Search Results (2,258)

Unlike traditional remote control systems for controlling unmanned aerial vehicles (UAVs) and drones, active research is being carried out in the domain of vision-based hand gesture recognition systems for drone control. However, contrary to static and sensor based hand gesture recognition, recognizing dynamic hand gestures is challenging due to the complex nature of multi-dimensional hand gesture data, present in 2D images. In a real-time application scenario, performance and safety is crucial. Therefore we propose a hybrid lightweight dynamic hand gesture recognition system and a 3D simulator based drone control environment for live simulation. We used transfer learning-based computer vision techniques to detect dynamic hand gestures in real-time. The gestures are recognized, based on which predetermine commands are selected and sent to a drone simulation environment that operates on a different computer via socket connectivity. Without conventional input devices, hand gesture detection integrated with the virtual environment offers a user-friendly and immersive way to control drone motions, improving user interaction. Through a variety of test situations, the efficacy of this technique is illustrated, highlighting its potential uses in remote-control systems, gaming, and training. The system is tested and evaluated in real-time, outperforming state-of-the-art methods. The code utilized in this study are publicly accessible. Further details can be found in the “Data Availability Statement”. Full article

Dopamine is a neurotransmitter which is classified as a catecholamine. It is also one of the main metabolites produced by some tumor types (such as paragangliomas and neoblastomas). As such, determining and monitoring the level of dopamine is of the utmost importance, ideally using analytical techniques that are sensitive, simple, and low in cost. Due to this, we have developed a non-enzymatic dopamine sensor that is highly sensitive, selective, and rapidly detects the presence of dopamine in the body. A hybrid material fabricated with NiO and ZnO, based on date fruit extract, was synthesized by hydrothermal methods and using NiO as a precursor material. This paper discusses the role of date fruit extracts in improving NiO’s catalytic performance with reference to ZnO and the role that they play in this process. An X-ray powder diffraction study, a scanning electron microscope study, and a Fourier transform infrared spectroscopy study were performed in order to investigate the structure of the samples. It was found that, in the composite NiO/ZnO, NiO exhibited a cubic phase and ZnO exhibited a hexagonal phase, both of which exhibited well-oriented aggregated cluster shapes in the composite. A hybrid material containing NiO and ZnO has been found to be highly electro-catalytically active in the advanced oxidation of dopamine in a phosphate buffer solution at a pH of 7.3. It has been found that this can be accomplished without the use of enzymes, and the range of oxidation used here was between 0.01 mM and 4 mM. The detection limit of non-enzymatic sensors is estimated to be 0.036 μM. Several properties of the non-enzymatic sensor presented here have been demonstrated, including its repeatability, selectivity, and reproducibility. A test was conducted on Sample 2 for the detection of banana peel and wheat grass, and the results were highly encouraging and indicated that biomass waste may be useful for the manufacture of medicines to treat chronic diseases. It is thought that date fruit extracts would prove to be valuable resources for the development of next-generation electrode materials for use in clinical settings, for energy conversion, and for energy storage. Full article

Clustering-based routing techniques are key to significantly extending the lifetime of wireless sensor networks (WSNs). However, these approaches often do not address the common hotspot issue in multi-hop WSNs. To overcome this challenge and enhance network lifespan, this study presents FQ-UCR, a hybrid approach that merges unequal clustering based on fuzzy logic (FL) with routing optimized through Q-learning. In FQ-UCR, a tentative CH employs a fuzzy inference system (FIS) to compute its probability of being selected as the final CH. By using the Q-learning algorithm, the best forwarding cluster head (CH) is chosen to construct the data transmission route between the CHs and the base station (BS). The approach is extensively evaluated and compared with protocols like EEUC and CHEF. Simulation results demonstrate that FQ-UCR improves energy efficiency across all nodes, significantly extends network lifetime, and effectively alleviates the hotspot issue. Full article

In recent years, spoof localized surface plasmon (SLSP) have gained increasing attention due to their strong electromagnetic wave confinements. Based on the multipole resonance of SLSP, a high-Q-factor terahertz resonator based on CMOS technology is proposed. Specifically, a quadrilateral hybridized SLSP structure, composed of a core and a cavity SLSP resonator, is designed to reduce electric dimension and improve the Q-factor. The experimentally measured Q-factor reached 56.7 at 194 GHz, which is quite a high value within the terahertz frequency band, particularly given the compact electrical dimension of 0.081λ × 0.081λ. Moreover, pharmaceutical testing in the terahertz frequency range was successfully conducted, including glucose and two traditional Chinese medicines: Chuanbei and Sanqi. And three frequency shifts (4 GHz, 3.2 GHz, and 1.4 GHz) were observed. Thus, the SLSP resonator holds great potential for high-performance terahertz applications. Full article

