Search Results (32)

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

Heavy metal contamination of soil and water is a growing environmental concern, especially mercury, lead, and cadmium. Therefore, fast and reliable methodologies to assess contamination in the field are in demand. However, many methodologies require laborious, expensive, and cumbersome equipment that is not convenient for rapid field analysis. Mobile phone technology coupled with bioluminescent assays provides accessible hands-on alternatives that has already been shown to be feasible. Previously, we demonstrated that firefly luciferases can be harnessed as luminescence color-tuning sensors for toxic metals. An assay based on such a principle was already successfully applied for teaching biochemistry laboratory lessons, which demonstrates the effect of cadmium on enzyme function based on bioluminescence color change. For analytical detection of cadmium in water, here, we developed a novel bioluminescence assay using the cadmium-sensitive Amydetes vivianii firefly luciferase coupled with a cell phone provided with a program to quantify cadmium concentration based on luminescence color discrimination. The application has proven to be efficient with high precision between 0.10 and 2 mM of cadmium, being appliable to diluted water samples (0.1–2 µM) upon concentration and relying on reference cadmium standards values. The light emitted by the reference standards and samples in a dark box is captured by the smartphone’s camera, which, using computer vision, automatically quantifies cadmium according to the RGB color. CadmiLume is a simple and easy luminescent enzymatic biosensor for cadmium contamination in water samples, which instantaneously can provide results with the convenience of a smartphone in the palm of one’s hands. Full article

Current technologies could potentially solve many of the urban problems in today’s cities. Many cities already possess cameras, drones, thermometers, pollution air gauges, and other sensors. However, most of these have been designated for use in individual domains within City Hall, creating a maze of individual data domains that cannot connect to each other. This jumble of domains and stakeholders prevents collaboration and transparency. Cities need an integrated system in which data and dashboards can be shared by city administrators to better deal with urban problems that involve several sectors and to improve oversight. This paper presents a model of an integrative system to manage classes of problems within one administrative municipal domain. Our model contains the cyber-physical system’s elements: the physical object, the sensors and electronic devices attached to it, a database of collected problems, code running on the devices or remotely, and the human. We tested the model by using it on the recurring problem of potholes in city streets. An AI model for identifying potholes was integrated into applications available to citizens and operators so that they can feed the municipal system with images and the locations of potholes using their cell phone camera. Preliminary results indicate that these sensors can detect potholes with an accuracy of 91% and 99%, depending on the detection equipment used. In addition, the dashboards provide the manager and the citizen with a transparent view of the problems’ progress and support for their correct address. Full article

Natural gas extraction is a critical driver of the economy in western North America. Ecological reclamation is important to ensure surface disturbance impacts associated with natural gas development are not permanent and to assist native biota. Previous studies in semi-arid natural gas fields within Sublette County, Wyoming, USA have shown insects respond favorably to 1–3-year-old well pads undergoing reclamation compared to older successional reference vegetation communities dominated by Wyoming big sagebrush (Artemisia tridentata spp. Wyomingensis). Here, we examined well pads which were initially seed 5, 8, 10, 11, and 12 years prior to our study. We used a free, image-based software called SamplePointv. 1.60 to quantify vegetation on these well pads and adjacent reference areas from cell phone camera photographs. Insects were collected with a sweep net and identified to the family and morphospecies level. Statistical analyses were conducted to compare both vegetation and insect communities between reclamation sites and their paired reference area. We found little statistical difference between vegetation communities across our study but found significantly more insect abundance on reclaimed well pads than reference areas in 3 of 5 years and significantly higher family and morphospecies richness on reclaimed well pads in 4 of 5 years. A total of 2036 individual insects representing 270 species from 71 families across 11 orders were identified across this study. A total of 1557 individuals (76.5%) were found on reclamation sites, whereas 479 (23.5%) were found in reference areas across the entire study. A total of 233 species (86.3% of total) were found on reclamation sites, whereas 121 species (44.8% of total) were found in reference areas across the entire study. A total of 67 families (94.4% of total) were found on reclamation sites, whereas 45 families (63.4% of total) were found in reference areas across the entire study. All 11 orders found in the study were found on reclamation sites, whereas 9 orders were found in reference areas across the entire study. Our results suggest reclamation of natural gas well pads within an old successional stand of sagebrush continues to support higher levels of insect biodiversity and abundance for at least 12 years. As insects are the most diverse group of animals on Earth and because they provide a wide array of ecosystem services, our findings suggest ecological reclamation plays an important role in returning biodiversity and ecosystem functionality to a semi-arid and old successional sagebrush–steppe ecosystem. Full article

