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Peru is a country located in a telluric area. The early detection of earthquakes will alert the population and avoid human losses. There are different methods to detect it, mainly on mechanical movements and electronic sensors, which are currently used. This article presents the analysis and implementation of a repetitive motion generation and detection system based on the study of the speckle phenomenon through an optical fiber. The analysis is calculated by the technique of averaged difference that allows obtaining the intensity variation of two consecutive frames, as the speckle pattern changes and occupies different positions. Several tests are carried out that show the relationship of the controlled random movement and speckle characteristics obtained, the test system that can be used for the detection of random movements similar to P and S earthquakes waves. [ABSTRACT FROM AUTHOR]

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified. [ABSTRACT FROM AUTHOR]

The risk of information leaks increases as images become a crucial medium for information sharing. There is a great need to further develop the versatility of image encryption technology to protect confidential and sensitive information. Herein, using high spatial redundancy (strong correlation of neighboring pixels) of the image and the in situ encryption function of a quantum dot functionalized encryption camera, in situ image encryption is achieved by designing quantum dot films (size, color, and full width at half maximum) to modify the correlation and reduce spatial redundancy of the captured image during encryption processing. The correlation coefficients of simulated encrypted image closely apporach to 0. High‐quality decrypted images are achieved with a PSNR of more than 35 dB by a convolutional neural network‐based algorithm that meets the resolution requirements of human visual perception. Compared with the traditional image encryption algorithms, chaotic image encryption algorithms and neural network‐based encryption algorithms described previously, it provides a universal, efficient and effective in situ image encryption method. [ABSTRACT FROM AUTHOR]

This article presents a systematic literature review of technologies and solutions for cattle tracking and monitoring based on a comprehensive analysis of scientific articles published since 2017. The main objective of this review is to identify the current state of the art and the trends in this field, as well as to provide a guide for selecting the most suitable solution according to the user's needs and preferences. This review covers various aspects of cattle tracking, such as the devices, sensors, power supply, wireless communication protocols, and software used to collect, process, and visualize the data. The review also compares the advantages and disadvantages of different solutions, such as collars, cameras, and drones, in terms of cost, scalability, precision, and invasiveness. The results show that there is a growing interest and innovation in livestock localization and tracking, with a focus on integrating and adapting various technologies for effective and reliable monitoring in real-world environments. [ABSTRACT FROM AUTHOR]

Objective: Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods: In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results: Compared with electrocardiography, the present work achieves limits of agreement of [−2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [−0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions: These results showcased Speckle Vibrometry's potential in vital sign monitoring in a clinical setting. Significance: This is the first human clinical study for Speckle Vibrometry. [ABSTRACT FROM AUTHOR]

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Two major perenial disasters are prevalent in Indonesia, namely, peatland fires and volcanic activities associated with Andesite rock. Thus, the Indonesian Government has prioritized the prevention and mitigation of both disasters. Indonesia’s Research Center for Satellite Technology-National Research and Innovation Agency then implemented the program as a satellite payload project. In this study, we describe the design of a space-based mid-wavelength infrared (SMWIR) camera module to monitor peatland fires and volcanic activities associated with Andesite rock. Using the spectral range as the basis of design and the iteration process of general steps in designing a camera, a SMWIR camera module was successfully designed. First, the spectral range was obtained by an intersection of four methods of determining spectral bands. Subsequently, the optical section, was conducted using Zemax by applying three criteria to analyze the optical performance, such as the spot diagram, encircled energy, and modulation transfer function (MTF). Thereafter, the mechanical design was achieved through the SOLIDWORKS software. The fourth step, namely, the structure or thermal design, was achieved by both Thermal Desktop/SINDA FLUINT and Zemax. In the electronic section, both the camera and detector were developed. Finally, a calibration system was specified over the module. Results in the form of graphs, pictures, and tables indicate that all established conditions, including those of the technical side, were achieved. Therefore, high performance in terms of the image, durability, transmission, and thermal stability can easily be achieved; additionally, the module is feasible, lightweight, and compact.

The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments’ ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps.

