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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]

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.

The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR–camera's external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods. [ABSTRACT FROM AUTHOR]

Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms' steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development. [ABSTRACT FROM AUTHOR]

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs' localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios. [ABSTRACT FROM AUTHOR]

With the increasing use of smart homes and IoT devices, security has become a significant concern. This paper presents a method to enhance smart home security using Deep Convolutional Neural Networks (DCNN) and multiple cameras. In this approach, three cameras are used to capture images from different angles, and these images are analysed using DCNNs including VGG16, VGG19 and DenseNet to detect potential intruders. Known for their excellent performance in image classification, these DCNN models aim to improve the accuracy and efficiency of the security system, thereby reducing false alarms and missed detections. Additionally, the system allows authorized individuals to remotely disable the security system, increasing convenience and usability. The proposed method has shown significant improvement in human presence detection and facial recognition, achieving 99.79% accuracy in classifying home occupants and intruders. This performance is superior to alternative models such as SVM, KNN, and complex decision trees. This paper introduces a new method that integrates multiple cameras with DCNN to boost the performance of security systems. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is known on the subject?: Nurses' observation of patients in seclusion is essential to ensure patient safety.Patient observation in seclusion assists nurses in adhering to the requirements of mental health legislation and hospital policy.Direct observation and video monitoring are widely used in observing patients in seclusion.Coercive practices may cause distress to patient‐staff relations. What the paper adds to existing knowledge?: We add detailed information on specific observation methods in seclusion and compare them from the perspective of patients.Nurses communicating with patients ensures relational contact and that quality care is provided to patients even in the most distressed phase of their illness.Providing prior information to patients on observation methods in seclusion and the need for engaging patients in meaningful activities, while in seclusion are emphasized.Observation via camera and nurses' presence near the seclusion room made patients feel safe and gave a sense of being cared for in seclusion.Pixellating the video camera would give a sense of privacy and dignity. What are the implications for practice?: The overarching goal is to prevent seclusion. However, when seclusion is used as a last resort to manage risk to others, it should be done in ways that recognize the human rights of the patient, in ways that are least harmful, and in ways that recognize and cater to patients' unique needs.A consistent approach to relational contact and communication is essential. A care plan must include patient's preferred approach for interacting while in seclusion to support individualized care provision.Viewing panels (small window on the seclusion door) are important in establishing two‐way communication with the patient. Educating nurses to utilize them correctly helps stimulate relational contact and communication during seclusion to benefit patients.Engaging patients in meaningful activities when in seclusion is essential to keep them connected to the outside world. Depending on the patient's presentation in the seclusion room and their preferences for interactions, reading newspapers, poems, stories, or a book chapter aloud to patients, via the viewing panel could help ensure such connectedness.More focus should be placed on providing communication training to nurses to strengthen their communication skills in caring for individuals in challenging care situations.Patient education is paramount. Providing prior information to patients using a co‐produced information leaflet might reduce their anxiety and make them feel safe in the room.When using cameras in the seclusion room, these should be pixelated to maintain patients' privacy. Introduction: A lack of research investigating the specific role that various observational techniques may have in shaping the therapeutic relations in mental health care during seclusion warranted this study. Aim: The aim of the study was to explore patients' experience of different methods of observation used while the patient was in seclusion. Method: A retrospective phenomenological approach, using semi‐structured interviews, ten patients' experiences of being observed in the seclusion room was investigated. Colaizzi's descriptive phenomenological method was followed to analyse the data. Results: Communicating and engaging patients in meaningful activities can be achieved via the viewing panel. The camera was considered essential in monitoring behaviour and promoting a sense of safety. Pixelating the camera may transform patient view on privacy in seclusion. Discussion: The mental health services must strive to prevent seclusion and every effort should be made to recognise the human rights of the patient. The study reveals numerous advantages when nurses actively engage in patient communication during the process of observation. Implications for Practice: Different observation methods yield different benefits; therefore, staff education in using these methods is paramount. Empowering the patient with prior information on seclusion, engaging them in meaningful activities and proper documentation on patient engagement, supports the provision of individualised care in seclusion. [ABSTRACT FROM AUTHOR]

