Search Results (1,113)

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. Full article

Agroforestry systems can influence the occurrence and abundance of pests and diseases because integrating crops with trees or other vegetation can create diverse microclimates that may either enhance or inhibit their development. This study analyzes the severity of coffee rust in two agroforestry systems in the provinces of Jaén and San Ignacio in the department of Cajamarca (Peru). This research used a quantitative descriptive approach, and 319 photographs were collected with a professional camera during field trips. The photographs were segmented, classified and analyzed using the deep learning MobileNet and VGG16 transfer learning models with two methods for measuring rust severity from SENASA Peru and SENASICA Mexico. The results reported that grade 1 is the most prevalent rust severity according to the SENASA methodology (1 to 5% of the leaf affected) and SENASICA Mexico (0 to 2% of the leaf affected). Moreover, the proposed MobileNet model presented the best classification accuracy rate of 94% over 50 epochs. This research demonstrates the capacity of machine learning algorithms in disease diagnosis, which could be an alternative to help experts quantify the severity of coffee rust in coffee trees and broadens the field of research for future low-cost computational tools for disease recognition and classification Full article

With the widespread deployment of video surveillance devices, a large number of indoor and outdoor places are under the coverage of cameras, which plays a significant role in enhancing regional safety management and hazard detection. However, a vast number of cameras lead to high installation, maintenance, and analysis costs. At the same time, low-quality images and potential blind spots in key areas prevent the full utilization of the video system’s effectiveness. This paper proposes an optimization method for video surveillance system deployment based on space syntax analysis and deep reinforcement learning. First, space syntax is used to calculate the connectivity value, control value, depth value, and integration of the surveillance area. Combined with visibility and axial analysis results, a weighted index grid map of the area’s surveillance importance is constructed. This index describes the importance of video coverage at a given point in the area. Based on this index map, a deep reinforcement learning network based on DQN (Deep Q-Network) is proposed to optimize the best placement positions and angles for a given number of cameras in the area. Experiments show that the proposed framework, integrating space syntax and deep reinforcement learning, effectively improves video system coverage efficiency and allows for quick adjustment and refinement of camera placement by manually setting parameters for specific areas. Compared to existing coverage-first or experience-based optimization, the proposed method demonstrates significant performance and efficiency advantages. Full article

This paper presents methods for a UAV-based survey of the site “Kuklica” near Kratovo, North Macedonia. Kuklica is a rare natural complex with earth pyramids, and because of its exceptional scientific, educational, touristic, and cultural significance, it was proclaimed to be a Natural Monument in 2008. However, after the proclamation, the interest in visiting this site and the threats in terms of its potential degradation rapidly grew, increasing the need for a detailed survey of the site and monitoring. Given the site’s small size (0.5 km²), the freely available satellite images and digital elevation models are not suitable for comprehensive analysis and monitoring of the site, especially in terms of the individual forms within the site. Instead, new tools are increasingly being used for such tasks, including UAVs (unmanned aerial vehicles) and LiDAR (Light Detection and Ranging). Since professional LiDAR is very expensive and still not readily available, we used a low-cost UAV (DJI Mini 4 Pro) to carry out a detailed survey. First, the flight path, the altitude of the UAV, the camera angle, and the photo recording intervals were precisely planned and defined. Also, the ground markers (checkpoints) were carefully selected. Then, the photos taken by the drone were aligned and processed using Agisoft Metashape software (v. 2.1.4), producing a digital elevation model and orthophoto imagery with a very high (sub-decimeter) resolution. Following this procedure, more than 140 earth pyramids were delineated, ranging in height from 1 to 2 m and to 30 m at their highest. At this stage, a very accurate UAV-based 3D model of the most remarkable earth pyramids was developed (the accuracy was checked using the iPhone 14 Pro LiDAR module), and their morphometrical properties were calculated. Also, the site’s erosion rate and flash flood potential were calculated, showing high susceptibility to both. The final goal was to monitor the changes and to minimize the degradation of the unique landscape, thus better protecting the geosite and its value. Full article

