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As natural habitats protection has become a global priority, smart sensing-nets are ever-increasingly needed for effective environmental observation. In a practical monitoring network, it is critical to deploy sensors with sufficient automated intelligence and motion flexibility. Recent advances in robotics and sensors technology have enabled automated mobile sensors deployment in a smart sensing-net. Existing deployment algorithms can be employed to calculate adequate destinations (goals) for sensors to perform respective monitoring tasks. However, given the calculated goal positions, the problem of how to actually coordinate a fleet of robots and schedule moving paths from random initials to reach their goals safely, without collisions, remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we investigate this problem and propose polynomial-time collision-free motion algorithms based on batched movements to ensure all the mobile sensors reach their goals successfully without incurring collisions. We observe that the grouping (batching) strategy is similar to the coloring procedure in graph theory. By constructing a conflict graph, we model the collision-free path scheduling as the well-known k-coloring problem, from which we reduce to our k-batching problem (determining the minimum number of required batches for a successful deployment) and prove its NP completeness. Since the k-batching problem is intractable, we develop CFMA (collision-free motion algorithm), a simple yet effective batching (coloring) heuristic mechanism, to approximate the optimal solution. Performance results show that our motion algorithms outperform other existing path-scheduling mechanisms by producing 100% sensors reachability (success probability of goals reaching), time-bounded deployment latency with low computation complexity, and reduced energy consumption. Note to Practitioners— This research was originally motivated by an oceanography project, which studied marine microbes by sending a team of tiny robots (sensors) randomly scattered on the ocean floor. For hard-to-access habitats like deserts or oceans, where manual placement of sensors is costly or impossible, automatically scheduling robots movements to calculated positions from random initials is essential for an effective monitoring. Our contribution is unique in two ways. First, traditional path-planning research focuses more on independent robots navigating the environment, whereas we target on the network automation problem, coordinating a fleet of robots working together to perform an environmental sensing task. Second, we observe that existing movement methods are too complicated and energy-consuming due to the calculation on-the-go nature. To provide a practical solution, we suggest and design our motion algorithms from a new perspective: pre-scheduled batched movements. Our approach requires a central server for grouping (batching) calculations, but can effectively reduce computation complexity and save significant energy expenditure exerted on resource-limited sensors. The proposed collision-free motion algorithm (CFMA) is a suboptimal yet efficient batching solution, which can be easily applied in real-life open-space monitoring networks. Insights from this foundational research will facilitate future opportunities to implement and verify our method in operational monitoring testbeds. [ABSTRACT FROM AUTHOR]

Video compression is an important procedure for digital applications. A commonly used format for inputs to video coders is YUV420, which is a subsampled result from YUV444, that is transformed and demosaicked from a color filter array (CFA) format. The process of chroma subsampling is a crucial step for the reconstructed image quality. A state-of-the-art method utilizes the solution of a prior method to compute the starting point, and devises a search method to improve the results. However, the search method does not consider the optimality condition of the problem, and the overall computational complexity is high because of the execution of the prior method for the starting point. In the proposed work, the cost function of the optimization problem is analyzed and decomposed into 4 subchannels based on the CFA format. The optimal line of each subterm is studied in 3-dimensional space. The problem is then mathematically reduced to a 3-subchannel problem. Based on the combinations of the optimal lines, a triangle search area is formed and proved to contain the optimal solution. And because the derived triangle search area is small on average, the search for the optimal value can be considerably fast. Experimental results demonstrate that the proposed approach can produce reconstructed images with qualities that are exactly the same as those generated using the state-of-the-art method. Relative to the state-of-the-art method, the proposed approach can reduce the time complexity by 84.08%–84.91%, and reduce the number of search points by 40.24%–85.25%, for the image datasets Kodak and IMAX with different demosaicking methods. [ABSTRACT FROM AUTHOR]

The touchless techniques in human-computer interaction (HCI) can effectively expand computer access capabilities for disabled people. This paper presents Touchless Head-Control (THC), an assistive system method for computer cursor control based on head pose captured with an RGB camera. Our work aimed to replace the standard cursor control using a device on the user’s head. The convolutional neural networks with predicted fine-grained feature maps and binned classification were applied to estimate the head pose angles. The mouse pointer or cursor is moved to actual locations on the screen based on head movement (yaw and pitch) and the center position of the face. Head tilt to the right or left (roll) to control the mouse button. In addition, the proposed method can be used to simulate the movement of the robot or joystick using the head to control objects within three degrees of freedom (DOF). Various participants were involved in the interaction design evaluation, in which target selection accuracy, travel time, and path efficiency were measured. This technology allows people with limited motor skills to easily control a PC cursor and 3D object orientation without the use of additional equipment or sensors. [ABSTRACT FROM AUTHOR]

Head pose estimation is one of the sensing systems needed for some intelligent surveillance, such as human behavior analysis, intelligent driver assistance, visual attention, and monitoring. These systems require accurate alignment and head movement direction prediction. The previous methods are greatly dependent on the facial landmarks and depth information. Usually, the head pose is measured by estimating several keypoints that require a correct head pose mapping to get accurate results. Moreover, facial landmarks have a detrimental effect on performance when the face is occluded or not adequately visualized. This paper proposes a method for head pose estimation of various facial conditions, such as occlusion and challenging viewpoints. We present a combination of coarse and fine feature maps classification to train a multi-loss deep Convolutional Neural Networks (CNN) to get precise Euler angles (yaw, pitch, roll) of the head position without keypoints and landmarks. Our proposed method uses more quantization units for angle classification to learn coarse and fine structure mapping for better spatial clustering features on an RGB image of a single camera. The experiments are performed on benchmark datasets and some head poses in real cases. The mean average error of prediction is 5.06°, 4.06°, and 2.96°, for the AFLW2000, AFLW, and BIWI datasets, which significantly improves the head pose estimation performance compared to the previous methods. Additionally, the proposed method outperforms previous approaches in computation time of 11 frames per second that is beneficial for real-life applications. [ABSTRACT FROM AUTHOR]