Trong-The Nguyen^1,2,3, Thi-Kien Dao^1,2,3,*, Trinh-Dong Nguyen^2,3

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3213-3234, 2023, DOI:10.32604/iasc.2023.041356

Abstract Wireless sensor networks (WSNs) are widely used for various practical applications due to their simplicity and versatility. The quality of service in WSNs is greatly influenced by the coverage, which directly affects the monitoring capacity of the target region. However, low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges. This study proposes an optimal node planning strategy for network coverage based on an adjusted single candidate optimizer (ASCO) to address these issues. The single candidate optimizer (SCO) is a metaheuristic algorithm with stable implementation procedures. However, it has limitations in avoiding local optimum traps in… More >

Sakorn Mekruksavanich¹, Narit Hnoohom², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2669-2686, 2023, DOI:10.32604/iasc.2023.038549

Abstract Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet and motion information from wearable… More >

Pan Chen^1,2,3, Baojin Chu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09199

Abstract Generally, film dielectric materials often exhibit size-dependent structure and electric properties. In this work, we demonstrate a similar behavior in bulk Na_0.5Bi_0.5TiO₃ (NBT)-based polycrystalline ceramics. According to the results from X-ray diffraction, the (Na_0.5Bi_0.5)_0.92Ba_0.08Ti_0.99Mg_0.01O_2.99 (NBT8M1.0) ceramic showed a complex structure that consists of rhombohedral, tetragonal and cubic symmetries. We found, when decreasing the thickness of a ϕ 10 mm NBT8M1.0 ceramic from 1230 μm to 230 μm, the ceramic showed increased content of cubic symmetry (CC) from 28% to 56%. Meanwhile, the piezoelectric response (d₃₃₎ increased from 107 pC/N to 134 pC/N and the depolarization temperature (T_d) decreased from 170… More >

Omar Reyad^1,2, Hazem Ibrahim Shehata^1,3, Mohamed Esmail Karar^1,4,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084

Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically… More >

Lilan Zou¹, Bo Liang¹, Xu Cheng², Shufa Li^1,*, Cong Lin^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2641-2659, 2023, DOI:10.32604/cmes.2023.028037

Abstract Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment. In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment, we proposed a more effective and robust target detection framework based on deep learning, which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection. Firstly, the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with… More > Graphic Abstract

Yingying Chen¹, Weiqiang Tan², Shidang Li^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2675-2691, 2023, DOI:10.32604/cmes.2023.028533

Abstract In this paper, a novel fairness-aware harvested energy efficiency-based green transmission scheme for wireless information and power transfer (SWIPT) aided sensor networks is developed for active beamforming of multiantenna transmitter and passive beamforming at intelligent reflecting surfaces (IRS). By optimizing the active beamformer assignment at the transmitter in conjunction with the passive beamformer assignment at the IRS, we aim to maximize the minimum harvested energy efficiency among all the energy receivers (ER) where information receivers (IR) are bound to the signal-interference-noise-ratio (SINR) and the maximum transmitted power of the transmitter. To handle the non-convex problem, both semi-definite relaxation (SDR) and… More >

Lei Yu¹, Hongjun Wang^1,*, Yubin Yue¹, Shucong Liu¹, Xiangxiang Mao², Fengshou Gu³

Structural Durability & Health Monitoring, Vol.17, No.4, pp. 315-336, 2023, DOI:10.32604/sdhm.2022.024899

Abstract In recent years, high-end equipment is widely used in industry and the accuracy requirements of the equipment have been risen year by year. During the machining process, the high-end equipment failure may have a great impact on the product quality. It is necessary to monitor the status of equipment and to predict fault diagnosis. At present, most of the condition monitoring devices for mechanical equipment have problems of large size, low precision and low energy utilization. A wireless self-powered intelligent spindle vibration acceleration sensor system based on piezoelectric energy harvesting is proposed. Based on rotor sensing technology, a sensor is… More >

Yousif Sufyan Jghef¹, Mohammed Jasim Mohammed Jasim², Subhi R. M. Zeebaree^3,*, Rizgar R. Zebari⁴

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1651-1664, 2023, DOI:10.32604/csse.2023.037449

Abstract Wireless Sensor Networks (WSNs) gather data in physical environments, which is some type. These ubiquitous sensors face several challenges responsible for corrupting them (mostly sensor failure and intrusions in external agents). WSNs were disposed to error, and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach. Machine learning (ML) was extremely utilized for detecting faults in WSNs. Therefore, this study proposes a billiards optimization algorithm with modified deep learning for fault detection (BIOMDL-FD) in WSN. The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency. To do so, the presented BIOMDL-FD… More >

Madallah Alruwaili^1,*, Muhammad Hameed Siddiqi¹, Kamran Farid², Mohammad Azad¹, Saad Alanazi¹, Asfandyar Khan², Abdullah Khan²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2293-2311, 2023, DOI:10.32604/csse.2023.040312

Abstract Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate, ECG, nasal/oral airflow, temperature, light sensor, and fall detection sensor. Different researchers have done different work in the field of health monitoring with sensor networks. Different researchers used built-in apps, such as some used a small number of parameters, while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate, and outdated tools for study development. While no efficient, cheap, and updated work is proposed in the field of sensor-based health monitoring systems. Therefore, this study developed… More >

M. Vlachos^1,*, R. Lopardo², A. Amditis¹

Journal on Internet of Things, Vol.4, No.4, pp. 235-261, 2022, DOI:10.32604/jiot.2022.040820

Abstract In the era of the Internet of Things (IoT), the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge. This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power. Thus, trying to tackle this issue, in this paper, a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed. The IoT system consists mainly of three components: (1) the ultra-low power consumption Wireless Sensor Node (WSN), (2) the IoT gateway and… More >