The increasing demand for laparoscopic surgery due to its cosmetic benefits and rapid post-surgery recovery is juxtaposed with a shortage of surgical support staff. This juxtaposition highlights the necessity for improved camera management in laparoscopic procedures, encompassing positioning, zooming, and focusing. Our feasibility study introduces the information and communications technology (ICT) laparoscopy system designed to aid solo laparoscopic surgery. This system tracks a surgeon's body motion using a controller, manipulating an embedded camera to focus on specific surgical areas. It comprises a camera module, a camera movement controller, and a motor within the main body, operating connected wires according to controller commands for camera movement. Surgeon movements are detected by an inertial measurement unit (IMU) sensor, facilitating precise camera control. Additional features include a foot pedal switch for motion tracking, a dedicated trocar for main body stability, and a display module. The system's effectiveness was evaluated using an abdomen phantom model and animal experimentation with a porcine model. The camera responded to human movement within 100 ms, a delay that does not significantly affect procedural performance. The ICT laparoscopy system with advanced motion-tracking technology is a promising tool for solo laparoscopic surgery, potentially improving surgical outcomes and overcoming staff shortages. [ABSTRACT FROM AUTHOR]

Apart from the existing mobile applications that can be downloaded separately from the Play Store, the Mccnians Assist: An All-in-one Student App Organizer for College is proposed. The study will be a great help in having personalized mobile applications based on the specific needs of the students. The mobile application provides the tools as a remedy for the aforementioned issues. For tracking school tasks and activities through using a to-do list, the mobile app allows the user to record notes or use paperless note-taking, use the budgeting feature and eliminate unplanned expenses among students, access the calendar to track and monitor scheduled exams and school events, to access the dictionary feature that allows students to enrich their vocabulary, access the camera instantly to capture presentations and modules, access study music that can be useful to stay focus while studying, and the sketchpad. Having multiple installed mobile applications in an Android device consumes Random Access Memory and internal storage and students tend to save more storage. They were using budget-friendly smartphones, which the technology resolves. Implementing the mobile application for college students will result in productivity and organization, and it will save storage. [ABSTRACT FROM AUTHOR]

Camera Mouse is a freely available software program that visually tracks the movement of facial features to allow individuals with motor impairments to control a computer mouse. The goal of this case study was to provide an evaluation of Camera Mouse as a computer access method as part of a multiple modality communication system for an individual with cerebral palsy. The participant was asked to reproduce sentences and respond to ethical dilemmas for language sampling. Tasks were completed using natural speech and an AAC solution consisting of Camera Mouse paired with an orthographic selection interface and speech synthesis. The participant completed a questionnaire for satisfaction with the introduced assistive technology. Camera Mouse resulted in higher intelligibility than natural speech, while natural speech had a higher rate. She used more complex language with her natural speech. The participant rated Camera Mouse as at least 3/5 on all measures, including 5/5 on weight and safety. The results of this case study suggest Camera Mouse is a promising computer access system for communication supported by the participant's satisfaction rating, expressive language, and synthesized speech production capabilities. [ABSTRACT FROM AUTHOR]

The cryptic and aquatic life histories of sea turtles have made them a challenging group to directly observe, leaving significant knowledge gaps regarding social behavior and fine‐scale elements of habitat use. Using a custom‐designed animal‐borne camera, we observed previously undocumented behaviors by green turtles (Chelonia mydas) at a foraging area in San Diego Bay, a highly urbanized ecosystem in California, USA. We deployed a suction‐cup‐attached pop‐off camera (manufactured by Customized Animal Tracking Solutions) on 11 turtles (mean straight carapace length = 84.0 ± 11.2 cm) for between 1 and 30.8 h. Video recordings, limited to sunlit hours, provided 73 h of total observation time between May 2022 and June 2023. We observed 32 conspecific interactions; we classified 18 as active, entailing clear social behaviors, as compared with 14 passive interactions representing brief, chance encounters. There was no evidence for agonistic interactions. The camera additionally revealed that green turtles consistently use metal structures within urban San Diego Bay. In seven instances, turtles exhibited rubbing behavior against metal structures, and we observed two examples of turtles congregating at these structures. High rates of intraspecific interaction exhibited relatively consistently among individuals provide a compelling case for sociality for green turtles in San Diego Bay, adding to a growing research base updating their historical label of "non‐social." The frequent use of metal structures by the population, in particular the rubbing of exposed skin, has implications for behavioral adaptations to urban environments. Our study exemplifies the promise of technological advances (e.g., underwater and animal‐borne cameras) for updating natural history paradigms, even for well‐studied populations. [ABSTRACT FROM AUTHOR]

Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train's operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train's operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting. [ABSTRACT FROM AUTHOR]