One of the fundamental stages in recognizing people by their ears, which most works omit, is locating the area of interest. The sets of images used for experiments generally contain only the ear, which is not appropriate for application in a real environment, where the visual field may contain part of or the entire face, a human body, or objects other than the ear. Therefore, determining the exact area where the ear is located is complicated, mainly in uncontrolled environments. This paper proposes a method for ear localization in controlled and uncontrolled environments using MediaPipe, a tool for face localization, and YOLOv5s architecture for detecting the ear. The proposed method first determines whether there are cues that indicate that a face exists in an image, and then, using the MediaPipe facial mesh, the points where an ear potentially exists are obtained. The extracted points are employed to determine the ear length based on the proportions of the human body proposed by Leonardo Da Vinci. Once the dimensions of the ear are obtained, the delimitation of the area of interest is carried out. If the required elements are not found, the model uses the YOLOv5s architecture module, trained to recognize ears in controlled environments. We employed four datasets for testing (i) In-the-wild Ear Database, (ii) IIT Delhi Ear Database, (iii) AMI Ear Database, and (iv) EarVN1.0. Also, we used images from the Internet and some acquired using a Redmi Note 11 cell phone camera. An accuracy of 97% with an error of 3% was obtained with the proposed method, which is a competitive measure considering that tests were conducted in controlled and uncontrolled environments, unlike state-of-the-art methods. Full article

Precision crop load management of apple requires counting fruiting structures at various times during the year to guide management decisions. The objective of the current study was to evaluate the accuracy of and compare different commercial computer vision systems and computer applications to estimate trunk cross-sectional area (TCSA), flower cluster number, thinning efficacy, and yield estimation. These studies evaluated two companies that offer different vision systems in a series of trials across 23 orchards in four states. Orchard Robotics uses a proprietary camera system, and Pometa (previously Farm Vision) uses a cell phone camera system. The cultivars used in the trials were ‘NY1’, ‘NY2’, ‘Empire’, ‘Granny Smith’, ‘Gala’, ‘Fuji’, and ‘Honeycrisp’. TCSA and flowering were evaluated with the Orchard Robotics camera in full rows. Flowering, fruit set, and yield estimation were evaluated with Pometa. Both systems were compared with manual measurements. Our results showed a positive linear correlation between the TCSA with the Orchard Robotics vision system and manual measurements, but the vision system underestimated the TCSA in comparison with the manual measurements (R²s between 0.5 and 0.79). Both vision systems showed a positive linear correlation between nubers of flowers and manual counts (R²s between 0.5 and 0.95). Thinning efficacy predictions (in June) were evaluated using the fruit growth rate model, by comparing manual measurements and the MaluSim computer app with the computer vision system of Pometa. Both systems showed accurate predictions when the numbers of fruits at harvest were lower than 200 fruit/tree, but our results suggest that, when the numbers of fruits at harvest were higher than 200 fruit/tree, both methods overestimated final fruit numbers per tree when compared with final fruit numbers at harvest (R²s 0.67 with both systems). Yield estimation was evaluated just before harvest (August) with the Pometa system. Yield estimation was accurate when fruit numbers were fewer than 75 fruit per tree, but, when the numbers of fruit at harvest were higher than 75 fruit per tree, the Pometa vision system underestimated the final yield (R² = 0.67). Our results concluded that the Pometa system using a smartphone offered advantages such as low cost, quick access, simple operation, and accurate precision. The Orchard Robotics vision system with an advanced camera system provided more detailed and accurate information in terms of geo-referenced information for individual trees. Both vision systems evaluated are still in early development and have the potential to provide important information for orchard managers to improve crop load management decisions. Full article

Forestry operations have become of great importance for a sustainable environment in the past few decades due to the increasing toll induced by rural abandonment and climate change. Robotics presents a promising solution to this problem; however, gathering the necessary data for developing and testing algorithms can be challenging. This work proposes a portable multi-sensor apparatus to collect relevant data generated by several onboard sensors. The system incorporates Laser Imaging, Detection and Ranging (LiDAR), two stereo depth cameras and a dedicated inertial measurement unit (IMU) to obtain environmental data, which are coupled with an Android app that extracts Global Navigation Satellite System (GNSS) information from a cell phone. Acquired data can then be used for a myriad of perception-based applications, such as localization and mapping, flammable material identification, traversability analysis, path planning and/or semantic segmentation toward (semi-)automated forestry actuation. The modular architecture proposed is built on Robot Operating System (ROS) and Docker to facilitate data collection and the upgradability of the system. We validate the apparatus’ effectiveness in collecting datasets and its flexibility by carrying out a case study for Simultaneous Localization and Mapping (SLAM) in a challenging woodland environment, thus allowing us to compare fundamentally different methods with the multimodal system proposed. Full article

Unnecessary ED visits and transfers to hand clinics raise treatment costs and patient burden at trauma centers. In the present COVID-19 pandemic, needless transfers can increase patients’ risk of viral exposure. Therefore, this review analyzes different aspects of the remote diagnosis and triage of traumatic hand injuries. The most common file was photography, with the most common devices being cell phone cameras. Treatment, triage, diagnosis, cost, and time outcomes were assessed, showing concordance between teleconsultation and face-to-face patient evaluations. We conclude that photography and video consultations are feasible surrogates for ED visits in patients with traumatic hand injuries. These technologies should be leveraged to decrease treatment costs and potentially decrease the time to definitive treatment after initial evaluation. Full article

