Mobile Terminals Research Articles

Signalling overhead minimization aware handover execution using ensemble learning in next generation wireless networks

Upcoming smart intelligent heterogeneous wireless networks (HWNs) and their uses can greatly benefit from the merging of long-term evolution (LTE) sub-6 GHz along with millimeter wave (mmWave) frequencies by boosting the coverage, bandwidth, reliability, seamless connectivity, and high quality of service (QoS). Nevertheless, because of the inability of directed waves in terms of coverage, it is difficult to locate the appropriate mmWave remote radio units (RRUs). Therefore, it is crucial to lessen the burden of the handover signaling processes. In meeting research requirements this paper presents signaling overhead minimization aware handover execution (SOMAHE) model. The SOMAHE model first introduces a novel handover mechanism between LTE and mmWave is presented in this research, followed by a machine learning (ML)-based autonomous handover execution technique. To estimate the handover success rate, the model introduces a feature ensemble learning (FEL) model built using XGBoost (XGB) model that makes use of sampling windows channel data. To conclude, combining FEL into the SOMAHE model reduces signaling overhead while simultaneously increasing the handover success-rate. Experiment results with varying mobile terminals, demonstrate that the SOMAHE model significantly outperforms the existing standard deep q-networks (DQN)-based handover-execution method.

Retraction Note: A novel traffic load balancing approach for scheduling of optical transparent antennas (OTAs) on mobile terminals

Retraction Note: A novel traffic load balancing approach for scheduling of optical transparent antennas (OTAs) on mobile terminals

Green Computation Offloading With DRL in Multi‐Access Edge Computing

ABSTRACTIn multi‐access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers; (2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process; (3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS‐DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS‐DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous time slots to upgrade both the actor and critic networks, followed by a buffer reset. Comprehensive experiments validate the superior performance, including stability and convergence, of our algorithm when juxtaposed with prior studies.

A growth posture identification method of immature peaches in natural environments

The precise bagging of immature peaches by the fruit bagging robot requires the identification of the target young fruits as well as the acquisition of their growth angles. The network models employed in single detection algorithms exhibit complexity and pose challenges for deployment on mobile terminals as they heavily rely on the availability of labeled samples. A semi-supervised learning and lightweight strategy based on YOLOv8n-seg was proposed to identify the growth posture of immature peaches in the work. Firstly, the self-training method and data enhancement in semi-supervised learning were used to generate efficient pseudo-label data. A tremendous labeling workload was solved. Secondly, the YOLOv8n-seg backbone network was replaced with an improved MobileNetv3 structure for better real-time detection on MT to reduce network parameters and calculations. The model was easily deployed with improved detection speed. Meanwhile, the original upsampling module was replaced with the CARAFE module to enhance the recognition capability of global features, which considered the impact of immature peaches on the nearby color background. The CIOU loss function was ultimately substituted with the SIOU loss function to further optimize the boundary frame loss and target detection accuracy. The enhanced model could predict the coordinate information of immature peaches and calculate growth angles. The experimental results show that the improved peach seedling growth posture network model has a weight size of only 23.1% of the original model. Additionally, the algorithm achieved a remarkable rate of 87.8% in identifying young peach fruits, with an average error of ±3.3° in estimating the growth angle. It took an average of 31 ms to detect a 3024 × 4032 pixels image in the edge computing device JETSON AGX ORIN CLB development kits. The method could rapidly identify immature peaches and estimate the growth angle, which ensured accurate bagging.

Shared Aperture Wideband Endfire/Broadside mm-Wave Phased Array With Enhanced Scanning Range Integrated With Metal Frame for Mobile Terminals

Shared Aperture Wideband Endfire/Broadside mm-Wave Phased Array With Enhanced Scanning Range Integrated With Metal Frame for Mobile Terminals

An Energy-Efficient Scheme Design for NOMA-Based UAV-Assisted MEC Systems

UAV-assisted MEC networks provide extensive communication coverage and massive computation services for mobile terminals (MTs), which are considered a promising edge paradigm to support future air–ground integrated communications. In this paper, an energy-efficient scheme in NOMA-based UAV-assisted MEC systems is proposed to address the system’s energy constraints and its inability to support massive MT access. Our goal is to minimize system-weighted energy consumption by jointly optimizing the allocation of transmission power, computation resources, and UAV trajectory scheduling. As the formulated problem is non-convex and difficult to solve directly, we decompose it into two manageable sub-problems and propose an iterative algorithm based on successive convex approximations (SCA) to solve each sub-problem alternatively. Simulation results show that the proposed joint optimization algorithm achieves a significant performance improvement compared to other benchmark approaches.