Thermal image drift is observed in prevalent industrial-level cameras because their optomechanical design is not optimised to reduce this phenomenon. In this paper, the effect of temperature on industrial-level cameras is investigated, focusing on the thermal image drift resulting from ambient temperature changes and warming-up process. Standard methods for reducing thermal image drift are reviewed, concentrating on the lack of repeatability aspect of this drift. Repeatable thermal image drift is crucial for applying a compensation model as random thermal deformations in sensors cannot be compensated. Moreover, the possible cause of this issue is explored, and novel optomechanical camera modifications are proposed that maintain the thermal degrees of freedom for the deforming sensor, limiting the lack of repeatability aspect of thermal image drift to a low level. The improvement is verified by conducting experiments using a specialised test stand equipped with an invar frame and thermal chamber. Considering the results from the application of the polynomial compensation model, the standard deviation of the central shifts of image drift is reduced by ×3.99, and the absolute range of image drift is reduced by ×2.53. [ABSTRACT FROM AUTHOR]

Aim: To compare current DSLR and mirrorless camera systems for ease of use, efficiency, and cost in routine dental photography. Methods: Two currently available camera systems (DSLR + macro lens + ring flash/external flash and mirrorless camera + macro lens + ring flash/external flash) were compared and assessed for ease of use, sensor sizes, features, quality of imaging, battery capability, and costs. Results: Mirrorless cameras were smaller and lighter by 16% as compared to DSLRs. Superior Digic X image processors in mirrorless give better image quality compared to DSLRs with older Digic 8 processors. Focus points on the mirrorless are greater at 651 as compared to just 9 on DSLRs. Battery capacity with DSLRs is better at 600–800 shots per charge as compared to 250–300 shots per charge with mirrorless cameras. Overall, the mirrorless camera was priced 16% higher than DSLR cameras. Conclusion: There is a technological shift toward mirrorless camera systems across manufacturers. In the medium to long term, mirrorless technology will replace current DSLR systems making it imperative for dentists to understand and adapt to using mirrorless cameras in imaging. [ABSTRACT FROM AUTHOR]

Abstract The challenges associated with climbing trees to measure cavity dimensions have limited the accumulation of knowledge regarding wood duck (Aix sponsa) nesting habitat and ecology. To overcome this issue, we developed a 2‐person method to measure external and internal tree‐cavity dimensions from the ground. Our approach uses a telescopic pole, wireless cavity inspection camera with a monitor, and reference scale, allowing an object of known length to be viewed and recorded inside the cavity. We tested our method using simulated cavities (n = 20), assessed accuracy by comparing the estimated and actual measurements, and evaluated precision between 2 observers. The average difference (±1 SE) between estimated and actual measurements (n = 40) for entrance width (0.9 ± 0.9 cm), entrance height (0.8 ± 1.1 cm), platform width (0.1 ± 3.7 cm), and platform length (1.0 ± 3.2 cm) were ≤1 cm. There was no significant difference between observer measurements for entrance width, entrance height, platform width, or platform length. Observers overestimated cavity depth by an average of 0.1 ± 1.6 cm and there was a significant difference (1.3 ± 2.2 cm) between observers for mean cavity depth. We applied the technique to naturally occurring cavities. The time to complete a natural‐cavity survey in the field (n = 37) averaged 12.2 ± 6.9 min. Our method increases the practicality, accessibility, and safety of researchers conducting cavity surveys for wood ducks and other cavity‐dependent wildlife through the use of a cost‐effective, cavity‐measuring tool.

Many neonates’ manifest hyperbilirubinemia and jaundice during the first week following birth because of bilirubin accumulation in the blood. Newborns with severe hyperbilirubinemia develop neurotoxicity. Therefore, sensitive, accurate, cost-effective, and timely determination of bilirubin levels is highly desired. Herein, we present three simple systems of bilirubin assessment based on spectrophotometric, and digital imaging tools. Image-capturing devices pose a formidable challenge to sophisticated spectrophotometers and field-monitoring devices because of their simplicity and sensitivity. The first and second systems of bilirubin assessment, in solution, relied on spectrophotometric and conventional color responses of digital images collected with conventional or android smartphone camera, with LODs of 0.060–0.082 and LOQs of 0.200–0.273 ppm bilirubin. The third system utilized a microfluidic paper-based analytical device (μ-PAD) that is patterned with a wax printer on Whatman grade-1 chromatographic papers, with linear calibration graphs of up to 2.70,s and LODs of 0.035–0.040 and LOQs of 0.117–0.133 μg bilirubin/μ-PAD.