Novel low-cost portable spectrophotometers could be an alternative to traditional spectrophotometers and calibrated RGB cameras by offering lower prices and convenient measurements but retaining high colorimetric accuracy. This study evaluated the colorimetric accuracy of low-cost, portable spectrophotometers on the established color calibration target—RAL Design System Plus (RAL+). Four spectrophotometers with a listed price between USD 100–1200 (Nix Spectro 2, Spectro 1 Pro, ColorReader, and Pico) and a smartphone RGB camera were tested on a representative subset of 183 RAL+ colors. Key performance metrics included the devices’ ability to match and measure RAL+ colors in the CIELAB color space using the color difference CIEDE2000 ΔE. The results showed that Nix Spectro 2 had the best performance, matching 99% of RAL+ colors with an estimated ΔE of 0.5–1.05. Spectro 1 Pro and ColorReader matched approximately 85% of colors with ΔE values between 1.07 and 1.39, while Pico and the Asus 8 smartphone matched 54–77% of colors, with ΔE of around 1.85. Our findings showed that low-cost, portable spectrophotometers offered excellent colorimetric measurements. They mostly outperformed existing RGB camera-based colorimetric systems, making them valuable tools in science and industry. Full article

Phytopathogens pose a serious threat to agriculture, causing a decrease in yield and product quality. This necessitates the development of methods for early detection of phytopathogens, which will reduce losses and improve product quality by using lower quantities of agrochemicals. In this study, the efficiency of spectral imaging in the early detection and differentiation of diseases caused by pathogens of different types (Potato virus X (PVX) and the bacterium Pseudomonas syringae) was analyzed. An evaluation of the visual symptoms of diseases demonstrated the presence of pronounced symptoms in the case of bacterial infection and an almost complete absence of visual symptoms in the case of viral infection. P. syringae caused severe inhibition of photosynthetic activity in the infected leaf, while PVX did not have a pronounced effect on photosynthetic activity. Reflectance spectra of infected and healthy plants were detected in the range from 400 to 1000 nm using a hyperspectral camera, and the dynamics of infection-induced changes during disease progression were analyzed. P. syringae caused a strong increase in reflectance in the blue and red spectral ranges, as well as a decrease in the near-infrared range. PVX-induced changes in the reflectance spectrum had smaller amplitudes compared to P. syringae, and were localized mainly in the red edge (RE) range. The entire set of normalized reflectance indices (NRI) for the analyzed spectral range was calculated. The most sensitive NRIs to bacterial (NRI_510/545, NRI_510/850) and viral (NRI_600/850, NRI_700/850) infections were identified. The use of these indices makes it possible to detect the disease at an early stage. The study of the identified NRIs demonstrated the possibility of using the multispectral imaging method in early pathogen detection, which has high performance and a low cost of analysis. Full article

The cost of indoor mapping methods based on three-dimensional (3D) LiDAR can be relatively high, and they lack environmental color information, thereby limiting their application scenarios. This study presents an innovative, low-cost, omnidirectional 3D color LiDAR mapping system for indoor environments. The system consists of two two-dimensional (2D) LiDARs, six monocular cameras, and a servo motor. The point clouds are fused with imagery using a pixel-spatial dual-constrained depth gradient adaptive regularization (PS-DGAR) algorithm to produce dense 3D color point clouds. During fusion, the point cloud is reconstructed inversely based on the predicted pixel depth values, compensating for areas of sparse spatial features. For indoor scene reconstruction, a globally consistent alignment algorithm based on particle filter and iterative closest point (PF-ICP) is proposed, which incorporates adjacent frame registration and global pose optimization to reduce mapping errors. Experimental results demonstrate that the proposed density enhancement method achieves an average error of 1.5 cm, significantly improving the density and geometric integrity of sparse point clouds. The registration algorithm achieves a root mean square error (RMSE) of 0.0217 and a runtime of less than 4 s, both of which outperform traditional iterative closest point (ICP) variants. Furthermore, the proposed low-cost omnidirectional 3D color LiDAR mapping system demonstrates superior measurement accuracy in indoor environments. Full article

Avalanches present substantial hazard risk in mountainous regions, particularly when avalanches obstruct roads, either hitting vehicles directly or leaving traffic exposed to subsequent avalanches during cycles. Traditional detection methods often are designed to cover only a limited section of a road stretch, hampering effective risk management. This research introduces a novel approach using Distributed Acoustic Sensing (DAS) for avalanche detection. The monitoring site in Northern Norway is known to be frequently impacted by avalanches. Between 2022–2024, we continuously monitored the road for avalanches blocking the traffic. The automated alert system identifies avalanches affecting the road and estimates accumulated snow. The system provides continuous, real-time monitoring with competitive sensitivity and accuracy over large areas (up to 170 km) and for multiple sites on parallel. DAS powered alert system can work unaffected by visual barriers or adverse weather conditions. The system successfully identified 10 road-impacting avalanches (100% detection rate). Our results via DAS align with the previous works and indicate that low frequency part of the signal (<20 Hz) is crucial for detection and size estimation of avalanche events. Alternative fiber installation methods are evaluated for optimal sensitivity to avalanches. Consequently, this study demonstrates its durability and lower maintenance requirements, especially when compared to the high setup costs and coverage limitations of radar systems, or the weather and lighting vulnerabilities of cameras. Furthermore the system can detect vehicles on the road as important supplemental information for search and rescue operations, and thus the authorities can be alerted, thereby playing a vital role in urgent rescue efforts. Full article

