Full-View Coverage Problems in Camera Sensor Networks

Camera Sensor Networks (CSNs) have emerged as an information-rich sensing modality with many potential applications and have received much research attention over the past few years. One of the major challenges in research for CSNs is that camera sensors are different from traditional scalar sensors, as different cameras from different positions can form distinct views of the object in question. As a result, simply combining the sensing range of the cameras across the field does not necessarily form an effective camera coverage, since the face image (or the targeted aspect) of the object may be missed. The angle between the object\u27s facing direction and the camera\u27s viewing direction is used to measure the quality of sensing in CSNs instead. This distinction makes the coverage verification and deployment methodology dedicated to conventional sensor networks unsuitable. A new coverage model called full-view coverage can precisely characterize the features of coverage in CSNs. An object is full-view covered if there is always a camera to cover it no matter which direction it faces and the camera\u27s viewing direction is sufficiently close to the object\u27s facing direction. In this dissertation, we consider three areas of research for CSNS: 1. an analytical theory for full-view coverage; 2. energy efficiency issues in full-view coverage CSNs; 3. Multi-dimension full-view coverage theory. For the first topic, we propose a novel analytical full-view coverage theory, where the set of full-view covered points is produced by numerical methodology. Based on this theory, we solve the following problems. First, we address the full-view coverage holes detection problem and provide the healing solutions. Second, we propose $k$-Full-View-Coverage algorithms in camera sensor networks. Finally, we address the camera sensor density minimization problem for triangular lattice based deployment in full-view covered camera sensor networks, where we argue that there is a flaw in the previous literature, and present our corresponding solution. For the second topic, we discuss lifetime and full-view coverage guarantees through distributed algorithms in camera sensor networks. Another energy issue we discuss is about object tracking problems in full-view coverage camera sensor networks. Next, the third topic addresses multi-dimension full-view coverage problem where we propose a novel 3D full-view coverage model, and we tackle the full-view coverage optimization problem in order to minimize the number of camera sensors and demonstrate a valid solution. This research is important due to the numerous applications for CSNs. Especially some deployment can be in remote locations, it is critical to efficiently obtain accurate meaningful data

Growth and Development of Two Broiler Strains with Low Protein and Crystalline Amino Acid Supplemented Diets

The objective of this research was to compare the growth performance of broilers from two commercial breeds with control, low protein and low protein supplemented with crystalline amino acids diets. This was a randomized block design, and identical experiments were conducted on successively in two years. In each experiment, day-old chicks, Ross 708 broilers and Cobb 405 broilers, were randomly assigned into three dietary treatments: 1) positive control, 2) low crude protein (LP), and 3) LP + crystalline amino acids (CAA). A three phase feeding program was used. Feed and water were provided ad-libitum. On d 12, 19, 26, 33, 40, 47, and 54, two birds per pen were randomly selected, weighed, and euthanized by carbon dioxide asphyxiation for further dissection. Three muscles (M. peronaeus longus, M. iliotibialis, and M. pectoralis thoracica), and three bones (tibia, femur, and radius), and organs were collected. Abdominal fat was only collected at the end of the experiment in the first year. The results showed that dietary protein restriction by 6% units had a retarding influence on the growth and development of visceral organs, muscle tissues and bone mass. The supporting effect of CAA helped compensate the negative effect of low protein diet on the bodyweight, organs, muscles and bones growth, but only during the early growing stages. Cobb broilers had a significantly heavier bodyweight with both low protein and low protein with CAA diets. However, Ross broilers produced significant heavier pectoralis, and had more pectoralis yield than Cobb broilers by feeding the control and low protein with CAA diets. The relative growth of pectoralis in both breeds was significantly inhibited by feeding low protein diet, and the decrease of pectoralis proportion even showed a week earlier, compared to the absolute pectoralis growth. The CAA supplementation enabled both breeds to produce of close pectoralis proportion compared to those on control diet, and this supportive effect of CAA on Ross broiler lasted a week longer than on Cobb broilers

Certificateless Signature Scheme Based on Rabin Algorithm and Discrete Logarithm

Certificateless signature can effectively immue the key escrow problem in the identity-based signature scheme. But the security of the most certificateless signatures usually depends on only one mathematical hard problem, which makes the signature vulnerable when the underlying hard problem has been broken. In order to strengthen the security, in this paper, a certificateless signature whose security depends on two mathematical hard problems, discrete logarithm and factoring problems, is proposed. Then, the proposed certificateless signature can be proved secure in the random oracle, and only both of the two mathematical hard problems are solved, can the proposed signature be broken. As a consequence, the proposed certificateless signature is more secure than the previous signatures. On the other hand, with the pre-computation of the exponential modular computation, it will save more time in the signature signing phase. And compared with the other schemes of this kind, the proposed scheme is more efficient

Influence Mechanism of Smart City Innovation on Green Supply Chain Network Efficiency

The traditional logistics industry faces increasingly prominent problems like high energy consumption, high pollution, and high emissions. The improvement of green supply chain network efficiency (GSCNE) has become the development direction of this industry. Focusing on the panel data of 225 prefectures in China during 2012-2021, this paper uses the difference in differences (DID) method to explore the influence mechanism of smart city construction on GSCNE. The results show that smart city construction can enhance GSCNE via three mediators: information and communication technology (ICT), sustainable development, and technological innovation. Finally, some managerial implications were summarized according to the research conclusions

Active Surface with Passive Omni-Directional Adaptation of Soft Polyhedral Fingers for In-Hand Manipulation

Track systems effectively distribute loads, augmenting traction and maneuverability on unstable terrains, leveraging their expansive contact areas. This tracked locomotion capability also aids in hand manipulation of not only regular objects but also irregular objects. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive network structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system's active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger's stiffness distribution and simplified models to assess the feasibility of repositioning and reorienting a grasped object. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.Comment: 10 pages, 6 figures, 2 tables, submitted to ICRA 202

Knowledge Graph Driven Recommendation System Algorithm

In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information. The final step involves integrating features of entities and users to produce a recommendation score. The model performance was evaluated by comparing its effects on various aggregation methods and influence factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%, respectively, which is better than existing benchmark methods like LibFM, DeepFM, Wide&Deep, and RippleNet