Building integrated photovoltaic thermal (BIPVT) greenhouse technology exhibits great potential to enhance crop yielding, energy harvesting, and water management for highly efficient farming and enables controlled environment agriculture (CEA) for automated plant cultivation. The proposed smart greenhouse system uses BIPVT, geothermal, image-based sensing, and control technologies for high energy efficiency and high-fidelity plant growth control. A camera-based, computer-controlled system was demonstrated to successfully monitor single and multiple food plants' growth. The growth rate over the life-cycle of the plant was automatically recorded by cameras without human interference. A novel algorithm was developed to extract the plants from the background of the image and then to measure the length from the ground to the top of the longest plant with different shapes or profiles. The growth rate was automatically generated as fundamental data for farming management and control. Because the plant growth is slow and the errors caused by image noise can be comparable with the actual growth when the interval between images is not large enough, the noise of a camera is evaluated by comparing multiple images under the same exposure condition, which is used to provide the error range of the measurements. The plant growth monitoring system will be integrated into an ongoing greenhouse project for smart greenhouse operation.

The prevalence of highly marbled cattle breeds, such as Wagyu, has increased in the US due to consumer demand for higher-quality beef. The ability to grade highly marbled carcasses accurately and consistently, particularly with quantifying intramuscular fat content for validation, remains a challenging task for the Wagyu beef industry. New camera grading technologies have been proposed by VIAS VBG 2000 (e+v Technology GmbH & Co. KG, 2021), Meat Image Japan (MIJ), and MasterBeef (MB) for Wagyu-influenced beef carcass assessment based on advanced image analysis. However, the intramuscular fat measurements of these camera technologies and the actual percent intramuscular fat (%IMF) in the longissimus at the 12/13th rib have yet to be investigated. Chilled carcasses (n = 173) from F1 Wagyu cattle were ribbed between the 12th and 13th ribs, and the left carcass sides were imaged with the E+V, MIJ, and MB cameras. Additionally, the marbling score was assigned by a team of 3 US Department of Agriculture (USDA) graders. Samples from the longissimus thoracis were collected, and the %IMF of the muscle was determined in triplicate. Linear regressions and descriptive statistics were done using JMP (Statistical Discovery, NC, USA) software. Camera fat-related measurements were linearly correlated, and R2 was calculated. The E+V camera had the highest %IMF predictability of all cameras (P < 0.0001) using the marbling score (R2 = 0.6450) estimate. The MIJ camera presented prediction accuracy between the other 2 technologies (P < 0.0001) of %IMF with identical R2 for fat percent and fat score estimates (R2 = 0.5952). The MB camera had the lowest predictability (P < 0.0001) of %IMF using the measured marbling score (R2 = 0.3269), marbling area (R2 = 0.3333), and marbling percent (R2 = 0.3269) estimates from the instrument. As technology advances, new technologies will provide alternative means for grading Wagyu-influenced carcasses. Additionally, these findings could aid the implementation of the USDA pilot program for remote carcass grading.

Facial expression when a person is sleepy can be seen from eye behavior and facial behavior. To detect the driver's drowsiness, facial expression is a form of non-verbal communication that results from one or more facial muscle movements that can indicate a person's emotional state. This detector works automatically when the system runs without external control. The system is controlled only via the desktop using the Convolutional Neural Network (CNN) method. The working principle of this tool is to start by connecting the power when the system and the Desktop are turned on, then activate the Open CV software created on the desktop and continue testing to recognize the driver's face. If the detected face has a sleepy expression, the device will show the driver's drowsiness through the speaker and warn the driver to be careful when driving. The test results show that the system can detect drowsiness optimally. With the existence of this tool, it is hoped that it can reduce the death rate due to traffic accidents by preventing the driver from experiencing the sleep phase while driving.

