Search Results (2,645)

Vehicle detection plays a pivotal role in traffic management as a key technology for intelligent traffic management and driverless driving. However, current deep learning-based vehicle detection models face several challenges in practical applications. These include slow detection speeds, large computational and parametric quantities, high leakage and misdetection rates in target-intensive environments, and difficulties in deploying them on edge devices with limited computing power and memory. To address these issues, this paper proposes an improved vehicle detection method called SMA-YOLO, based on the YOLOv7 model. Firstly, MobileNetV3 is adopted as the new backbone network to lighten the model. Secondly, the SimAM attention mechanism is incorporated to suppress background interference and enhance small-target detection capability. Additionally, the ACON activation function is substituted for the original SiLU activation function in the YOLOv7 model to improve detection accuracy. Lastly, SIoU is used to replace CIoU to optimize the loss of function and accelerate model convergence. Experiments on the UA-DETRAC dataset demonstrate that the proposed SMA-YOLO model achieves a lightweight effect, significantly reducing model size, computational requirements, and the number of parameters. It not only greatly improves detection speed but also maintains higher detection accuracy. This provides a feasible solution for deploying a vehicle detection model on embedded devices for real-time detection. Full article

In the field of advanced electrical energy conversion and storage, remarkable attention has been given to the development of new, more sustainable electrolytes. In this regard, the combination of redox shuttles with aqueous bio-polymer gels seems to be a valid alternative via which to overcome the typical drawbacks of common liquid electrolytes such as corrosion, volatility or leakage. Despite the promising results obtained so far, redox-active species such as bis(6,6′-dimethyl-2,2′-bipyridine)copper(I) trifluoromethanesulfonylimide, ([Cu(I)(dmby)₂]TFSI), still present inherent challenges associated with their poor water solubility and oxidative lability, which prevents their employment in cheap and sustainable aqueous electrolytes. The present study investigates the stabilization of the Cu(I) complex ([Cu(I)(dmby)₂]TFSI) within two natural hydrogels based on the biopolymers κ-carrageenan and galactomannan, using ZnO nanoparticles as gelling agents. These eco-friendly and biocompatible systems are proposed as potential matrices for quasi-solid electrolytes (QSEs), offering a promising platform for advanced electrolyte design in electrochemical applications. Both hydrogels effectively stabilized and retained the redox species within their networks. In order to shed light on distinct stabilization mechanisms, complementary FTIR and SEM analyses were relevant to reveal the structural rearrangements, specific to each matrix, upon complex incorporation. Furthermore, thermogravimetric analysis confirmed notable thermal resilience in both systems, with the galactomannan-based gel demonstrating enhanced performance. Altogether, this work introduces a novel strategy for embedding copper-based redox couples into gelled electrolytes, paving the way toward their integration in real electrochemical devices, where long-term stability, redox retention, and energy conversion efficiency are critical evaluation criteria. Full article

In this paper, a novel Tunneling-Drift-Diffusion Field-Effect Transistor (TDDFET) based on the combination of the quantum tunneling and conventional drift-diffusion mechanisms is proposed for the design of ternary logic circuits. The working principle of the TDDFET is analyzed in detail. Then, the device is packaged as a “black box” based on the table lookup method and further embedded into the HSPICE platform using the Verilog-A language. The basic unit circuits, such as the Standard Ternary Inverter (STI), Negative Ternary Inverter (NTI), Positive Ternary Inverter (PTI), Ternary NAND gate (T-NAND), and Ternary NOR gate (T-NOR), are designed. In addition, based on the designed unit circuits, the combinational logic circuits, such as the Ternary Encoder (T-Encoder), Ternary Decoder (T-Decoder), and Ternary Half Adder (T-HA), and the sequential logic circuits, such as the Ternary D-Latch and edge-triggered Ternary D Flip-Flop (T-DFF), are built, which has important significance for the subsequent investigation of ternary logic circuits. Full article

