Search Results (48)

The deployment of wireless sensor networks (WSNs) is fundamental for smart buildings, industrial automation, and healthcare. However, achieving uniform wireless coverage in complex indoor environments remains a significant challenge due to structural obstructions and non-line-of-sight areas. As an example of this problem and of the proposed solution, this paper addresses the signal coverage issue in an L-shaped corridor. We present a novel solution based on a double, entirely passive Reflective Intelligent Surface (RIS) configuration. This setup significantly improves both the amplitude and the spatial uniformity of the received power in the shadowed region, effectively overcoming the limitations of the single-RIS configuration, which often leaves coverage gaps in Non-Line-of-Sight areas. To model realistic multipath propagation, we developed a custom ray-tracing algorithm that takes advantage of the regular geometry of indoor environments to improve processing speed. The field response of an RIS is then evaluated by analyzing possible reflecting-surface configurations and comparing the performance of single- and double-RIS configurations. Additionally, a statistical analysis of the power received by an observer located anywhere in the corridor, considering RIS positioning uncertainties across various deployment scenarios, has been performed. Results show that the double-RIS solution increases the covered area by $76\%$, considering a receiver sensitivity of $-100$ dBm. The proposed approach can be easily generalized to other typical indoor environments with similar structural characteristics. Full article

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation. However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs. To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance. The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection. Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy. The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation. An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor. The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 °C. It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%. An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected. Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%. The system accurately tracked charging/discharging patterns, validating its effectiveness. This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution. Full article

The growing diversity of cloud services has made evaluating their relative merits in terms of price, functionality, and availability increasingly complex, particularly given the wide range of deployment alternatives and service capabilities. Cloud manufacturing often requires the integration of multiple services to accomplish user tasks, where the effectiveness of resource utilization and capacity sharing is closely tied to the adopted service composition strategy. This complexity, intensified by competition among providers, renders cloud service selection and composition an NP-hard problem involving multiple challenges, such as identifying suitable services from large pools, handling composition constraints, assessing the importance of quality-of-service (QoS) parameters, adapting to dynamic conditions, and managing abrupt changes in service and network characteristics. To address these issues, this study applies the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in conjunction with Multi-Criteria Decision Making (MCDM) to evaluate and rank cloud services, while the Analytic Hierarchy Process (AHP) combined with the entropy weight method is employed to mitigate subjective bias and improve evaluation accuracy. Building on these techniques, a novel Adaptive Multi-Level Linked-Priority-based Best Method Selection with Multistage User-Feedback-driven Cloud Service Composition (MLLP-BMS-MUFCSC) framework is proposed, demonstrating enhanced service selection efficiency and superior quality of service compared to existing approaches. Full article

A novel architectural design is proposed to optimize the thermal management of lithium-ion batteries (LiBs) through a software-enabled switching mechanism. This approach addresses critical challenges such as hot-spot generation, peak temperature rise, and uneven thermal distribution—issues commonly observed in conventional single-terminal battery modules (STBMs). The proposed dual-terminal configuration integrates an enhanced battery pack structure with a software-enabled switching algorithm that identifies the 50% depth of discharge (DoD) and toggles the current path between two terminals to supply the load. Correspondingly, the module also incorporates the division of four thermal zones and four regions concept in the battery module (BM). Experiments were conducted to evaluate the performance of the proposed model at five different C-rates: 0.5C, 0.75C, 1C, 1.25C, and 1.5C. The results demonstrate that the software-enabled dual-terminal switching (Se-DTS) consistently outperforms the STBM across three key aspects. First, in terms of peak temperature, Se-DTS achieved reductions of 19.33%, 17.83%, and 12.72% at C-rates of 1C, 1.25C, and 1.5C, respectively. Second, in thermal distribution, Se-DTS improved performance, with an 86.1% reduction at 1.25C. Third, regarding hot-spot reduction, improvements of 100% (regional level) and 72.22% (zonal level) were observed at 1.25C, while at 1.5C, an 80% improvement was achieved at the zonal level, without using a cooling system. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries under complex operating conditions remains challenging, as the SOC signal combines a global linear (quasi-linear) trend with localized dynamic fluctuations driven by polarization, ion diffusion, temperature gradients, and load transients. In practice, open-circuit-voltage (OCV) approaches are affected by hysteresis and parameter drift, while high-fidelity electrochemical models require extensive parameterization and significant computational resources that hinder their real-time deployment in battery management systems (BMS). Purely data-driven methods capture temporal patterns but may under-represent abrupt local fluctuations and blur the distinction between trend and fluctuation, leading to biased SOC tracking when operating conditions change. To address these issues, LF-Net is proposed. The architecture decomposes battery time series into long-term trend and local fluctuation components. A linear branch models the quasi-linear SOC evolution. Multi-scale convolutional and differential branches enhance sensitivity to transient dynamics. An adaptive Fusion Module aggregates the representations, improving interpretability and stability, and keeps the parameter budget small for embedded hardware. Our experimental results demonstrate that the proposed model achieves a mean absolute error (MAE) of 0.0085 and a root-mean-square error (RMSE) of 0.0099 at 40 °C, surpassing mainstream models and confirming the method’s efficacy. Full article

