暂无摘要。

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.

UNASSIGNED: To assess the utility of wearable cameras in medical examinations, we created a physician-view video-based examination question and explanation, and the survey results indicated that these cameras can enhance the evaluation and educational capabilities of medical examinations.

BACKGROUND: Older adults are at greater risk of eating rotten fruits and of getting food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated various tools to detect rotten food items in various ways. Nevertheless, little is known about how to create an app to detect rotten food items to support older adults at a risk of health problems from eating rotten food items.
OBJECTIVE: This study aimed to (1) create a smartphone app that enables older adults to take a picture of food items with a camera and classifies the fruit as rotten or not rotten for older adults and (2) evaluate the usability of the app and the perceptions of older adults about the app.
METHODS: We developed a smartphone app that supports older adults in determining whether the 3 fruits selected for this study (apple, banana, and orange) were fresh enough to eat. We used several residual deep networks to check whether the fruit photos collected were of fresh fruit. We recruited healthy older adults aged over 65 years (n=15, 57.7%, males and n=11, 42.3%, females) as participants. We evaluated the usability of the app and the participants\' perceptions about the app through surveys and interviews. We analyzed the survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from the interviewees for in-depth analysis of the survey responses.
RESULTS: The participants were satisfied with using an app to determine whether a fruit is fresh by taking a picture of the fruit but are reluctant to use the paid version of the app. The survey results revealed that the participants tended to use the app efficiently to take pictures of fruits and determine their freshness. The qualitative data analysis on app usability and participants\' perceptions about the app revealed that they found the app simple and easy to use, they had no difficulty taking pictures, and they found the app interface visually satisfactory.
CONCLUSIONS: This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work to make the app distinguish the freshness of various food items other than the 3 fruits selected still remains.

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.

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.

暂无摘要。

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.

The Kenyan part of the East African Rift System hosts several geothermal fields for energy production. Changes in the extraction rate of geothermal fluids and the amount of water re-injected into the system affect reservoir pressure and production capacity over time. Understanding the balance of production, natural processes and the response of the geothermal system requires long-term monitoring. The presence of a geothermal system at depth is often accompanied by surface manifestations, such as hot water springs and fumaroles, which have the potential for monitoring subsurface activity. Two thermal camera timelapse systems were developed and installed as part of a multi-sensor observatory in Kenya to capture fumarole activity over time. These cameras are an aggregation of a camera unit, a control unit, and a battery charged by a solar panel, and they monitor fumarole activity on an hourly basis, with a deep sleep of the system in between recordings. The article describes the choice of hardware and software, presents the data that the cameras acquire, and discusses the system\'s performance and possible improvement points.

We describe a Raspberry Pi-based camera system that is portable, robust and weatherproof, with a close-up focus (2.5 cm). We show that this camera system can be used in remote locations with high rainfall and humidity. The camera has an Infrared LED light to film in dark places and can continuously record up to 21 days (504 h). We also describe how to make concrete artificial shelters to mount the camera in. One of the great strengths of this shelter/camera set-up is that the animals choose to take up residence and can then be filmed for extended periods with no disturbance. Furthermore, we give examples of how shelters and cameras could be used to film a range of behaviours in not only many small cryptic amphibian species but also other small vertebrates and invertebrates globally.