We present a highly sensitive and selective gas sensor based on an advanced silicon-on-insulator tunnel field-effect transistor (SOI-TFET) architecture, enhanced through the integration of customized conducting polymers. In this design, traditional metal gates are replaced with distinct functional polymers—PPP-TOS/AcCN, PP-TOS/AcCN, PP-FE(CN)₆³⁻/H₂O, PPP-TCNQ-TOS/AcCN, and PPP-ClO₄/AcCN—which enable precise molecular recognition and discrimination of various target gases. To further enhance sensitivity, the device employs an oppositely doped source region, significantly improving gate control and promoting stronger band-to-band tunneling. This structural modification amplifies sensing signals and improves noise immunity, allowing reliable detection at trace concentrations. Additionally, optimization of the subthreshold swing contributes to faster switching and response times. Thermal stability is addressed by embedding a P-type buffer layer within the buried oxide, which increases thermal conductivity and reduces lattice temperature, further stabilizing device performance. Experimental results demonstrate that the proposed sensor outperforms conventional SOI-TFET designs, exhibiting superior sensitivity and selectivity toward analytes such as methanol, chloroform, isopropanol, and hexane. Beyond gas sensing, the unique polymer-functionalized gate design enables integration of microbial biosensing capabilities, making the platform highly versatile for biochemical detection. This work offers a promising pathway toward ultra-sensitive, low-power sensing technologies for environmental monitoring, industrial safety, and medical diagnostics. Full article

Effective noise elimination is essential for ensuring data reliability in high-accuracy measurement systems. However, selecting the optimal denoising strategy for diverse and non-stationary signal types remains a major challenge. This study presents a wavelet-based denoising optimization framework that systematically identifies and applies the most suitable noise reduction model for each signal segment. By evaluating multiple wavelet types and thresholding strategies, the proposed method enables adaptive and automated selection tailored to the specific characteristics of each signal. The framework was validated using synthetic, open-access, and experimentally acquired signals in both reference-based and reference-free scenarios. Extensive testing covered signals from power quality disturbance (PQD) events, electrocardiogram (ECG) data, and electroencephalogram (EEG) recordings, all of which represent critical applications where signal integrity under noise is essential. The method achieved optimal model selection in 22.15 s (across 4558 iterations) on a standard PC, with an average denoising time of 4.86 ms per signal window. These results highlight its potential for real-time and embedded applications, including smart grid monitoring systems, wearable health devices, and automated biomedical diagnostic platforms, where adaptive, fast, and reliable denoising is vital. The framework’s versatility makes it highly relevant for deployment in smart grid monitoring systems and intelligent energy infrastructures requiring robust signal conditioning. Full article

Technological innovation has significantly transformed the field of statistics, not only in data analysis but also in data collection. Traditional methods based on direct observation have evolved into hybrid approaches that combine passively collected data (e.g., from GPS or accelerometers) with active user input through digital interfaces. This evolution has led to Smart Surveys—next-generation tools that leverage smart devices, such as smartphones and wearables, to collect data actively (via questionnaires or images) and passively (via embedded sensors). Smart Surveys offer strategic value in industrial contexts by enabling real-time data collection on worker behavior, environments, and operational conditions. However, the heterogeneity of such data poses challenges in management, integration, and quality assurance. This study proposes a modular system architecture incorporating gamification elements to enhance user participation, particularly among hard-to-reach worker segments, such as mobile or shift workers. By leveraging motivational strategies and interactive feedback mechanisms, the system seeks to foster greater engagement while addressing critical data security and privacy concerns within industrial Internet of Things (IoT) environments. Full article

Unmanned aerial vehicles (UAVs) increasingly demand robust onboard diagnostic frameworks to ensure safe operation under irregular telemetry and mission-critical conditions. This paper presents a real-time fault detection framework for unmanned aerial vehicles (UAVs), optimized for deployment on edge devices and designed to handle irregular, nonuniform telemetry. The system reconstructs raw sensor data using compactly supported B-spline interpolation, ensuring stable recovery of flight dynamics under jitter, dropouts, and asynchronous sampling. A lightweight hybrid anomaly detection module—combining a Long Short-Term Memory (LSTM) autoencoder with an Isolation Forest—analyzes both temporal patterns and statistical deviations across reconstructed signals. The full pipeline operates entirely onboard embedded platforms such as the Raspberry Pi 4 and NVIDIA Jetson Nano, with end-to-end inference latency under 50 milliseconds. Experiments using real PX4 UAV flight logs and synthetic fault injection demonstrate a detection accuracy of 93.6% and strong resilience to telemetry disruptions. These results support the feasibility of autonomous, sensor-based health monitoring in UAV systems and broader real-time cyber–physical applications. Full article