Introduction: An image has the capability to describe things better than words. These lines are applicable, especially in the case of dermatological photography (DP). However, a good image can be obtained if one has good photographic guiding principles. In the present article, we report on the development of standard guidelines for DP. Development of dermatological photographic guidelines with consistency and uniformity. To ensure the best possible dermatological photographs which visualize the finest skin lesion of the patient from various distances. Methods: Guidelines were prepared for each and every aspect of photography such as standardization of instrumentation, camera, lighting condition, and distance of skin lesion (which is variable in size). In setup, Nikon D 3300 Camera was used to take photographs of skin lesions, which is one of the high generations digital single lens reflex camera. Guidelines for lighting conditions were prepared using the LUX meter, by achieving a constant lighting condition. For the distance, an artificial skin lesions model of various sizes from 2 mm to 20 cm was made. Standard guidelines were prepared so that the best possible image was captured. Result and Conclusion: Dermatological photography guidelines with consistency and uniformity made diagnosis easier for further reliability and validation purposes in the study. The best possible quality photographs have been achieved through the above said guidelines. Standard guidelines which were prepared for dermatological photography was found appropriate for various purposes such as skin disease diagnosis, and education.

Abstract Subway button detection is paramount for passenger safety, yet the occurrence of inadvertent touches poses operational threats. Camera-based detection is indispensable for identifying touch occurrences, ascertaining person identity, and implementing scientific measures. Existing methods suffer from inaccuracies due to the small size of buttons, complex environments, and challenges such as occlusion. We present YOLOv8-DETR-P2-DCNv2-Dynamic-NWD-DA, which enhances occlusion awareness, reduces redundant annotations, and improves contextual feature extraction. The model integrates the RTDETRDecoder, P2 small target detection layer, DCNv2-Dynamic algorithm, and the NWD loss function for multiscale feature extraction. Dataset augmentation and the GAN algorithm refine the model, aligning feature distributions and enhancing precision by 6.5%, 5%, and 5.8% in precision, recall, and mAP50, respectively. These advancements denote significant improvements in key performance indicators.

Among materials produced as colloidal quantum dots (CQDs), HgTe has a special status being the only material covering the whole infrared range from the visible to the THz (0.7–100 µm). This unique property resulting from its electronic structure, combined with an air stability and a capacity for charge conduction has generated consistent and massive efforts to produce and improve HgTe CQDs over the past two decades. Meanwhile, HgTe CQDs offer an infrared platform more advanced than any other colloidal alternatives in the mid‐wave infrared regarding their integration into advanced photonic and optoelectronic applications. Here, the latest developments of HgTe CQDs relative to the material's growth, electron structure modelling, its integration into photonic structures and its transfer as the active material from single element devices toward complex sensors and infrared imagers are reviewed. Finally, a discussion about the potential of this material for industry, rising new challenges beyond economical and production considerations at low technological readiness level, relative to the material and device design, is also included. [ABSTRACT FROM AUTHOR]

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class's easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture. [ABSTRACT FROM AUTHOR]

Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods. [ABSTRACT FROM AUTHOR]

Selecting uniform and healthy seedlings is important to ensure that a certain level of production can be reliably achieved in a plant factory. The objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce and to determine the feasibility of using a simple image analysis to select robust seedlings that can produce a uniform and dependable yield of lettuce in a plant factory. To vary the range of the leaf area and shoot fresh weight of lettuce seedlings, we applied two- and three-day irrigation intervals during the period of seedling production and calculated the projected canopy size (PCS) from the top-view images of the lettuce seedlings, although there were no significant growth differences between the irrigation regimes. A high correlation was identified between the PCS and shoot fresh weight for the lettuce seedlings during the period of seedling production, with a coefficient of determination exceeding 0.8. Therefore, the lettuce seedlings were classified into four grades (A–D) based on their PCS values calculated at transplanting. In the early stages of cultivation after transplanting, there were differences in the lettuce growth among the four grades; however, at the harvest (28 days after transplanting), there was no significant difference in the lettuce yield between grades A–C, with the exception of grade D. The lettuce seedlings in grades A–C exhibited the anticipated yield (150 g/plant) at the harvest time. In the correlation between the PCS and leaf area or the shoot fresh weight of lettuce during the cultivation period after transplanting and the entire cultivation period, the R2 values were higher than 0.9, confirming that PCS can be used to predict lettuce growth with greater accuracy. In conclusion, we demonstrated that the PCS calculation from the top-view images, a straightforward image analysis technique, can be employed to non-destructively and accurately predict lettuce leaf area and shoot fresh weight, and the seedlings with the potential to yield above a certain level after transplanting can be objectively and accurately selected based on PCS. [ABSTRACT FROM AUTHOR]