Research on Remote eSIM Provisioning Management Technology for 5G Terminal

The use of eSIM in mobile terminals such as the Internet of Vehicles has become the general trend, and more and more enterprises have put forward clear requirements for the remote provisioning of eSIM. This paper aims to combine 5G technology to analyze remote eSIM provisioning management technology for terminal, and combine algorithm improvement to improve remote eSIM information management and control effects. Moreover, this paper describes in detail the three-layer communication structure in the communication model, the design of edge gateway nodes, and the query mechanism for cloud center request names and data resources. In addition, this paper uses experiments to evaluate the connectivity of the eSIM three-layer communication model and the characteristics of the NDN network. Through experimental research, it can be seen that the remote eSIM provisioning management model proposed in this paper has certain advantages when applied to the Internet of Vehicles compared with the existing remote provisioning management technologies for the Internet of Vehicles. This article takes the Internet of Vehicles as an example to conduct research. At the same time, its support for mobility can be well applied to the Internet of Vehicles to meet the mobility of terminal vehicles. Therefore, it can provide some reference in the subsequent development of Internet of Vehicles technology.

Thermal calibration for triaxial gyroscope of MEMS-IMU based on segmented systematic method

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method.

Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission.

In response to the conflicting demands between real-time satellite communication and high-resolution synthetic aperture radar (SAR) imaging, we propose a method that aligns the data transmission rate with the imaging data volume. This approach balances SAR performance with the requirements for real-time data transmission. To meet the need for mobile user terminals to access real-time SAR imagery data of their surroundings without depending on large traditional ground data transmission stations, we developed an application system based on filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM). To address the interference problem with SAR signals' transmission and reception, we developed a signal sequence based on spaceborne SAR echo and data transmission and reception. This system enables SAR and data transmission signals to share the same frequency band, radio frequency transmission system, and antenna, creating an integrated sensing and communication system. Simulation experiments showed that, compared to the equal power allocation scheme for subcarriers, the echo image signal-to-noise ratio (SNR) improved by 2.79 dB and the data transmission rate increased by 24.075 Mbps.

Shared-Aperture Three-Antenna Module Using a Single Floating Metal With Small Clearance for 5G Mobile Terminals

Shared-Aperture Three-Antenna Module Using a Single Floating Metal With Small Clearance for 5G Mobile Terminals

MobilenetV2-RC: A lightweight network model for retinopathy classification in retinal OCT images

Abstract Retinopathy is an important ophthalmic disease that causes blindness in the elderly population. With the global old peoples increasing, the ophthalmic healthcare system is more and significant to pre-diagnosis. Optical Coherence Tomography (OCT) is considered the gold standard for ophthalmic treatment and diagnosis. OCT technologies and equipment continue to develop towards the intelligence and convenience for requirements of rapid diagnosis in the remote and poverty-stricken areas. We proposed an improved MobilenetV2 lightweight model for retinopathy classification (MobilenetV2-RC), which incorporates Spatial and Channel Reconstruction Convolution (SCConv) and the improved Convolutional Block Attention Module (CBAM) attention mechanism into the framework. Not only can it effectively limit feature redundancy to reduce model parameters, but also enhance the ability of feature representation to improve classification accuracy. The parameters of the proposed model are only 1.96 M with an overall accuracy of 98.96%, which is higher 3.32% than the original MobilenetV2. Compared with ResNet18, InceptionV3, and VGG16_BN, the overall accuracy is increased by 4.6%, 6.3%, and 3.9%, respectively. The test results of UCSD and Duke open-source datasets are more remarkable. Experimental results show that our proposed algorithm has strong reliability and generalization for the accurate classification of retinopathy, and a greater application prospect in the intelligent diagnosis of ophthalmology and mobile detection terminals.

Design of a Tri-band Minkowski Fractal MIMO Antenna for FR2 5G Applications

The growing importance of fifth-generation (5G) communication technologies for the Internet-of-Things have made it necessary to design multi-element antennas that operate within the millimetre-wave spectrum. However, simultaneously achieving multiple operating frequencies, high gain and good isolation between the elements of such small-sized antennas is a challenging task. In this research, a composite right/left hand millimetre-wave fractal antenna was designed for 5G mobile terminals, in order to simultaneously satisfy these key design goals. This aim was accomplished by designing a 2 x 2 multiple-input, multiple output (MIMO) antenna based on the second-order Minkowski fractal using an electromagnetics simulation software, Computer Simulation Technology (CST) Studio Suite ®. Two composite-right/left hand structures were included on the reverse side of the antenna, to reduce the coupling between the MIMO antenna elements. Simulation results show that the designed antenna operates at three frequency bands within the millimetre-wave spectrum, namely 25.52 GHz – 28.23 GHz, 36.5 GHz – 37.5 GHz, and 47.7 GHz – 49.7 GHz. The maximum antenna gains at these three bands are 6.9 dBi, 7.5 dBi, and 6.0 dBi, respectively. Also, good isolation is realized between the antenna elements at the operating frequencies, with a minimum value of 10.8 dB. With a spacing of 0.03λ between the MIMO antenna elements, the overall size of the designed antenna was 30 mm x 12 mm x 0.508 mm, which compares positively with other related fractal antenna designs. The designed antenna shows good prospects for integration into various mobile terminal devices to aid 5G-based millimetre-wave communications.

Flexible Wearable Chemoresistive Ethylene Gas-Monitoring Device Utilizing Pd/Ti3C2Tx Nanocomposites for In Situ Nondestructive Monitoring of Kiwifruit Ripeness.