In response to the need for precise blossom identification and optimization of key operational parameters in intelligent cherry spraying pollination, the SMD-YOLO (You Only Look Once with spatial and channel reconstruction convolution, multi-scale channel attention, and dual convolution modules) cherry blossom detection model is proposed, along with a pollination experiment platform for parameter optimization. The SMD-YOLO model, built upon YOLOv11, enhances feature extraction through the ScConvC3k2 (spatial and channel reconstruction convolution C3k2) module, incorporates the MSCA (multi-scale channel attention) attention mechanism, and employs the DualConv module for a lightweight design, ensuring both detection accuracy and operational efficiency. Tested on a self-constructed cherry blossom dataset, the model delivered a precision of 87.6%, a recall rate of 86.1%, and an mAP (mean average precision) reaching 93.1% with a compact size of 4765 KB, 2.28 × 10⁶ parameters, a computational cost of 5.8 G, and a detection speed of 75.76 FPS, demonstrating strong practicality and potential for embedded real-time detection in edge devices, such as cherry pollination robots. To further enhance pollination effectiveness, a dedicated pollination experiment bench was designed, and a second-order orthogonal rotational combination experiment method was employed to systematically optimize three key parameters: spraying distance, spraying time, and liquid flow rate. Experimental results indicate that the optimal pollination effect occurs when the spraying distance is 3.4 cm, spraying time is 1.9 s, and liquid flow rate is 339 mL/min, with a deposition amount of 0.18 g and a coverage rate of 97.25%. This study provides a high-precision image detection algorithm and operational parameter optimization basis for intelligent and precise cherry blossom pollination. Full article

With the rapid advancement of computer vision and deep learning technologies, the accuracy and efficiency of real-time gesture recognition have significantly improved. This paper introduces a gesture-controlled robot system based on thermal imaging sensors. By replacing traditional physical button controls, this design significantly enhances the interactivity and operational convenience of human–machine interaction. First, a thermal imaging gesture dataset is collected using Python3.9. Compared to traditional RGB images, thermal imaging can better capture gesture details, especially in low-light conditions, thereby improving the robustness of gesture recognition. Subsequently, a neural network model is constructed and trained using Keras, and the model is then deployed to a microcontroller. This lightweight model design enables the gesture recognition system to operate on resource-constrained embedded devices, achieving real-time performance and high efficiency. In addition, using a standalone thermal sensor for gesture recognition avoids the complexity of multi-sensor fusion schemes, simplifies the system structure, reduces costs, and ensures real-time performance and stability. The final results demonstrate that the proposed design achieves a model test accuracy of 99.05%. In summary, through its gesture recognition capabilities—featuring high accuracy, low latency, non-contact interaction, and low-light adaptability—this design precisely meets the core demands for “convenient, safe, and natural interaction” in rehabilitation, smart homes, and elderly assistive devices, showcasing clear potential for practical scenario implementation. Full article

Continuous progress in the sector of additive manufacturing has drastically aided the design and fabrication of urban furniture, offering high levels of customization and adaptability. This work looks into the potential of 3D printing to transform urban public spaces by allowing for the creation of functional, aesthetically pleasing, and user-centered furniture solutions. Through additive manufacturing processes, urban furniture can be tailored to meet the unique needs of diverse communities, allowing for the extended usage of sustainable materials, modular designs, and smart technologies. The flexibility of 3D printing also promotes the fabrication of complex, intricate designs that would be difficult or cost-prohibitive using traditional methods. Additionally, 3D-printed furniture can be optimized for specific environmental conditions, providing solutions that enhance accessibility, improve comfort, and promote inclusivity. The various advantages of 3D-printed urban furniture are examined, including reduced material waste and the ability to rapidly prototype and iterate designs alongside the potential for on-demand, local production. By embedding sensors and IoT devices, 3D-printed furniture can also contribute to the development of smart cities, providing real-time data for urban management and improving the overall user experience. As cities continue to encourage and adopt sustainable and innovative solutions, 3D printing is believed to play a crucial role in future urban infrastructure planning. Full article

Total knee replacement (TKR) is a common procedure for pain relief and restoration of the mobility of the knee joint in patients with severe knee joint problems. Despite this, some patients still suffer from stiffness, instability, or pain caused by soft tissue imbalance, malalignment, or implant-related issues. Previously, surgeons have had to use their experience and visual judgment to balance the knee, which has resulted in variability of outcomes. Smart knee implants are addressing these issues by using sensor technology to provide real-time feedback on joint motion, pressure distribution, and loading forces. This enables more accurate intra-operative adjustment, enhancing implant positioning and soft tissue balance and eliminating post-operative adjustment. These implants also enable post-operative monitoring, simplifying the ability to have more effective individualized rehabilitation programs directed at optimizing patient mobility and minimizing complications. While the patient pool for smart knee implantation remains not commonly documented, it was found in a study that 83.6% of the patients would opt to have the monitoring device implemented, and nearly 90% find reassurance in monitoring their healing indicators. As the number of knee replacements is likely to rise due to aging populations and the rising prevalence of joint disease, smart implants are a welcome development in orthopedics, optimizing long-term success and patient satisfaction. Smart knee implants are built with embedded sensors such as force, motion, temperature, and pressure detectors placed within the implant structure. These sensors provide real-time data during surgery and recovery, allowing earlier detection of complications and supporting tailored rehabilitation. The design aims to improve outcomes through better monitoring and personalized care. Full article