Subway button detection is paramount for passenger safety, yet the occurrence of inadvertent touches poses operational threats. Camera-based detection is indispensable for identifying touch occurrences, ascertaining person identity, and implementing scientific measures. Existing methods suffer from inaccuracies due to the small size of buttons, complex environments, and challenges such as occlusion. We present YOLOv8-DETR-P2-DCNv2-Dynamic-NWD-DA, which enhances occlusion awareness, reduces redundant annotations, and improves contextual feature extraction. The model integrates the RTDETRDecoder, P2 small target detection layer, DCNv2-Dynamic algorithm, and the NWD loss function for multiscale feature extraction. Dataset augmentation and the GAN algorithm refine the model, aligning feature distributions and enhancing precision by 6.5%, 5%, and 5.8% in precision, recall, and mAP50, respectively. These advancements denote significant improvements in key performance indicators. [ABSTRACT FROM AUTHOR]

In almost all cases, forest inventories require the measurement of diameter at breast height (DBH) and tree height, which are essential for the quantitative study of trees and stand structure, as well as for estimating forest stand parameters. Taking into account the digitization trend, this article aims to address the possibility of using the Measure app, a free and common application integrated into the new smartphone models that use the iOS operating system, to measure DBH, by highlighting the impact of diameter measurement errors on tree- and stand-level basal area. To achieve our objectives, two rectangular plots of 1 ha each were established in an uneven-aged mixed forest from Eastern Carpathians. DBH on two orthogonal directions was measured for 888 trees using a caliper and the Measure app built-in operating system iOS 14 (Apple Inc., Cupertino, California, USA), and girth at the level of 1.30 m was measured using a tape. Stand basal area was also estimated by point sampling running 120 horizontal point samples with various basal area factors. The mean error (MEDBH), the absolute mean error (AMEDBH), and the root mean squared error (RMSEDBH) in the breast height diameters were calculated by species. DBH differences were tested using the paired samples t test and the related-samples Wilcoxon signed rank test. Furthermore, the correlation coefficient (R) and multiple R were computed by species to identify the relation between basal area error and various tree characteristics. Our study showed that there was a tendency to underestimate diameters when the app was used, and thus mean DBH was underestimated by 0.45–0.89 cm in the case of the conifers and by 0.17–0.71 cm in the case of the broadleaves. By using the iPhone Measure app, AMEDBH varied from 0.96 to 1.51 cm, while RMSEDBH varied between 1.15 and 1.92 cm. In the cases of the sessile oak and sycamore trees, no significant differences were found between DBH measured by caliper and by app, while for the other species, the differences were significant. Therefore, the ± 5% frequency of individual errors when estimating basal area was reduced by approximately half in the case of using the app as opposed to the caliper, and the risk of recording errors > 10% in tree basal area estimation was 26 times higher. For the most part, basal area error at tree level was determined by the error in DBH measurement, but tree size and the ovality of the cross-sections also contributed to a smaller degree. The tendency to underestimate DBH when the iPhone Measure app was used resulted in underestimating stand basal area by approximately 4.1%. Considering the DBH derived from girth measurement as reference, stand basal area was estimated by the iPhone Measure app with a difference of -6.50%. In contrast, point sampling produced better results using BAF = 4 m2ha− 1. Despite the fact that the iPhone Measure app is a convenient digital measurement tool, it still requires upgrades of certain details to achieve the accuracy of the caliper, so that in the near future this type of application can be used on a wider scale to collect field data related to tree diameter and height, as well as to estimate stand basal area. [ABSTRACT FROM AUTHOR]

Traffic accidents are a significant global problem, with human error being a major contributing factor. Autonomous vehicles have emerged as a promising solution to address this issue. Vehicle pedestrians understand the environment through sensor is crucial in this field because sensor data can be used by vehicle to drive safely such as navigate the route, avoid collision with other object such as pedestrian, car, building, animals, and many more. This paper proposed a comparison of 3D object detection models for autonomous vehicles using a study literature review technique. This technique consists of ten steps from including defining research question to produce the result. As a result, 9 papers were retrieved from 90 papers and then all object detection models are grouped into three parts to determine the best model based on dataset that being used such as KITTI, nuScenes, and Waymo Open Dataset. [ABSTRACT FROM AUTHOR]