Color consistency when reading colorimetric sensors is a key factor for this technology. Here, we demonstrate how the usage of machine-readable patterns, like QR codes, can be used to solve the problem. We present our approach of using back-compatible color QR codes as colorimetric sensors, which are common QR codes that also embed a set of hundreds of color references as well as colorimetric indicators. The method allows locating the colorimetric sensor within the captured scene and to perform automated color correction to ensure color consistency regardless of the hardware used. To demonstrate it, a $C{O}_2$-sensitive colorimetric indicator was printed on top of a paper-based substrate using screen printing. This indicator was formulated for Modified Atmosphere Packaging (MAP) applications. To verify the method, the sensors were exposed to several environmental conditions (both in gas composition and light conditions). And, images were captured with an 8M pixel digital camera sensor, similar to those used in smartphones. Our results show that the sensors have a relative error of 9% when exposed with a $C{O}_2$ concentration of 20%. This is a good result for low-cost disposable sensors that are not intended for permanent use. However, as soon as light conditions change (2500–6500 K), this error increases up to ${\mathsf{ϵ}}_{\mathbf{20}}$ = 440% (rel. error at 20% $C{O}_2$ concentration) rendering the sensors unusable. Within this work, we demonstrate that our color QR codes can reduce the relative error to ${\mathsf{ϵ}}_{\mathbf{20}}$ = 14%. Furthermore, we show that the most common color correction, white balance, is not sufficient to address the color consistency issue, resulting in a relative error of ${\mathsf{ϵ}}_{\mathbf{20}}$ = 90%. Full article

Although mass is one of the most relevant process variables, industries may lack an inline monitoring of mass, which has a high cost in some cases. Due to their availability in sorting processes, cameras have potential as a low-cost alternative for the estimation of mass in recycling applications. Nevertheless, further research is needed to transform image information into mass. This work tackles this challenge by proposing a novel method of converting image information into mass of particles, complementing size measures with intensity and texture features extracted from the whole picture. Models were adjusted, employing machine learning techniques, using an industrial waste sample of post-consumer plastic film. The visual properties showed a dependency on mass labels, and the models achieved an error of 9 g for subsamples between 2 and 82 g. The analysis and validation of this image processing method provide a new alternative for the estimation of particle mass. Full article

We report a workflow and a software description for digital image colorimetry aimed at obtaining a quantitative dose–response curve and the minimal inhibitory concentration in the Resazurin Microtiter Assay (REMA) test of the activity of antimycobacterial drugs. The principle of this analysis is based on the newly established correspondence between the intensity of the ${\mathrm{a}}^{*}$ channel of the CIE L*a*b* colour space and the concentration of resorufin produced in the course of this test. The whole procedure can be carried out using free software. It has sufficiently mild requirements for the quality of colour images, which can be taken by a typical smartphone camera. Thus, the approach does not impose additional costs on the medical examination points and is widely accessible. Its efficiency is verified by applying it to the case of two representatives of substituted 2-(quinolin-4-yl) imidazolines. The direct comparison with the data on the indicator’s fluorescence obtained using a commercial microplate reader argues that the proposed approach provides results of the same range of accuracy on the quantitative level. As a result, it would be possible to apply the strategy not only for new low-cost studies but also for expanding databases on drug candidates by quantitatively reprocessing existing data, which were earlier documented by images of microplates but analysed only qualitatively. Full article

Cameras, with their low cost and efficiency, are widely used in construction management and structural health monitoring. However, existing reviews on camera sensor placement (CSP) are outdated due to rapid technological advancements. Furthermore, the construction industry poses unique challenges for CSP implementation due to its scale, complexity, and dynamic nature. Previous reviews have not specifically addressed these industry-specific demands. This study aims to fill this gap by analyzing articles from the Web of Science and ASCE databases that focus exclusively on CSP in construction. A rigorous selection process ensures the relevance and quality of the included studies. This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. Simultaneously, Building Information Modeling is employed to consider the progress of construction and visualize optimized layouts, improving the effect of CSP. This paper delves into perspective distortion, the field of view considerations, and the occlusion impacts, proposing a unified framework that bridges practical execution with the theory of optimal CSP. Furthermore, the roadmap for future exploration in the CSP of construction is proposed. This work enriches the study of construction CSP, charting a course for future inquiry, and emphasizes the need for adaptable and technologically congruent CSP approaches amid evolving application landscapes. Full article