This article explores the concept of textuality as embedded within contemporary architecture, understood as the capacity of buildings to generate meanings, narratives, and interpretations that transcend their physical and functional dimensions. An interdisciplinary approach is adopted, integrating architectural theory, semiotics, hermeneutics, and cultural studies, positioning architecture as a form of symbolic production deeply intertwined with current social and technological contexts. The primary aim is to demonstrate how certain paradigmatic buildings operate as open texts that engage in dialogue with their users, urban surroundings, and cultural frameworks. The methodology combines theoretical analysis with an in-depth study of three emblematic cases: the Guggenheim Museum in Bilbao, the Centre Pompidou in Paris, and the Seattle Public Library. The findings reveal that these buildings articulate multiple layers of meaning, fostering rich and participatory interpretive experiences that influence both the perception and construction of public space. The study concludes that contemporary architecture functions as a narrative and symbolic device that actively contributes to the shaping of collective imaginaries. The article also identifies the study’s limitations and proposes future research directions concerning architectural textuality within the context of emerging digital technologies. Full article

Concrete is an essential material in the construction industry due to its strength and versatility. However, its quality can be compromised by environmental factors during its fresh and early-age states. To address this vulnerability, various sensors have been implemented to monitor critical parameters. While high-precision sensors (e.g., piezoelectric and fiber optic) offer accurate measurements, their cost and fragility limit their widespread use in construction environments. In response, this study proposes a cost-effective, Arduino-based wireless monitoring system to track temperature and humidity in fresh and early-age concrete elements. The system was validated through laboratory tests on cylindrical specimens and industrial applications on self-compacting concrete New Jersey barriers. The sensors recorded temperature variations between 15 °C and 35 °C and relative humidity from 100% down to 45%, depending on environmental exposure. In situ monitoring confirmed the system’s ability to detect thermal gradients and evaporation dynamics during curing. Additionally, the presence of embedded sensors caused a tensile strength reduction of up to 37.5% in small specimens, highlighting the importance of sensor placement. The proposed solution demonstrates potential for improving quality control and curing management in precast concrete production with low-cost devices. Full article

Embedded systems in IoT are expected to be run by reliable, resource-efficient software. Devices on the edge are typically required to communicate with central nodes, and in some setups with each other, constituting a distributed system. The Erlang language, favored for its constructs that support building fault-tolerant, distributed systems, offers solutions to these challenges. Its dynamic type system and higher-level abstractions enable fast development, while also featuring tools for building highly available and fault-tolerant applications. To study the viability of using Erlang in embedded systems, we analyze the solutions the language offers, contrasting them with the challenges of developing embedded systems, with a particular focus on resource use. We measure the footprint of the language’s constructs in executing tasks characteristic of end devices, such as gathering, processing and transmitting sensor data. We conduct our experiments with constructs and data of varying sizes to account for the diversity in software complexity of real-world applications. Our measured data can serve as a basis for future research, supporting the design of the software stack for embedded systems. Our results demonstrate that Erlang is an ideal technology for implementing software on embedded systems and a suitable candidate for developing a prototype for a real-world use case. Full article

Biofilms are a spontaneously formed slimy matrix of extracellular polymeric substances (EPS) enveloping miniature bacterial colonies, which aid in pathogen colonization, shielding the bacteria from antibiotics, as well as imparting them resistance towards the same. Biofilms employ a robust communication mechanism called quorum sensing that serves to keep their population density constant. What is most significant about biofilms is that they contribute to the development of bacterial virulence by providing protection to pathogenic species, allowing them to colonize the host, and also inhibiting the activities of antimicrobials on them. They grow on animate surfaces (such as on teeth and intestinal mucosa, etc.) and inanimate objects (like catheters, contact lenses, pacemakers, endotracheal devices, intrauterine devices, and stents, etc.) alike. It has been reported that as much as 80% of human infections involve biofilms. Serious implications of biofilms include the necessity of greater concentrations of antibiotics to treat common human infections, even contributing to antimicrobial resistance (AMR), since bacteria embedded within biofilms are protected from the action of potential antibiotics. This review explores various contemporary strategies for controlling biofilms, focusing on their modes of action, mechanisms of drug resistance, and innovative approaches to find a solution in this regard. This review interestingly targets the extracellular polymeric matrix as a highly effective strategy to counteract the potential harm of biofilms since it plays a critical role in biofilm formation and significantly contributes to antimicrobial resistance. Full article