inline-formula>, the proposed device achieves a high detectivity and SNR of <inline-formula> <tex-math notation="LaTeX">$>2\times {10}^{11}$ </tex-math></inline-formula> Jones and 120 dB (at 3 THz), respectively, with the bias voltage of −2V. These encouraging results pave the path for the future development of low-noise and high-speed detectors.
Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes.
In today’s dynamic business environment, work life balance has become one of the key issues faced by many employees all over the world. Maintaining work life balance is an issue increasingly recognized as of strategic importance to organization and of significance to employees. A lack of work life balance also has an adverse effect on their employer's prospects for success in many respects. The main objective of this study is to carry out research on the Sri Lankan telecommunication industry and recognize the impact of work life balance on the employee performance. And also to identify whether the work life balance leads to higher employee performance through employee job satisfaction. The target population of this research is executive level married employees in telecommunication industry in Sri Lanka. This investigation area is used Cluster sampling method to select 2 major companies in telecommunication industry (i.e., Dialog Axiata PLC and Sri Lanka Telecom – Sample Size 100). Data were gathered through questionnaire method. In this study, for the purpose of hypothesis testing the researcher used univariate, bivariate and multivariate statistics methods. The Data were analyzed through SPSS 15.0 software to find out the relationship between variables. Findings of the study reveal that there is a strong relationship between work life balance and employee performance, a strong relationship between work life balance and employee job satisfaction and a strong relationship between employee job satisfaction and employee performance. All these relationships are positive and have significant levels. The research findings give evidence that the better work life balance of the employees leads to increased employee performance and employee job satisfaction.
Quantum memories for light are important components for future long distance quantum networks. We present on-chip quantum storage of telecommunications band light at the single photon level in an ensemble of erbium-167 ions in an yttrium orthosilicate photonic crystal nanobeam resonator. Storage times of up to 10 $\mu$s are demonstrated using an all-optical atomic frequency comb protocol in a dilution refrigerator under a magnetic field of 380 mT. We show this quantum storage platform to have high bandwidth, high fidelity, and multimode capacity, and we outline a path towards an efficient erbium-167 quantum memory for light.
ABSTRACT Automated telecommunication interventions, including short message service and interactive voice response, are increasingly being used to promote adherence to medications prescribed for cardio-metabolic conditions. This systematic review aimed to comprehensively assess the effectiveness of such interventions to support medication adherence, and to identify the behaviour change techniques (BCTs) and other intervention characteristics that are positively associated with greater intervention effectiveness. Meta-analysis of 17 randomised controlled trials showed a small but statistically significant effect on medication adherence, OR = 1.89, 95% CI [1.51, 2.36], I2 = 89%, N = 25,101. Multivariable meta-regression analysis including eight BCTs explained 88% of the observed variance in effect size (ES). The BCTs ‘tailored’ and ‘information about health consequences’ were positively and significantly associated with ES. Future studies could explore whether the inclusion of these and/or additional techniques (e.g., ‘implementation intentions’) would increase the effect of automated telecommunication interventions, using rigorous designs and objective outcome measures.
Over the past decade, the rogue wave debate has stimulated the comparison of predictions and observations among different branches of wave physics, particularly between hydrodynamics and optics, in situations where analogous dynamical behaviors can be identified, thanks to the use of common universal models. Although the scalar nonlinear Schroedinger equation (NLSE) has constantly played a central role for rogue wave investigations, moving beyond the standard NLSE model is relevant and needful for describing more general classes of physical systems and applications. In this direction, the coupled NLSEs are known to play a pivotal role for the understanding of the complex wave dynamics in hydrodynamics and optics. Benefiting from the advanced technology of high-speed telecommunication-grade components, and relying on a careful design of the nonlinear propagation of orthogonally-polarized optical pump waves in a randomly birefringent telecom fiber, this work explores, both theoretically and experimentally, the rogue wave dynamics governed by such coupled NLSEs. We report, for the first time, the evidence of a group of three dark rogue waves, the so-called dark three-sister rogue waves, where experiments, numerics, and analytics show a very good consistency.
This article describes how the Internet of Things (IoT) is a new paradigm shift in information technology (IT). The IoT manifests the phenomenon of ubiquitous computing when objects or ‘things' are connected to the Internet providing automated services related to the things. However, few studies investigated the acceptance of these services by customers. Consequently, the purpose of this article is to investigate the factors that affect the acceptance and use of the IoT services by customers of telecommunication companies in Jordan. A total of 176 respondents participate in this study and the collected data is analyzed using SPSS. The findings indicate that behavioral intention significantly affects the use behavior of IoT services. In addition, IT knowledge is the most important factor that affects the behavioral intention followed by other factors.
Atmospheric clearness is a key issue for free space optical communications (FSO). We present the first active method to achieve FSO through clouds and fog, using ultrashort high intensity laser filaments. The laser filaments opto-mechanically expel the droplets out of the beam and create a cleared channel for transmitting high bit rate telecom data at 1.55 microns. The low energy required for the process allows considering applications to Earth-satellite FSO and secure ground based optical communication, with classical or quantum protocols.
In the past decade, the competition has increased in mobile telecommunication services; moreover, a stagnating rate of diffusion suggests that the market may have reached maturity. Thus, customer loyalty has become an important area of research in the mobile telecommunication sector. The goal of this current study is to offer and test a theoretical model of customer retention in the mobile telecommunication context. To test the theoretical model, a self-administrated questionnaire was developed and tested on a sample of 400 customers. The results show that greater levels of satisfaction toward loyalty program lead to greater customer retention. The satisfaction of loyalty program positively impacts customer relationship satisfaction. The mediating effect of relationship satisfaction is supported. Since the majority of research on perceived justice focused on service recovery and complaint outcomes, this framework provides empirical evidence on the direct and indirect effect of procedural, distributive and interactional perceived justice regarding loyalty programs as antecedents of relational satisfaction loyalty/commitment and retention from a consumer's perspective.
Telecommunication fraud has continuously been causing severe financial loss to telecommunication customers in China for several years. Traditional approaches to detect telecommunication frauds usually rely on constructing a blacklist of fraud telephone numbers. However, attackers can simply evade such detection by changing their numbers, which is very easy to achieve through VoIP (Voice over IP). To solve this problem, we detect telecommunication frauds from the contents of a call instead of simply through the caller’s telephone number. Particularly, we collect descriptions of telecommunication fraud from news reports and social media. We use machine learning algorithms to analyze data and to select the high-quality descriptions from the data collected previously to construct datasets. Then we leverage natural language processing to extract features from the textual data. After that, we build rules to identify similar contents within the same call for further telecommunication fraud detection. To achieve online detection of telecommunication frauds, we develop an Android application which can be installed on a customer’s smartphone. When an incoming fraud call is answered, the application can dynamically analyze the contents of the call in order to identify frauds. Our results show that we can protect customers effectively.