IoT systems can successfully employ wireless sensor networks (WSNs) for data gathering and fog/edge computing for processing collected data and providing services. The proximity of edge devices to sensors improves latency, whereas cloud assets provide higher computational power when needed. Fog networks include various heterogeneous fog nodes and end-devices, some of which are mobile, such as vehicles, smartwatches, and cell phones, while others are static, such as traffic cameras. Therefore, some nodes in the fog network can be randomly organized, forming a self-organizing ad hoc structure. Moreover, fog nodes can have different resource constraints, such as energy, security, computational power, and latency. Therefore, two major problems arise in fog networks: ensuring optimal service (application) placement and determining the optimal path between the user end-device and the fog node that provides the services. Both problems require a simple and lightweight method that can rapidly identify a good solution using the constrained resources available in the fog nodes. In this paper, a novel two-stage multi-objective path optimization method is proposed that optimizes the data routing path between the end-device and fog node(s). A particle swarm optimization (PSO) method is used to determine the Pareto Frontier of alternative data paths, and then the analytical hierarchy process (AHP) is used to choose the best path alternative according to the application-specific preference matrix. The results show that the proposed method works with a wide range of objective functions that can be easily expanded. Moreover, the proposed method provides a whole set of alternative solutions and evaluates each of them, allowing us to choose the second- or third-best alternative if the first one is not suitable for some reason. Full article

A common laboratory method involves gel electrophoresis followed by photographic documentation of the results, a procedure which is performed worldwide by students and experienced scientists alike. Proprietary Gel Documentation Systems are convenient and useful for documentation of electrophoresis results, but the systems can be prohibitively expensive to purchase and repair, they contain features that are not necessary for everyday documentation, and some users may not find the systems intuitive to operate. We describe our gel documentation setup that meets the everyday needs for documentation in our lab. The setup is inexpensive, modular, user friendly, and increases sustainability through extending the working life of obsolete cell phones, iPads, or other electronic devices containing a camera. More importantly, the setup completely shields users from potentially damaging ultraviolet radiation. Full article

This paper describes a new open data set, consisting of images of a chessboard collected underwater with different refractive indices, which allows for investigation of the quality of different radial distortion correction methods. The refractive index is regulated by the degree of salinity of the water. The collected data set consists of 662 images, and the chessboard cell corners are manually marked for each image (for a total of $35,748$ nodes). Two different mobile phone cameras were used for the shooting: telephoto and wide-angle. With the help of the collected data set, the practical applicability of the formula for correction of the radial distortion that occurs when the camera is submerged underwater was investigated. Our experiments show that the radial distortion correction formula makes it possible to correct images with high precision, comparable to the precision of classical calibration algorithms. We also show that this correction method is resistant to small inaccuracies in the indication of the refractive index of water. The data set, as well as the accompanying code, are publicly available. Full article

In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset. Full article

Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification. Full article

A Global Positioning System (GPS) spoofing attack can be launched against any commercial GPS sensor in order to interfere with its navigation capabilities. These sensors are installed in a variety of devices and vehicles (e.g., cars, planes, cell phones, ships, UAVs, and more). In this study, we focus on micro UAVs (drones) for several reasons: (1) they are small and inexpensive, (2) they rely on a built-in camera, (3) they use GPS sensors, and (4) it is difficult to add external components to micro UAVs. We propose an innovative method, based on the video stream captured by a drone’s camera, for the real-time detection of GPS spoofing attacks targeting drones. The proposed method collects frames from the video stream and their location (GPS coordinates); by calculating the correlation between each frame, our method can detect GPS spoofing attacks on drones. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at altitudes of 50–100 m over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1–4 m (an average of 2.5 m) from the real location. Full article

The fruit fly Drosophila melanogaster is a classic research object in genetics and systems biology. In the genetic analysis of flies, a routine task is to determine the offspring size and gender ratio in their populations. Currently, these estimates are made manually, which is a very time-consuming process. The counting and gender determination of flies can be automated by using image analysis with deep learning neural networks on mobile devices. We proposed an algorithm based on the YOLOv4-tiny network to identify Drosophila flies and determine their gender based on the protocol of taking pictures of insects on a white sheet of paper with a cell phone camera. Three strategies with different types of augmentation were used to train the network. The best performance (F1 = 0.838) was achieved using synthetic images with mosaic generation. Females gender determination is worse than that one of males. Among the factors that most strongly influencing the accuracy of fly gender recognition, the fly’s position on the paper was the most important. Increased light intensity and higher quality of the device cameras have a positive effect on the recognition accuracy. We implement our method in the FlyCounter Android app for mobile devices, which performs all the image processing steps using the device processors only. The time that the YOLOv4-tiny algorithm takes to process one image is less than 4 s. Full article