Over the last ten years, the need for high-resolution time-domain digital signal production has grown exponentially. More than ever, applications call for a digital-to-time converter (DTC) that is extremely accurate and precise. Skew compensation and camera shutter operation represent just a few examples of such applications. The advantages of adopting a flexible and rapid time-to-market strategy focused on fast prototyping using programmable logic devices—such as field-programmable gate arrays (FPGAs) and system-on-chip (SoC)—have become increasingly evident. These benefits outweigh those of performance-focused yet expensive application-specific integrated circuits (ASICs). Despite the availability of various architectures, the high non-recurring engineering (NRE) costs make them unsuitable for low-volume production, especially in research or prototyping environments. To address this trend, we introduce an innovative DTC IP-Core with a resolution, also known as least significant bit (LSB), of 52 ps, compatible with all Xilinx 7-Series FPGAs and SoCs. Measurements have been performed on a low-end Artix-7 XC7A100TFTG256-2, guaranteeing a jitter lower than 50 ps r.m.s. and offering a high dynamic range up to 56 ms. With resource utilization below 1% and a dynamic power dissipation of 285 mW for our target FPGA, the design maintains excellent differential and integral nonlinearity errors (DNL/INL) of 1.19 LSB and 1.56 LSB, respectively. Full article

Gas leakage detection is a critical concern in both industrial and residential settings, where real-time systems are essential for quickly identifying potential hazards and preventing dangerous incidents. Traditional detection systems often rely on centralized data processing, which can lead to delays and scalability issues. To overcome these limitations, in this study, we present a solution based on tiny machine learning (TinyML) to process data directly on devices. TinyML has the potential to execute machine learning algorithms locally, in real time, and using tiny devices, such as microcontrollers, ensuring faster and more efficient responses to potential dangers. Our approach combines an MLX90640 thermal camera with two optimized convolutional neural networks (CNNs), MobileNetV1 and EfficientNet-B0, deployed on the Arduino Nano 33 BLE Sense. The results show that our system not only provides real-time analytics but does so with high accuracy—88.92% for MobileNetV1 and 91.73% for EfficientNet-B0—while achieving inference times of 1414 milliseconds and using just 124.8 KB of memory. Compared to existing solutions, our edge-based system overcomes common challenges related to latency and scalability, making it a reliable, fast, and efficient option. This work demonstrates the potential for low-cost, scalable gas detection systems that can be deployed widely to enhance safety in various environments. By integrating cutting-edge machine learning models with affordable IoT devices, we aim to make safety more accessible, regardless of financial limitations, and pave the way for further innovation in environmental monitoring solutions. Full article

One method for the colorimetric detection of hydrogen peroxide vapor is based on a titanium–hydrogen peroxide complex. A color changing material based on a titania hydroxypropyl cellulose thin film was initially developed. However, as this material dries, the sensitivity of the material is significantly reduced. Thus, an alternative sensing material, based on titanium(IV) oxysulfate, an ionic liquid, and in some cases, triflouromethanesulfonic acid adsorbed onto low-cost silicon thin-layer chromatography (TLC) plates, was developed. TiO₂ was heated with concentrated sulfuric acid in a controlled environment, usually at temperatures ranging from 100 °C to 250 °C. These sensors are disposable and single-use and are simple and inexpensive. When the resulting thin-film sensors are exposed to ppm levels of hydrogen peroxide vapor, they turn from a white reflective material to an intense yellow or orange. Ti(IV) oxysulfate combined with an acid catalyst and an ionic-liquid-based material provides an opportunity to enhance the sensor activity towards the peroxide vapor and decreases the detection limit. Kinetic measurements were made by the quantification of the intensity of the reflected light as a function of the exposure time from the sensor in a special cell using a low-cost web camera and a tungsten lamp. The measured rate of the color change indicates high sensitivity and first-order kinetics over a hydrogen peroxide concentration range of approximately 2 to 31 ppm. These new materials are a starting point for the preparation of more active sensor materials for hydrogen peroxide and organic peroxide vapor detection. Full article