Fully automated vehicles collect information about their road environments to adjust their driving actions, such as braking and slowing down. The development of artificial intelligence (AI) and the Internet of Things (IoT) has improved the cognitive abilities of vehicles, allowing them to detect traffic signs, pedestrians, and obstacles for increasing the intelligence of these transportation systems. Three-dimensional (3D) object detection in front-view images taken by vehicle cameras is important for both object detection and depth estimation. In this paper, a joint channel attention and multidimensional regression loss method for 3D object detection in automated vehicles (called CAMRL) is proposed to improve the average precision of 3D object detection by focusing on the model’s ability to infer the locations and sizes of objects. First, channel attention is introduced to effectively learn the yaw angles from the road images captured by vehicle cameras. Second, a multidimensional regression loss algorithm is designed to further optimize the size and position parameters during the training process. Third, the intrinsic parameters of the camera and the depth estimate of the model are combined to reduce the object depth computation error, allowing us to calculate the distance between an object and the camera after the object’s size is confirmed. As a result, objects are detected, and their depth estimations are validated. Then, the vehicle can determine when and how to stop if an object is nearby. Finally, experiments conducted on the KITTI dataset demonstrate that our method is effective and performs better than other baseline methods, especially in terms of 3D object detection and bird’s-eye view (BEV) evaluation.

Recently, cognitive serious games have successfully been employed to train cognitive abilities in elderly people with mild cognitive impairment, Alzheimer’s disease, and related disorders. However, despite the continuous rehabilitation game design and its applications, the existing cognitive exercise games fall short of user interaction and personalized elements with regard to difficult levels, which leads to users leaving early and losing interests during the gameplay. In this regard, the purpose of the study was to design and develop the serious game inclusive of playful elements for user motivation, the web‐based mobile application system for easy accessibility, and Artificial Intelligence– (AI–) based difficulty level adjustment system for prevention from earlier leaving out in the middle of the play so that the elderly users can feel entertaining and immersed into the cognitive game voluntarily. This study was designed as an eight‐week pilot experiment with thirty‐seven participants in their 60s to 80s for the game’s usability assessment purpose. Results of the study showed that the AI‐based cognitive exercise game was acceptable, interesting, and motivating for the elderly people and the test results before and after the eight‐week training suggest a relationship between longer the training on the game and lower cognitive assessment scores including geriatric quality of life scale, geriatric depression scale, and Korean version of mini‐mental state examination (MMSE). These correlations demonstrate the potential value of serious games in clinical assessment of cognitive status for the elderly users with varying cognitive ability. Based on these results, the elderly‐centered serious game with playful element can be potentially used in clinical settings, allowing the cognitive training to be more enjoyable and more medically effective. Given these promising results, a more focused study can extend to the game system or additional game tools or features to be explored that solely target the elderly by applying AI and advanced visualization devices.

In the wake of developments in consumer electronics, electric vehicles are gradually becoming representative carriers of next-generation consumer electronics. Electric vehicles incorporate 5G and artificial intelligence (AI) technologies, which significantly enhance the driving experience. Autonomous driving, as a widely supported feature of most electric vehicles, allows these vehicles to make decisions automatically, reducing safety hazards and enhancing driving efficiency. There has been a growing interest in cost-effective monocular vision solutions to further improve the viability of this technology. However, the limited imaging capabilities of monocular cameras make it challenging to extract explicit depth information from images, which is crucial for locating objects in a scene. In this paper, based on a location-aware attention mechanism and an importance-aware detection head, we propose MonoLI, a monocular 3D object detection method for precisely locating objects. First, the location-aware attention mechanism can perceive location information in both the spatial and channel dimensions. This helps the network learn a more suitable global feature representation for the detection task. Second, the importance-aware detection head considers the difference between auxiliary and target tasks, as well as the importance of different branches in target tasks. In addition, to enhance the applicability and deployability of the method, we introduce partial convolution blocks, which improve the performance and effectively reduce the parameter size and computational complexity. Finally, experiments conducted on the KITTI benchmark dataset show that our method outperforms many well-known baseline methods in terms of 3D object detection and bird’s-eye view (BEV) evaluation.

In recent years, the emergence of digital therapeutics as a novel approach to managing conditions has garnered significant attention. This approach involves using evidence-based therapeutic interventions that are facilitated by high-quality software programs to treat, manage, or prevent medical conditions. The incorporation of digital therapeutics into the Metaverse has increased the feasibility of their implementation and application in all areas of medical services. In urology, substantial digital therapeutics are being produced and researched, including mobile apps, bladder devices, pelvic floor muscle trainers, smart toilet systems, mixed reality-guided training and surgery, and training and telemedicine for urological consultations. The purpose of this review article is to provide a comprehensive overview of the current impact of the Metaverse on the field of digital therapeutics and identify its current trends, applications, and future perspectives in the field of urology.

Wireless sensor networks have grown rapidly with the innovation in Information Technology. Sensor nodes are distributed and deployed over the area for gathering requisite information. Sensor nodes possess a negative characteristic of limited energy which pulls back the network from exploiting its peak capabilities. Hence, it is necessary to gather and transfer the information in an optimized way which reduces the energy dissipation. Ant Colony Optimization (ACO) is being widely used in optimizing the network routing protocols. Ant Based Routing can play a significant role in the enhancement of network life time. In this paper, Intercluster Ant Colony Optimization algorithm (IC-ACO) has been proposed that relies upon ACO algorithm for routing of data packets in the network and an attempt has been made to minimize the efforts wasted in transferring the redundant data sent by the sensors which lie in the close proximity of each other in a densely deployed network. The IC-ACO algorithm was studied by simulation for various network scenarios. The results depict the lead of IC-ACO as compared to LEACH protocol by indicating higher energy efficiency, prolonged network lifetime, enhanced stability period, and the elevated amount of data packets in a densely deployed wireless sensor network.