Search Results (8)

Automated livestock monitoring in wide-area grasslands is a critical component of smart agriculture development. Devices such as Unmanned Aerial Vehicles (UAVs), remote sensing, and high-mounted cameras provide unique monitoring perspectives for this purpose. The high-resolution images they capture cover vast grassland backgrounds, where targets often appear as small, distant objects and are extremely unevenly distributed. Applying standard detectors directly to such images yields poor results and extremely high miss rates. To improve the detection accuracy of small targets in high-resolution images, methods represented by Slicing Aided Hyper Inference (SAHI) have been widely adopted. However, in specific scenarios, SAHI’s drawbacks are dramatically amplified. Its strategy of uniform global slicing divides each original image into a fixed number of sub-images, many of which may be pure background (negative samples) containing no targets. This results in a significant waste of computational resources and a precipitous drop in inference speed, falling far short of practical application requirements. To resolve this conflict between accuracy and efficiency, this paper proposes an efficient detection framework named CAMS-AI (Clustering and Adaptive Multi-level Slicing for Aided Inference). CAMS-AI adopts a “coarse-to-fine” intelligent focusing strategy: First, a Region Proposal Network (RPN) is used to rapidly locate all potential target areas. Next, a clustering algorithm is employed to generate precise Regions of Interest (ROIs), effectively focusing computational resources on target-dense areas. Finally, an innovative multi-level slicing strategy and a high-precision model are applied only to these high-quality ROIs for fine-grained detection. Experimental results demonstrate that the CAMS-AI framework achieves a mean Average Precision (mAP) comparable to SAHI while significantly increasing inference speed. Taking the RT-DETR detector as an example, while achieving 96% of the mAP_50–95 accuracy level of the SAHI method, CAMS-AI’s end-to-end frames per second (FPS) is 10.3 times that of SAHI, showcasing its immense application potential in real-world, high-resolution monitoring scenarios. Full article

Due to the severe threats posed by smart jammers, anti-jamming decision making has become an essential technology for wireless communications. Most of the existing anti-jamming decision-making approaches have adopted Q-Learning to improve accuracy. However, the performances of these approaches drop dramatically in fast-varying jamming environments. Thus, an advanced Q-Learning approach utilizing domain knowledge graph as prior knowledge is proposed to select the optimal strategies with high flexibility and accuracy in different jamming environments. Specifically, by taking a knowledge graph that contains anti-jamming knowledge to initialize the Q-table, Q-Learning can avoid becoming stuck at local suboptimal solutions and obtain accurate strategies with fewer iterations. The iterations of the proposed approach are one third of those of other approaches based on Q-Learning and the average rewards of the proposed approach have improved by 2 percent. Numerical results demonstrate the optimality and excellent performance of the proposed approach over various existing benchmarks. Full article

The purpose of this study was to develop and characterise bionanocomposites based on chitosan (CHIT) and alginate (ALG) in two series, which were subsequently functionalised with emulsions based on a combination of water, oil, ozonated oil and hibiscus flower extracts. The structure and morphology of the materials produced were characterised by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and ultraviolet and visible light (UV-Vis) absorption spectroscopy, along with a surface colour analysis and the determination of the mechanical and thermal properties of the resulting composites. Functionalisation did affect the analysed composite parameters. The FTIR spectra indicated that the polysaccharide matrix components were compatible. The SEM images also confirmed the presence of nano/microcapsules in the polysaccharide matrix. The obtained results indicate that the order of adding polysaccharides has a significant impact on the encapsulation capacity. The encapsulation resulted in the improved thermal stability of the composites. The emissions analysis showed that the composites containing nano/microcapsules are characterised by a higher emission intensity and are sensitive to acid or base changes. Significant differences in emission intensity were observed even at low concentrations of acids and bases. A drop in the mechanical properties was observed following functionalisation. The results of this study suggest that these bionanocomposites can be used as active and/or smart packaging materials. Full article

Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm. Full article

Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3. Full article

Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations. We propose a method that transforms the recorded motion sensor sequences invariantly to sensor unit orientation. The method is based on estimating the sensor unit orientation and representing the sensor data with respect to the Earth frame. We also calculate the sensor rotations between consecutive time samples and represent them by quaternions in the Earth frame. We incorporate our method in the pre-processing stage of the standard activity recognition scheme and provide a comparative evaluation with the existing methods based on seven state-of-the-art classifiers and a publicly available dataset. The standard system with fixed sensor unit orientations cannot handle incorrectly oriented sensors, resulting in an average accuracy reduction of 31.8%. Our method results in an accuracy drop of only 4.7% on average compared to the standard system, outperforming the existing approaches that cause an accuracy degradation between 8.4 and 18.8%. We also consider stationary and non-stationary activities separately and evaluate the performance of each method for these two groups of activities. All of the methods perform significantly better in distinguishing non-stationary activities, our method resulting in an accuracy drop of 2.1% in this case. Our method clearly surpasses the remaining methods in classifying stationary activities where some of the methods noticeably fail. The proposed method is applicable to a wide range of wearable systems to make them robust against variable sensor unit orientations by transforming the sensor data at the pre-processing stage. Full article

Fabrics with moisture management properties are strongly expected to benefit various potential applications in daily life, industry, medical treatment and protection. Here, a bifacial fabric with dual trans-planar and in-plane liquid moisture management properties was reported. This novel fabric was fabricated to have a knitted structure on one face and a woven structure on the other, contributing to the different in-plane water transfer properties of the fabric. A facile three-step plasma treatment was used to enrich the bifacial fabric with asymmetric wettability and liquid absorbency. The plasma treated bifacial fabric allowed forced water to transfer from the hydrophobic face to hydrophilic face, while it prevented water to spread through the hydrophobic face when water drops were placed on the hydrophilic face. This confirmed one-way water transport capacity of the bifacial fabric. Through the three-step plasma treatment, the fabric surface was coated with a Si-containing thin film. This film contributed to the hydrophobic property, while the physical properties of the fabrics such as stiffness and color were not affected. This novel fabric can potentially be used to design and manufacture functional and smart textiles with tunable moisture transport properties. Full article

Reaction-oriented research in flow chemistry and microreactor has been extensively focused upon in special journal issues and books. On a process level, this resembled the “drop-in” (retrofit) concept with the microreactor replacing a conventional (batch) reactor. Meanwhile, with the introduction of the mobile, compact, modular container technology, the focus is more on the process side, including also providing an end-to-end vision of intensified process design. Exactly this is the focus of the current special issue “Design and Engineering of Microreactor and Smart-Scaled Flow Processes” of the journal “Processes”. This special issue comprises three review papers, five research articles and two communications. [...] Full article