Police departments around the world implement algorithmic systems to enhance various policing tasks. Ensuring such innovations take place responsibly – with public values upheld – is essential for public organisations. This paper analyses how public values are safeguarded in the case of MONOcam, an algorithmic camera system designed and used by the Netherlands police. The system employs artificial intelligence to detect whether car drivers are holding a mobile device. MONOcam can be considered a good example of value-sensitive design; many measures were taken to safeguard public values in this algorithmic system. In pursuit of responsible implementation of algorithms, most calls and literature focus on such value-sensitive design. Less attention is paid to what happens beyond design. Building on 120+ hours of ethnographic observations as well as informal conversations and three semi-structured interviews, this research shows that public values deemed safeguarded in design are re-negotiated as the system is implemented and used in practice. These findings led to direct impact, as MONOcam was improved in response. This paper thus highlights that algorithmic system design is often based on an ideal world, but it is in the complexities and fuzzy realities of everyday professional routines and sociomaterial reality that these systems are enacted, and public values are renegotiated in the use of algorithms. While value-sensitive design is important, this paper shows that it offers no guarantees for safeguarding public values in practice. [ABSTRACT FROM AUTHOR]

In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made 3D LiDAR sensors more compact, lightweight, and affordable. This progress has spurred interest in integrating LiDAR with sensors such as Inertial Measurement Units (IMUs) and cameras for Simultaneous Localization and Mapping (SLAM) research. Public datasets covering different scenarios, platforms, and viewpoints are crucial for multi-sensor fusion SLAM studies, yet most focus on handheld or vehicle-mounted devices with front or 360-degree views. Data from aerial vehicles with downward-looking views is scarce, existing relevant datasets usually feature low altitudes and are mostly limited to small campus environments. To fill this gap, we introduce the Multi-sensor Aerial Robots SLAM dataset (MARS-LVIG dataset), providing unique aerial downward-looking LiDAR-Visual-Inertial-GNSS data with viewpoints from altitudes between 80 m and 130 m. The dataset not only offers new aspects to test and evaluate existing SLAM algorithms, but also brings new challenges which can facilitate researches and developments of more advanced SLAM algorithms. The MARS-LVIG dataset contains 21 sequences, acquired across diversified large-area environments including an aero-model airfield, an island, a rural town, and a valley. Within these sequences, the UAV has speeds varying from 3 m/s to 12 m/s, a scanning area reaching up to 577,000 m2, and the max path length of 7.148 km in a single flight. This dataset encapsulates data collected by a lightweight, hardware-synchronized sensor package that includes a solid-state 3D LiDAR, a global-shutter RGB camera, IMUs, and a raw message receiver of the Global Navigation Satellite System (GNSS). For algorithm evaluation, this dataset releases ground truth of both localization and mapping, which are acquired by on-board Real-time Kinematic (RTK) and DJI L1 (post-processed by its supporting software DJI Terra), respectively. The dataset can be downloaded from: https://mars.hku.hk/dataset.html. [ABSTRACT FROM AUTHOR]

In late 2019/early 2020, the global outbreak of the COVID-19 coronavirus has resulted in the largest medical emergency in history. This necessitated the need to develop a robot to minimize the contact of medical personnel with quarantined patients to reduce the infection of medical personnel. For quarantined patients, we created an automatic robotic assistance system, namely, a robot was developed to transport medication to quarantined patients. The stages of model development are shown in the paper. The architecture of the robot and the control system is shown. A description of the equipment used, actuators, sensors, as well as the scheme of electrical connection of the system is given. This scheme will allow medical personnel to serve the largest number of patients in the minimum time. In addition, it will create a social distance to pay more attention to patients with severe diseases. [ABSTRACT FROM AUTHOR]

Abstract The risk of information leaks increases as images become a crucial medium for information sharing. There is a great need to further develop the versatility of image encryption technology to protect confidential and sensitive information. Herein, using high spatial redundancy (strong correlation of neighboring pixels) of the image and the in situ encryption function of a quantum dot functionalized encryption camera, in situ image encryption is achieved by designing quantum dot films (size, color, and full width at half maximum) to modify the correlation and reduce spatial redundancy of the captured image during encryption processing. The correlation coefficients of simulated encrypted image closely apporach to 0. High‐quality decrypted images are achieved with a PSNR of more than 35 dB by a convolutional neural network‐based algorithm that meets the resolution requirements of human visual perception. Compared with the traditional image encryption algorithms, chaotic image encryption algorithms and neural network‐based encryption algorithms described previously, it provides a universal, efficient and effective in situ image encryption method.