Ian Gerard Brennan,1,&ast; Stephen R Kelly,1,&ast; Edel McBride,2 Darragh Garrahy,1 Robert Acheson,2 Joanne Harmon,2 Shane McMahon,2 David J Keegan,1 Helen Kavanagh,1 Louise O’Toole2 1Diabetic RetinaScreen, National Screening Service, Health Service Executive, Dublin, Ireland; 2Diabetic Retinal Screening Service, NEC Care, Cork City, Co. Cork, Ireland&ast;These authors contributed equally to this workCorrespondence: Louise O’Toole, NEC Care, Matthew House-3rd Floor, Fr Matthew Street, Cork City, Co. Cork, T12 TN56, Ireland, Email lotoole@materprivate.iePurpose: Diabetic retinopathy (DR) is a preventable cause of blindness detectable through screening using retinal digital photography. The Irish National Diabetic Retina Screening (DRS) programme, Diabetic RetinaScreen, provides free screening services to patients with diabetes from aged 12 years and older. A technical failure (TF) occurs when digital retinal imaging is ungradable, resulting in delays in the diagnosis and treatment of sight-threatening disease. Despite their impact, the causes of TFs, and indeed the utility of interventions to prevent them, have not been extensively examined.Aim: Primary analysis aimed to identify factors associated with TF. Secondary analysis examined a subset of cases, assessing patient data from five time points between 2019 and 2021 to identify photographer/patient factors associated with TF.Methods: Patient data from the DRS database for one provider were extracted for analysis between 2018 and 2022. Information on patient demographics, screening results, and other factors previously associated with TF were analyzed. Primary analysis involved using mixed-effects logistic regression models with nested patient-eye random effects. Secondary analysis reviewed a subset of cases in detail, checking for causes of TF.Results: The primary analysis included a total of 366,528 appointments from 104,407 patients over 5 years. Most patients had Type 2 diabetes (89.2%), and the overall TF rate was 4.9%. Diabetes type and duration, dilate pupil status, and the presence of lens artefacts on the camera were significantly associated with TF. The Secondary analysis identified the primary cause of TF was found to be optically dense cataracts, accounting for over half of the TFs.Conclusion: This study provides insight into the causes of TF within the Irish DRS program, highlighting cataracts as the primary contributing factor. The identification of patient-level factors associated with TF facilitates appropriate interventions that can be put in place to improve patient outcomes and minimize delays in treatment and diagnosis.Keywords: ungradable image, cataract, screen failure, camera, retinal photography

In this paper, we provide a solution based on fast Fourier Transform (FFT) for frequency detection and interference analysis in a non-line-of-sight (NLOS) multiple-input multiple-output (MIMO) optical camera communications (OCC) link. The proposed NLOS MIMO-OCC uses two organic light-emitting diodes (OLEDs) and a camera as transmitters (Txs) and the receiver, respectively, and reflections from various surfaces such as plain wall, paper and wooden surfaces. In this paper, for the first time, we analyze the performance of NLOS MIMO-OCC for two indoor scenarios utilizing two OLED Txs that are in phase and modulated at the same modulation frequency $f_{s}$ and especially when they are out of phase and under different $f_{s}$ . We carry out FFT for $f_{s}$ detection and interference analysis for OLEDs operating at different phases and $f_{s}$ . To analyze the performance of NLOS MIMO-OCC, first FFT-based $f_{s}$ detection is performed to identify Txs followed by evaluation of the reception success rates $R_{\mathrm {rs}}$ for a range of camera exposure times $t_{\exp }$ and the gain $G_{v}$ for a given $f_{s}$ . The results show that for two OLEDs in phase and different phases at $t_{\exp }$ of 1500, $G_{v}$ of 45 dB and a link span of 2 m, $R_{\mathrm {rs}}$ of 94% is achieved at $f_{s}$ of 600 Hz for the plain wall and paper reflectors. For interference analysis, the proposed FFT analysis gives accurate, i.e., 100 % $f_{s}$ detection in all the above cases.

We describe a quasi-deterministic channel propagation model for human gesture recognition reduced from real-time measurements with our context-aware channel sounder, considering four human subjects and 20 distinct body motions, for a total of 120000 channel acquisitions. The sounder features a radio-frequency (RF) system with 28 GHz phased-array antennas to extract discrete multipaths backscattered from the body in path gain, delay, azimuth angle-of-arrival, and elevation angle-of-arrival domains, and features camera / Lidar systems to extract discrete keypoints that correspond to salient parts of the body in the same domains as the multipaths. Thanks to the precision of the RF system, with average error of only 0.1 ns in delay and 0.2° in angle, we can reliably associate the multipaths to the keypoints. This enables modeling the backscatter properties of individual body parts, such as Radar cross-section and correlation time. Once the model is reduced from the measurements, the channel is realized through raytracing a stickman of keypoints – the deterministic component of the model to represent generalizable motion – superimposed with a Sum-of-Sinusoids process – the stochastic component of the model to render enhanced accuracy. Finally, the channel realizations are compared to the measurements, substantiating the model’s high fidelity.