The accurate prediction of path loss is essential for planning and optimizing communication networks, as it directly impacts the user experience. In 5G signal propagation, the mix of varied terrain and dense high-rise buildings poses significant challenges. For example, signals are more prone to multipath effects and occlusion and shadowing occur often, leading to high nonlinearities and uncertainties in the signal path. Traditional and shallow models often fail to accurately depict 5G signal characteristics in complex terrains, limiting the accuracy of path loss modeling. To address this issue, our research introduces innovative feature engineering and prediction models for 5G signals. By utilizing smartphones as signal receivers and creating a multimodal system that captures 3D structures and obstructions in the N1 and N78 bands in China, the study aimed to overcome the shortcomings of traditional linear models, especially in mountainous areas. It employed the XGBoost algorithm with Optuna for hyperparameter tuning, improving model performance. After training on real 5G data, the model achieved a breakthrough in 5G signal path loss prediction, with an R² of 0.76 and an RMSE of 3.81 dBm. Additionally, SHAP values were employed to interpret the results, revealing the relative impact of various environmental features on 5G signal path loss. This research enhances the accuracy and stability of predictions and offers a technical framework and theoretical foundation for planning and optimizing wireless communication networks in complex environments and terrains. Full article

Power batteries and their management technology are crucial for the safe and efficient operation of electric vehicles (EVs). The life and safety issues of power batteries have always plagued the EV industry. To achieve an intelligent battery management system (BMS), it is crucial to accurately estimate the internal state of the power battery. The purpose of this review is to analyze the current status of research on multi-state estimation of power batteries, which mainly focuses on the estimation of state of charge (SOC), state of energy (SOE), state of health (SOH), state of power (SOP), state of temperature (SOT), and state of safety (SOS). Moreover, it also analyzes and prospects the research hotspots, development trends, and future challenges of battery state estimation. It is a significant guide for designing BMSs for EVs, as well as for achieving intelligent safety management and efficient power battery use. Full article

Energy efficiency (EE) remains an underexploited opportunity in many developing economies, where a complex interplay of policy, institutional, and market-related challenges limit its implementation at scale. This study explores the structural, economic, and policy-related constraints affecting the EE market in Jordan, a country with a high dependence on imported energy. Using a multi-framework approach, we apply the political, economic, social, technological, environmental, and legal (PESTEL) framework to categorize these barriers, complemented by Brown’s business model (BM) typology to enhance the analytical depth. Primary data were collected through semi-structured interviews with key market actors. The findings highlight issues such as economic volatility, regulatory fragmentation, and the structural biases associated with donor-driven interventions, which contribute to an uneven and loosely regulated market environment in which businesses face significant scaling challenges. This study reflects on international experience to explore how strategies from other contexts might inform markets’ adaptation in emerging economies. This study concludes with targeted policy recommendations aimed at clarifying regulatory pathways and supporting more effective market delivery. This research contributes to ongoing policy discourse by highlighting how context-specific BM innovations might help address systemic barriers, while potentially supporting national energy goals. Full article

