Storage-Centric Wireless Sensor Networks for Smart Buildings

In the first part of the dissertation, we propose a model-based systems design framework, called WSNDesign, to facilitate the design and implementation of wireless sensor networks for Smart Buildings. We apply model-based systems engineering principles to enhance model reusability and collaboration among multiple engineering domains. Specifically, we describe a hierarchy of model libraries to model various behaviors and structures of sensor networks in the context of Smart Buildings, and introduce a system design flow to compose both continuous-time and event-triggered modules to develop applications with support for performance evaluation. WSNDesign can obtain early feedback and high-confidence evaluation of a design without requiring any intrusive and costly deployment. In addition, we develop a graphical tool that exposes a sequence of design choices to system designers, and provides instant feedback about the influence of a design decision on the complexity of system analysis. Our tool can facilitate comprehensive analysis and bring competitive advantage to the systems design workflow by reducing costly unanticipated behaviors. One of the main challenges to design efficient sensor networks is to collect and process the data generated by various sensor motes in Smart Buildings efficiently. To make this task easier, we provide an abstraction for data collection and retrieval in the second part of the dissertation. Specifically, we design and implement a distributed database system, called HybridDB, for application development. HybridDB enables sensors to store large-scale datasets in situ on local NAND flash using a novel resource-aware data storage system, and can process typical queries in sensor networks extremely efficiently. In addition, HybridDB supports incremental $\epsilon$-approximate querying that enables clients to retrieve a just-sufficient set of sensor data by issuing refinement and zoom-in sub-queries to search events and analyze sensor data efficiently. HybridDB can always return an approximate dataset with guaranteed maximum absolute ($L_\infty$-norm) error bound, after applying temporal approximate locally on each sensor, and spatial approximate in the neighborhood on the proxy. Furthermore, HybridDB exploits an adaptive error distribution mechanism between temporal approximate and spatial approximate for trade-offs of energy consumption between sensors and the proxy, and response times between the current sub-query and the following sub-queries. The implementation of HybridDB in TinyOS 2.1 is transformed and imported to WSNDesign as a part of the model libraries

PointHuman: Reconstructing Clothed Human from Point Cloud of Parametric Model

It is very difficult to accomplish the 3D reconstruction of the clothed human body from a single RGB image, because the 2D image lacks the representation information of the 3D human body, especially for the clothed human body. In order to solve this problem, we introduced a priority scheme of different body parts spatial information and proposed PointHuman network. PointHuman combines the spatial feature of the parametric model of the human body with the implicit functions without expressive restrictions. In PointHuman reconstruction framework, we use Point Transformer to extract the semantic spatial feature of the parametric model of the human body to regularize the implicit function of the neural network, which extends the generalization ability of the neural network to complex human poses and various styles of clothing. Moreover, considering the ambiguity of depth information, we estimate the depth of the parameterized model after point cloudization, and obtain an offset depth value. The offset depth value improves the consistency between the parameterized model and the neural implicit function, and accuracy of human reconstruction models. Finally, we optimize the restoration of the parametric model from a single image, and propose a depth perception method. This method further improves the estimation accuracy of the parametric model and finally improves the effectiveness of human reconstruction. Our method achieves competitive performance on the THuman dataset

Photoinduced High-Chern-Number Quantum Anomalous Hall Effect from Higher-Order Topological Insulators

Quantum anomalous Hall (QAH) insulators with high Chern number host multiple dissipationless chiral edge channels, which are of fundamental interest and promising for applications in spintronics and quantum computing. However, only a limited number of high-Chern-number QAH insulators have been reported to date. Here, we propose a dynamic approach for achieving high-Chern-number QAH phases in periodically driven two-dimensional higher-order topological insulators (HOTIs).In particular, we consider two representative kinds of HOTIs which are characterized by a quantized quadruple moment and the second Stiefel-Whitney number, respectively. Using the Floquet formalism for periodically driven systems, we demonstrate that QAH insulators with tunable Chern number up to four can be achieved. Moreover, we show by first-principles calculations that the monolayer graphdiyne, a realistic HOTI, is an ideal material candidate. Our work not only establishes a strategy for designing high-Chern-number QAH insulators in periodically driven HOTIs, but also provides a powerful approach to investigate exotic topological states in nonequilibrium cases.Comment: 6 pages, 3 figure

Studying Real-time Traffic in Multi-hop Networks Using the EMANE Emulator: Capabilities and Limitations