Traffic monitoring systems featuring robust, multi-sensor fusion capabilities are rapidly growing in demand to observe traffic flow, reduce congestion and to detect and report traffic accidents. However, monitoring outdoor environments using cameras remains challenging due to complex weather conditions, including fog, rain, snow and variable lighting conditions. The presence of these weather conditions can significantly reduce vehicle detection and classification performance using machine learning methods. Unfortunately, openly available datasets for multi-sensor traffic monitoring development and testing remain limited, especially those featuring infrastructure-based cameras and millimeter wave (mmWave) radar. To address these challenges, we evaluate open camera and mmWave radar data using vehicle classification models for cars, trucks, vans and buses on embedded hardware. We also provide an open multi-sensor traffic monitoring dataset with more than 8,000 manually annotated frames as well as mmWave radar point clouds recorded in an urban environment under sunny, partially cloudy, cloudy, rainy and night conditions.

Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semi-automation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75 % of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.

The integration of cameras and millimeter-wave radar into sensor fusion algorithms is essential to ensure robustness and cost effectiveness for vehicle pose estimation. Due to the low resolution of traditional radar, several studies have investigated 4D imaging radar, which provides range, Doppler, azimuth, and elevation information with high resolution. This paper presents a method for robustly estimating vehicle pose through 4D radar and camera fusion, utilizing the complementary characteristics of each sensor. Leveraging the single-view geometry of the detected vehicle bounding box, the lateral position is derived based on the camera images, and the yaw rate is calculated through feature matching between consecutive images. The high-resolution 4D radar data are used to estimate the heading angle and forward velocity of the target vehicle by leveraging the position and Doppler velocity information. Finally, an extended Kalman filter (EKF) is employed to fuse the physical quantities obtained by each sensor, resulting in more robust pose estimation results. To validate the performance of the proposed method, experiments were conducted in foggy environments, including straight and curved driving scenarios. The experimental results indicate that the performance of the camera-based method is reduced due to frame loss in visually challenging scenarios such as foggy environments, whereas the proposed method exhibits superior performance and enhanced robustness.

Vision-aided wireless communication is attracting increasing interest and finding new use cases in various wireless communication applications. These vision-aided communication frameworks leverage visual data captured, for example, by cameras installed at the infrastructure or mobile devices to construct some perception about the communication environment through the use of deep learning and advances in computer vision and visual scene understanding. Prior work has investigated various problems such as vision-aided beam, blockage, and hand-off prediction in millimeter wave (mmWave) systems and vision-aided covariance prediction in massive MIMO systems. This prior work, however, has focused on scenarios with a single object (user) in front of the camera. In this paper, we define the user identification task as a key enabler for realistic vision-aided communication systems that can operate in crowded scenarios and support multi-user applications. The objective of the user identification task is to identify the target communication user from the other candidate objects (distractors) in the visual scene. We develop machine learning models that process either one frame or a sequence of frames of visual and wireless data to efficiently identify the target user in the visual/communication environment. Using the large-scale multi-modal sense and communication dataset, DeepSense 6G, which is based on real-world measurements, we show that the developed approaches can successfully identify the target users with more than 97% accuracy in realistic settings. This paves the way for scaling the vision-aided wireless communication applications to real-world scenarios and practical deployments.

Detection based on LiDAR and camera fusion is increasingly popular for researchers in the autonomous driving domain. Compared to the camera-only and LiDAR-only methods, the fusion-based methods indeed improve the detection accuracy on public-available datasets. However, due to the complexity of the projection or fusion mechanism, few of these methods can run in real time even on an advanced desktop GPU. Thus, in this paper, we propose a new fusion detection model G-Fusion with a light and fast image view-transform module. According to our receptive field analysis of image feature maps, we directly project image features to only one voxel layer located on the ground, then fuse the LiDAR and image features by concatenation and convolution. With this delicately designed module, G-Fusion greatly boosts the state-of-the-art speed performance on the nuScenes dataset, achieving a good balance with the competitive detection scores. Meanwhile, since the precision of sensor extrinsic parameters is important for most fusion-based methods, we also deeply dig into our model’s calibration error tolerance ability and discover the failure noise condition.

The article focuses on the role for parental-camera recording and telemedicine in the evaluation of vertigo in children. Topics include the need for the careful collection of medical history and description of symptoms in the diagnostic process, the value of patients' self-recorded videos for observing children's behaviors during acute vertigo attacks and how telemedicine could be useful for evaluating dizzy children. A diagnostic flow chart for the evaluation of vertigo is also presented.

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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.