Electric vehicles (EVs) are central to global efforts in reducing carbon emissions and transitioning toward sustainable transportation. This literature review emphasizes the critical role of safety in advancing EV adoption in Indonesia by drawing insights from global advancements and addressing local challenges. Key findings highlight that while EVs promise significant environmental benefits, safety concerns, such as battery thermal runaway risks and structural reliability in diverse road and climatic conditions, remain significant barriers. Issues such as battery safety, including thermal runaway risks, and the reliability of structural designs in Indonesia’s diverse road and climatic conditions are pivotal. Globally, advancements in battery management systems (BMS), crash-resistant vehicle designs, and autonomous driving technologies provide effective pathways to mitigate these safety risks. Locally, the development of safety standards tailored to tropical climates and robust infrastructure is essential. Leveraging Indonesia’s natural resources, such as nickel, offers opportunities to produce safer and cost-effective batteries. Additionally, policy frameworks like Presidential Regulation No. 55/2019 must prioritize safety measures, including rigorous testing, recycling protocols, and public education. This study concludes by advocating for an integrated approach that combines technological innovation, enhanced safety features, and supportive policies to accelerate EV adoption in Indonesia. Future research should focus on improving safety technologies, lifecycle assessments, and renewable energy integration to ensure the long-term success of EV adoption in the country. Full article

Arterial diseases (ADs) are a significant health problem, with high mortality and morbidity rates. Endovascular interventions, such as balloon angioplasty (BA), bare-metal stents (BMSs), drug-eluting stents (DESs) and drug-coated balloons (DCBs), have made significant progress in their treatments. However, the issue has not been fully resolved, with restenosis remaining a major concern. In this context, bioresorbable vascular stents (BVSs) have emerged as a promising area of investigation. This manuscript includes articles that assess the use of BVSs. Studies have identified ongoing challenges, such as negative vascular remodeling and elastic recoil post-angioplasty, stent-related injury, and in-stent restenosis following BMS placement. While DESs have mitigated these issues to a considerable extent, their durable structures are unable to prevent late stent thrombosis and delay arterial recovery. BVSs, with their lower support strength and tendency towards thicker scaffolds, increase the risk of scaffold thrombosis. Despite inconsistent study results, the superiority of BVSs over DESs has not been demonstrated in randomized trials, and DES devices continue to be the preferred choice for most cases of arterial disease. Esprit BTK (Abbott Vascular) received approval from the US FDA for below-knee lesions in 2024, offering hope for the use of BVSs in other vascular conditions. Enhancing the design and thickness of BVS scaffolds may open up new possibilities. Large-scale and longer-term comparative studies are still required. This article aims to provide an overview of the use of biodegradable stents in the endovascular treatment of vascular stenosis. Full article

The increase in set-valued data such as transaction records and medical histories has introduced new challenges in data anonymization. Traditional anonymization techniques targeting structured microdata comprising single-attribute- rather than set-valued records are often insufficient to ensure privacy protection in complex datasets, particularly when re-identification attacks leverage partial background knowledge. To address these limitations, this study proposed the Local Generalization and Reallocation (LGR) + algorithm to replace the Normalized Certainty Penalty loss measure (hereafter, NCP) used in traditional LGR algorithms with the Information Gain Heuristic metric (hereafter, IGH). IGH, an entropy-based metric, evaluates information loss based on uncertainty and provides users with the advantage of balancing privacy protection and data utility. For instance, when IGH causes greater information-scale data annotation loss than NCP, it ensures stronger privacy protection for datasets that contain sensitive or high-risk information. Conversely, when IGH induces less information loss, it provides better data utility for less sensitive or low-risk datasets. The experimental results based on using the BMS-WebView-2 and BMS-POS datasets showed that the IGH-based LGR + algorithm caused up to 100 times greater information loss than NCP, indicating significantly improved privacy protection. Although the opposite case also exists, the use of IGH introduces the issue of increased computational complexity. Future research will focus on optimizing efficiency through parallel processing and sampling techniques. Ultimately, LGR+ provides the only viable solution for improving the balance between data utility and privacy protection, particularly in scenarios that prioritize strong privacy or utility guarantees. Full article