Kiwifruit, renowned for its antioxidant properties and nutritional richness, faces challenges in maintaining quality during transportation, often leading to suboptimal products reaching the market. To address this issue, a wireless transmission flexible ethylene monitoring device (WFEMD) was developed. This device comprises a flexible ethylene gas sensor and a signal transmission processing unit integrated with electronic components, enabling real-time monitoring capabilities. In this study, the catalytic activity of Pd and Pd/Ti heterojunctions was leveraged to enhance the ethylene gas sensing. The impact of Ti3C2Tx modified with varying masses of Pd nanoparticles on ethylene gas response levels was investigated. The signal transmission processing unit, fabricated by using the laser direct-writing method, was optimized to collect signals from the flexible ethylene gas sensor, convert them into corresponding ethylene concentrations, and transmit data via an antenna. By introducing a random forest (RF) classification algorithm, a remarkable 97.5% accuracy in predicting kiwifruit ripeness grades was achieved. The algorithm facilitated precise classification by collecting key parameters such as ethylene and CO2 during transportation. The WFEMD enables real-time acquisition of kiwifruit ethylene gas information, which is transmitted wirelessly for data visualization and traceability via mobile terminals. This empowers managers with timely insights into ethylene emissions and ripeness predictions, facilitating informed decision-making processes.

Distributed Mobile Antennas: A Novel Miniaturized Wideband Antenna Strategy to Maximize the Use of the Fragmented Spaces of the Mobile Terminals

Distributed Mobile Antennas: A Novel Miniaturized Wideband Antenna Strategy to Maximize the Use of the Fragmented Spaces of the Mobile Terminals

Compact Tri-Band Shared-Aperture Antenna With Large Frequency Ratio for 5G Mobile Terminals

Compact Tri-Band Shared-Aperture Antenna With Large Frequency Ratio for 5G Mobile Terminals

A Deep Learning Framework for Evaluating the Over-the-Air Performance of the Antenna in Mobile Terminals.

This study introduces RTEEMF (Real-Time Evaluation Electromagnetic Field)-PhoneAnts, a novel Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance, addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens' box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model's accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development (R&D), offering a promising alternative to traditional electromagnetic field simulations. The study is publicly available on GitHub for further development and customization. Engineers can customize the model using their own datasets.

Effect of stability state transition of variable potential well in tri-hybridized energy harvesters

Energy harvesting is a crucial technology for self-powering wireless sensor nodes and mobile terminals. In addressing this, we utilized a variable potential well to create a tri-hybrid energy harvester that integrates piezoelectric effect, electromagnetic induction, and triboelectric effect. The compression and stretching of a compressible magnet-spring oscillator alter the system’s potential well, thereby promoting the operation of the piezoelectric unit (PU), electromagnetic unit (EU), and triboelectric unit (TU). A coupled electromechanical model using Hamilton’s principle was established. By calculating the static potential energy and restoring force, we discussed the stability state transition, revealing that when the magnet distance is less than 38 mm, the system transitions from a mono-stable to a bi-stable state. The experimental study investigated the impact of excitation and structural parameters on voltage and power. A high excitation level enhances all three units. The TU demonstrates excellent electrical performance under high potential energy in the bi-stable state, while the PU performs best in mono- and bi-stable switching structure. The EU shows relatively balanced electrical performance across various conditions. These findings offer new insights into enhancing energy harvesting capabilities for external circuits.

Analysis of Guangzhou city image perception based on weibo text data (2019–2023)

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to “Guangzhou City” between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

Transforming policing in Ghana through technology: a focus on police operations in the Greater Accra region

Integrating technology into policing has the potential to greatly improve the effectiveness, transparency, and community participation of law enforcement agencies. This study examines the profound influence of technology on the Ghana Police Service, with a specific emphasis on the Greater Accra Region. The main aim of this research is to examine how technological advancements might enhance police results and tackle existing difficulties in the context of law enforcement in Ghana. The study adopts a qualitative research approach and collects data from police officers and community members through the use of semi-structured interviews and focus group discussions. The investigation involved the participation of a combined total of twenty-six police officials from the Tema and Accra Regional Headquarters, as well as twenty-two community members. The data gathering procedure sought to gather a wide range of viewpoints on the present condition of technology in law enforcement, its advantages, difficulties, and prospects for future advancement. The results demonstrate that the utilisation of technology, such as mobile data terminals and social media platforms, can greatly improve the effectiveness of police operations, promote community involvement, and increase transparency. Nevertheless, the presence of obstacles such as insufficient infrastructure, cultural opposition, and the necessity for extensive training were also acknowledged. The research enhances the current knowledge by presenting empirical evidence on the benefits and constraints of technology in law enforcement, particularly in the setting of Ghana. This study emphasises the significance of making strategic investments in technology infrastructure and training, as well as the necessity of establishing legal frameworks to tackle ethical and privacy concerns. Additionally, it proposes that future studies should prioritise examining the enduring viability of technological advancements in law enforcement and investigating their effects in various geographical areas. The study suggests that politicians and law enforcement organisations should promote the deployment of technology in a way that is successful and build public trust in the Ghana Police Service.

Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.