Obstacle-Aware Wireless Video Sensor Network Deployment For 3D Indoor Space Monitoring

In recent years wireless video sensors networks (WVSNs) have emerged as a leading technology for monitoring 3D indoor space in campus, industrial and medical areas as well as other types of environments. In contrast to traditional sensors such as heat or light sensors often considered with omnidirectional sensing range, the sensing range of a video sensor is directional and can be deemed as a pyramid-shape in 3D. Moreover, in an indoor environment, there are often obstacles such as lamp stands or furniture, which introduce additional challenges and further render the deployment solutions for traditional sensors and 2D sensing field inapplicable or incapable of solving the WVSN deployment problem for 3D indoor space monitoring. In this thesis, we take the first attempt to address this by modeling the general problem in a continuous space and strive to minimize the number of required video sensors to cover the given 3D regions. We then convert it into a discrete version by incorporating 3D grids for our discrete model, which can achieve arbitrary approximation precision by adjusting the grid granularity. We also create two strategies for dealing with stationary obstacles existed in the 3D indoor space, namely, Divide and Conquer Detection strategy and Accurate Detection strategy. We propose a greedy heuristic and an enhanced Depth First Search (DFS) algorithm to solve the discrete version problem where the latter, if given enough time can return the optimal solution. We evaluate our solutions with a customized simulator that can emulate the actual WVSN deployment and 3D indoor space coverage. The evaluation results demonstrate that our greedy heuristic can reduce the required video sensors by up to 47% over a baseline algorithm, and our enhanced DFS can achieve an additional reduction of video sensors by up to 25%

Adaptive multi-layer selective ensemble least square support vector machines with applications

Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate sub-sub-models with same KPs and different RPs are selectively fused by using the branch and bound SEN (BBSEN) to obtain K SEN-sub-models. Third, these SEN-sub-models are selectively combined through using BBSEN again to obtain SEN models with different ensemble sizes, and then a new metric index is defined to determine the final AMLSEN-LSSVMbased soft measuring model. Finally, the learning parameters and ensemble sizes of different SEN layers are obtained adaptively. Simulation results based on the UCI benchmark and practical DXN datasets are conducted to validate the effectiveness of the proposed approach

Load-balancing rendezvous approach for mobility-enabled adaptive energy-efficient data collection in WSNs

Copyright © 2020 KSII The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs

Adversarial Noise Layer: Regularize Neural Network By Adding Noise

In this paper, we introduce a novel regularization method called Adversarial Noise Layer (ANL) and its efficient version called Class Adversarial Noise Layer (CANL), which are able to significantly improve CNN's generalization ability by adding carefully crafted noise into the intermediate layer activations. ANL and CANL can be easily implemented and integrated with most of the mainstream CNN-based models. We compared the effects of the different types of noise and visually demonstrate that our proposed adversarial noise instruct CNN models to learn to extract cleaner feature maps, which further reduce the risk of over-fitting. We also conclude that models trained with ANL or CANL are more robust to the adversarial examples generated by FGSM than the traditional adversarial training approaches

Study on a Strong Polymer Gel by the Addition of Micron Graphite Oxide Powder and Its Plugging of Fracture

It is difficult to plug the fracture water channeling of a fractured low-permeability reservoir during water flooding by using the conventional acrylamide polymer gel due to its weak mechanical properties. For this problem, micron graphite powder is added to enhance the comprehensive properties of the acrylamide polymer gel, which can improve the plugging effect of fracture water channeling. The chemical principle of this process is that the hydroxyl and carboxyl groups of the layered micron graphite powder can undergo physicochemical interactions with the amide groups of the polyacrylamide molecule chain. As a rigid structure, the graphite powder can support the flexible skeleton of the original polyacrylamide molecule chain. Through the synergy of the rigid and flexible structures, the viscoelasticity, thermal stability, tensile performance, and plugging ability of the new-type gel can be significantly enhanced. Compared with a single acrylamide gel, after adding 3000 mg/L of micrometer-sized graphite powder, the elastic modulus, the viscous modulus, the phase transition temperature, the breakthrough pressure gradient, the elongation at break, and the tensile stress of the acrylamide gel are all greatly improved. After adding the graphite powder to the polyacrylamide gel, the fracture water channeling can be effectively plugged. The characteristics of the networked water flow channel are obvious during the injected water break through the gel in the fracture. The breakthrough pressure of water flooding is high. The experimental results are an attempt to develop a new gel material for the water plugging of a fractured low-permeability reservoir

Partition-based K-space Synthesis for Multi-contrast Parallel Imaging

Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore,utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve super reconstruction quality of T2-weighted image by feature fusion. Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image. Finally, the objective T2 is synthesized by extracting the sub-T2 data of each part. Experimental results showed that our combined technique can achieve comparable or better results than using traditional k-space parallel imaging(SAKE) that processes each contrast independently

Hydroclimatic variability in loess delta D-wax records from the central Chinese Loess Plateau over the past 250 ka

This study reports hydrogen isotopic records from the central Chinese Loess Plateau (CLP) over the past 250 ka. After eliminating the influence of ice and local temperatures, the delta D-wax records extracted from two loess sites at Xifeng and Luochuan can be taken to represent arid/humid alternations in the hydrological environment in this marginal Asian Summer Monsoon (ASM) region; they also contain integrated information on summer precipitation patterns and the corresponding responses to these changes by predominant vegetation cover types. These arid/humid alternations show 100 ka, 40 ka and 20 ka cycles. An increase in precipitation in association with an enhanced summer monsoon has historically been taken to be the major factor driving a humid environment in the central CLP. However, hydroclimatic changes in delta D-wax records differ for the central CLP, central China and southern China. Over a 20 ka cycle, the influence of solar insolation on hydroclimatic changes can be shown to be consistent throughout the central CLP. However, changes in the relative location of the land and sea may have caused different hydroclimatic responses between southern China and the central CLP on a glacial-interglacial scale. The hydroclimatic variability in the central CLP would suggest that an enhanced summer monsoon due to climatic warming is the key to understanding decreased drought degree in this marginal monsoonal region

Tackling the Confusing Words of Strategy: Effective Use of Key Words for Publication Impact

The tremendous growth of the strategic management field has not mitigated the problem of lack of consistency in terminology. To make things even worse, general-purpose catalogs, such as ABI and Social Sciences Citation Index (SSCI) have developed inconsistent lists of strategy terms. The phenomenon weakens the legitimacy of the field as a normal science. Based on extensive review of business indices and high quality business journals, we help address this problem by proposing a taxonomy for strategic management scholars to use in key word selection. This effort is rendered in a three-step approach. First, we identify terms associated with strategy by investigating two different types of databases, which are general indices such as ABI/INFORM and the Permuterm Subject Index (PSI) and journal indexes. Second, we record an explicit definition for each of the terms identified. Finally, we eliminate any terms that were clearly not relevant to the field of strategy based on criteria established ex post selection of the terms. To complement our key word selections, we further propose a preliminary draft of an indexing system based on the Journal of Economics (JEL) model. Taken together, our research proposes a mechanism which can be used by the strategic management field to help researchers signal the subject and scope of their studies more effectively