This article provides a comprehensive overview of the potential challenges and solutions of second-life batteries. First, safety issues of second-life batteries are investigated, which is highly related to the thermal runaway of battery systems. The critical solutions for the thermal runaway problem are discussed, including structural optimization, parameter identification, advanced BMS, and artificial intelligence (AI)-based control strategies. Furthermore, the cell inhomogeneity problem of second-life battery systems is analyzed, where the passive balancing strategy and active balancing strategy are reviewed, respectively. Then, the compatibility issue of second-life batteries is investigated to determine whether electrical dynamic characteristics of a second-life battery can meet the performance requirements for energy storage. In addition, date security and protection methods are reviewed, including digital passport, smart meters and Internet of Things (IoT). The future trends and solutions of key challenges for second-life battery utilization are discussed. Full article

Synthetic aperture radar (SAR) has been extensively applied in remote sensing applications. Nevertheless, it is a challenge to process and interpret SAR images. The key to interpreting SAR images lies in transforming them into other forms of remote sensing images to extract valuable hidden remote sensing information. Currently, the conversion of SAR images to optical images produces low-quality results and incomplete spectral information. To address these problems, an end-to-end network model, S2MS-GAN, is proposed for converting SAR images into multispectral images. In this process, to tackle the issues of noise and image generation quality, a TV-BM3D module is introduced into the generator model. Through TV regularization, block-matching, and 3D filtering, these two modules can preserve the edges and reduce the speckle noise in SAR images. In addition, spectral attention is added to improve the spectral features of the generated MS images. Furthermore, we construct a very high-resolution SAR-to-MS image dataset, S2MS-HR, with a spatial resolution of 0.3 m, which is currently the most comprehensive dataset available for high-resolution SAR-to-MS image interpretation. Finally, a series of experiments are conducted on the relevant dataset. Both quantitative and qualitative evaluations demonstrate that our method outperforms several state-of-the-art models in translation performance. The solution effectively facilitates high-quality transitions of SAR images across different types. Full article

Lithium-ion battery state of health (SOH) estimation is critical in battery management systems (BMS), with data-driven methods proving effective in this domain. However, accurately estimating SOH for lithium-ion batteries remains challenging due to the complexities of battery cycling conditions and the constraints of limited data. This paper proposes an estimation approach leveraging partial incremental capacity curves and transfer learning to tackle these challenges. First, only partial voltage segments are utilized for incremental capacity analysis, which are then fed into a stacked bidirectional gated recursive unit (SBiGRU) network, and finally, transfer learning is utilized to address issues related to limited data availability and differing data distributions. The method is further enhanced through hyperparameter optimization to refine estimation accuracy. The proposed method is validated in two publicly available datasets. For the base model, the root mean square error is 0.0033. With the transfer learning method, which utilized only 1.6% of the target domain data, the root mean square error is 0.0039. Experimental results demonstrate that the proposed method can accurately estimate SOH and works well in training and testing over different voltage ranges. The results underscore the potential of the proposed SOH estimation method for lithium-ion batteries. Full article

With the growing adoption of battery energy storage systems in renewable energy sources, electric vehicles (EVs), and portable electronic devices, the effective management of battery systems has become increasingly critical. The advent of wireless battery management systems (wBMSs) represents a significant innovation in battery management technology. Traditional wired battery management systems (BMSs) face challenges, including complexity, increased weight, maintenance difficulties, and a higher chance of connection failure. In contrast, wBMSs offer a robust solution, eliminating physical connections. wBMSs offer enhanced flexibility, reduced packaging complexity, and improved reliability. Given that wBMSs are still in a preliminary stage, this review paper explores their evolution, current state, and future directions. A comprehensive survey of state-of-the-art wBMS technologies, including academic and commercial solutions, is elaborated in this paper. We compare wireless communication technologies like Bluetooth Low Energy (BLE), Zigbee, Near-Field Communication (NFC), Wi-Fi, and cellular networks in the context of wBMSs. We discuss their performance in terms of efficiency, reliability, scalability, and security. Despite its promising outlook, wBMSs still face challenges such as data security, signal interference, regulatory and standardization issues, and competition from the continued advancement of wired BMS technologies, making the advantages of wBMSs less evident. This paper concludes with guidelines for future research and development of wBMSs, aiming to address these challenges and pave the way for a broad adoption of wBMSs across various applications. This paper aims to inspire further research and innovation in the field, contributing to developing an industry-ready wBMS. Full article