The applications of polymeric materials are being constantly reviewed and improved. In the present world, the word hybrid, and the general idea of combining two or more inherently different approaches, designs, and materials is gaining significant attention. The area of sustainable materials with a low environmental impact is also rapidly evolving with many new discoveries, including the use of materials of a natural origin and countless combinations thereof. This review tries to summarize the current state of knowledge about hybrid polymeric materials and their applications with special attention to the materials that can be considered “environmentally friendly”. As the current application field is quite broad, the review was limited to the following topics: packaging, medical applications, sensors, water purification, and electromagnetic shielding. Furthermore, this review points out the new prospects and challenges for the use of the mentioned materials in terms of creating novel solutions with different nano and micro-materials of mostly natural and renewable origin. Full article

As the core component of an airplane, the health status of the aviation engine is crucial for the safe operation of the aircraft. Therefore, predicting the remaining service life of the engine is of great significance for ensuring its safety and reliability. In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. For each sensor of the engine, an SA-CNN-BiLSTM network is established. The one-dimensional convolutional neural network and the bidirectional long short-term memory network are used to extract the spatial features and temporal features of the input data, respectively. Moreover, multichannel modeling is utilized to achieve the parallel processing of different sensors. Subsequently, the results are stitched together to establish a mapping relationship with the engine’s remaining useful life (RUL). Experimental validation was conducted on the aero-engine C-MAPSS dataset. The prediction results were compared with those of the other seven models to verify the effectiveness of this method in predicting the remaining service life. The results indicate that the proposed method significantly reduces the prediction error compared to other models. Specifically, for the two datasets, their mean absolute errors were only 11.47 and 11.76, the root-mean-square error values were only 12.26 and 12.78, and the scoring function values were only 195 and 227. Full article

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article

Bisphenol A (BPA) is a typical environmental estrogen that is distributed worldwide and has the potential to pose a hazard to the ecological environment and human health. The development of an efficient and sensitive sensing strategy for the monitoring of BPA residues is of paramount importance. A novel electrochemical sensor based on carbon black and carbon nanofibers composite (CB/f-CNF)-assisted signal amplification has been successfully constructed for the amperometric detection of BPA in foods. Herein, the hybrid CB/f-CNF was prepared using a simple one-step ultrasonication method, and exhibited good electron transfer capability and excellent catalytic properties, which can be attributed to the large surface area of carbon black and the strong enhancement of the conductivity and porosity of carbon nanofibers, which promote a faster electron transfer process on the electrode surface. Under the optimized conditions, the proposed CB/f-CNF/GCE sensor exhibited a wide linear response range (0.4–50.0 × 10⁻⁶ mol/L) with a low limit of detection of 5.9 × 10⁻⁸ mol/L for BPA quantification. Recovery tests were conducted on canned peaches and boxed milk, yielding satisfactory recoveries of 86.0–102.6%. Furthermore, the developed method was employed for the rapid and sensitive detection of BPA in canned meat and packaged milk, demonstrating comparable accuracy to the HPLC method. This work presents an efficient signal amplification strategy through the utilization of carbon/carbon nanocomposite sensitization technology. Full article

Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance. In this paper, we use an improved TimeGAN model for the augmentation of energy consumption data, which incorporates a multi-head self-attention mechanism layer into the recovery model to enhance prediction accuracy. A hybrid CNN-GRU model is used to predict the energy consumption data from the operational processes of manufacturing equipment. After data augmentation, the prediction model exhibits significant reductions in RMSE and MAE along with an increase in the R² value. The prediction accuracy of the model is maximized when the amount of generated synthetic data is approximately twice that of the original data. Full article

In recent years, metal surface plasmon resonance sensors and dielectric guided-mode resonance sensors have attracted the attention of researchers. Metal sensors are sensitive to environmental disturbances but have high optical losses, while dielectric sensors have low losses but limited sensitivity. To overcome these limitations, hybrid resonance sensors that combine the advantages of metal and dielectric were proposed to achieve a high sensitivity and a high Q factor at the same time. In this paper, a hybrid hollow cylindrical tetramer array was designed, and the effects of the hole radius, external radius, height, period, incidence angle, and polarization angle of the hollow cylindrical tetramer array on the refractive index sensing properties were quantitatively analyzed using the finite difference time domain method. It is found that the position of the resonance peaks can be freely tuned in the visible and near-infrared regions, and a sensitivity of up to 542.8 nm/RIU can be achieved, with a Q factor of 1495.1 and a figure of merit of 1103.3 RIU⁻¹. The hybrid metal–dielectric nanostructured array provides a possibility for the realization of high-performance sensing devices. Full article

Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering more consistent, precise, and faster scans, potentially reducing human error and healthcare costs. Effective force control is crucial in robotic ultrasound scanning to ensure consistent image quality and patient safety. However, existing robotic ultrasound systems rely heavily on expensive commercial force sensors or the integrated sensors of commercial robotic arms, limiting their accessibility. To address these challenges, we developed a cost-effective, lightweight, 3D-printed force sensor and a hybrid position–force control strategy tailored for robotic ultrasound scanning. The system integrates patient-to-robot registration, automated scanning path planning, and multi-sensor data fusion, allowing the robot to autonomously locate the patient, target the region of interest, and maintain optimal contact force during scanning. Validation was conducted using an ultrasound-compatible abdominal aortic aneurysm (AAA) phantom created from patient CT data and healthy volunteer testing. For the volunteer testing, during a 1-min scan, 65% of the forces were within the good image range. Both volunteers reported no discomfort or pain during the whole procedure. These results demonstrate the potential of the system to provide safe, precise, and autonomous robotic ultrasound imaging in real-world conditions. Full article

Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This study introduces a novel hybrid plant growth model integrating stochastic, empirical, and optimization approaches, using Internet of Things sensors for real-time data collection. Unlike traditional methods, the hybrid model systematically captures environmental variability, simulates plant growth dynamics, and optimizes resource inputs. The prototype growth chamber, equipped with IoT sensors for monitoring environmental parameters such as light intensity, temperature, CO₂, humidity, and water intake, was primarily used to provide accurate input data for the model and specifically light intensity, water intake and nutrient intake. While experimental tests on lettuce were conducted to validate initial environmental conditions, this study was focused on simulation-based analysis. Specific tests simulated plant responses to varying levels of light, water, and nutrients, enabling the validation of the proposed hybrid model. We varied light durations between 6 and 14 h/day, watering levels between 5 and 10 L/day, and nutrient concentrations between 3 and 11 g/day. Additional simulations modeled different sowing intervals to capture internal plant variability. The results demonstrated that the optimal growth conditions were 14 h/day of light, 9 L/day of water, and 5 g/day of nutrients; maximized plant biomass (200 g), leaf area (800 cm²), and height (90 cm). Key novel metrics developed in this study, the Growth Efficiency Ratio (GER) and Plant Growth Index (PGI), provided solid tools for evaluating plant performance and resource efficiency. Simulations showed that GER peaked at 0.6 for approximately 200 units of combined inputs, beyond which diminishing returns were observed. PGI increased to 0.8 to day 20 and saturated to 1 by day 30. The role of IoT sensors was critical in enhancing model accuracy and replicability by supplying real-time data on environmental variability. The hybrid model’s adaptability in the future may offer scalability to diverse crop types and environmental settings, establishing a foundation for its integration into decision-support systems for large-scale indoor farming. Full article

A novel electrochemical detection method utilizing a cost-effective hybrid-modified electrode has been established. A glassy carbon (GC) modified electrode was tested for its ability to measure electrochemical tTG antibody levels, which are essential for diagnosing and monitoring Celiac disease (CD). Tissue transglutaminase protein biomolecules are immobilized on a quantum dots-polypyrrole nanocomposite in the improved electrode. Initial, quantum dots (QDs) were obtained from Bombyx mori silk fibroin and embedded in polypyrrole film. Using carbodiimide coupling, a polyamidoamine (PAMAM) dendrimer was linked with GQDs-polypyrrole film to improve sensor sensitivity. The tissue transglutaminase (tTG) antigen was cross-linked onto PAMAM using N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC)-N-hydroxy succinimide (NHS) chemistry to develop a nanoprobe that can detect human serum anti-tTG antibodies. The physicochemical characteristics of the synthesized nanocomposite were examined by FTIR, UV-visible, FE-SEM, EDX, and electrochemical studies. The novel electrode measures anti-tissue antibody levels in real time using human blood serum samples. The modified electrode has great repeatability and an 8.7 U/mL detection limit. Serum samples from healthy people and CD patients were compared to standard ELISA kit assays. SPSS and Excel were used for statistical analysis. The improved electrode and detection system can identify anti-tissue antibodies up to 80 U/mL. Full article

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments. Full article

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging networks that offer a wide range of applications, including mine prevention, power grid maintenance, underground pipeline monitoring, and upstream oil monitoring. MI-based wireless underground sensor networks (WUSNs), utilizing small antenna coils, offer a viable solution by providing consistent channel conditions. The cross-layer protocols address the specific challenges of WUSNs, leading to improved network performance and enhanced operational capabilities in real-world applications. This work proposes a distributed cross-layer solution, leveraging the hybrid marine predator naked mole rat algorithm (MPNMRA) for MI-operated WUSNs. The solution, called DECMN (distributed energy-throughput efficient cross-layer network using MPNMRA), is designed to optimize the MI communication channels, MI relay coils (MI waveguide), and MI waveguide with 3D coils to fulfill quality of service (QoS) parameters, while achieving energy savings and throughput gains. DECMN utilizes the interactions between various layers to develop cross-layer protocols based on MPNMRA. Simulation results demonstrate the effectiveness of DECMN, offering energy savings, increased throughput, and reliable transmissions within the performance limits. Full article