In this paper, we study the fidelity of an open-source software emulator to provide reliable estimation of performance for real-time traffic in mobile ad-hoc networks. We emulate the IEEE 802.11 MAC/PHY (DCF) using the EMANE software emulator deployed on a cluster and run experiments for different multi-hop wireless scenarios with the Optimized Link State Routing (OLSR) protocol. As an instance of real-world usage scenario, we study the performance of real-time streaming media over a mesh network supported by OLSR. In particular, we study the effect of mobility and background traffic on carried load, delay and jitter. As another application, we analyze the impact of the wireless network on the self-similarity of aggregate traffic. Using traffic source models with high variability, we show that the aggregate traffic in the wireless network is self-similar and hence preserves its burstiness at larger time scales. The results are consistent with those obtained from high-fidelity simulation within some limitations of the emulator.Research supported by the US Army Research Office through MURI awards with numbers W911-NF-08-1-0238 and W911-NF-07-1-0287, and by the Defense Advanced Research Projects Agency (DARPA) under award number 013641-001 for the Multi-Scale Systems Center (MuSyC), through the FRCP of SRC and DARPA

Inhibition of the m6A reader IGF2BP2 as a strategy against T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive malignant leukemia with extremely limited treatment for relapsed patients. N6‐methyladenosine (m6A) reader insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) participates in the initiation and growth of cancers by communicating with various targets. Here, we found IGF2BP2 was highly expressed in T-ALL. Gain and loss of IGF2BP2 demonstrated IGF2BP2 was essential for T-ALL cell proliferation in vitro and loss of IGF2BP2 prolonged animal survival in a human T-ALL xenograft model. Mechanistically, IGF2BP2 directly bound to T-ALL oncogene NOTCH1 via an m6A dependent manner. Furthermore, we identified a small-molecule IGF2BP2 inhibitor JX5 and treatment of T-ALL with JX5 showed similar functions as knockdown of IGF2BP2. These findings not only shed light on the role of IGF2BP2 in T-ALL, but also provide an alternative γ‑Secretase inhibitors (GSI) therapy to treat T-ALL.Inhibition of the m6A reader IGF2BP2 as a strategy against T-cell acute lymphoblastic leukemiapublishedVersio

Research progress and challenges faced by unmanned aerial vehicles in complex underground spaces

The technological development and application status of underground complex space UAVs are analyzed. It is pointed out that underground complex space UAVs face problems such as insufficient individual performance, limited environmental situational awareness and autonomous navigation capabilities, and limited formation collaboration capabilities. In order to solve the above problems, the development trends of key technologies for underground UAVs are prospected. ① Small and lightweight integrated UAV design technology is proposed. By improving the mechanical structure of the UAV, improving the integration of information perception sensors such as LiDAR and depth camera with control systems, and optimizing power management systems, the ultimate goal is to improve the cruise speed, endurance time, and other performance of individual UAV. ② Situation awareness and autonomous navigation technology in GPS rejection environment is proposed. The key technical challenges such as simultaneous localization and mapping (SLAM) navigation and real-time path planning should be overcome. The limitations of algorithms around specific scenarios should be gradually broken through. The perception capability, environmental adaptability, and robustness of unmanned systems should be improved. ③ Formation collaboration control technology under limited information is proposed. The technical problems such as heterogeneous/isomorphic UAV cluster collaboration, and wireless communication in complex channel environments should be overcome. By optimizing UAV swarm intelligence control strategies, information interaction mechanisms, and task decision-making collaboration mechanisms, the robustness of clustered unmanned systems should be enhanced. The adaptability of unmanned systems in complex underground environments should be improved. Furthermore, the task execution efficiency and success rate of unmanned systems should be improved

Exploring the Mechanical Anisotropy and Ideal Strengths of Tetragonal B4CO4

First-principles calculations were employed to study the mechanical properties for the recently proposed tetragonal B4CO4 (t-B4CO4). The calculated structural parameters and elastic constants of t-B4CO4 are in excellent agreement with the previous results, indicating the reliability of the present calculations. The directional dependences of the Young’s modulus and shear modulus for t-B4CO4 are deduced in detail, and the corresponding results suggest that the t-B4CO4 possesses a high degree of anisotropy. Based on the strain-stress method, the ideal tensile and shear strengths along the principal crystal directions are calculated, and the obtained results indicate that the shear mode along (001)[100] slip system dominates the plastic deformation of t-B4CO4, which can be ascribed to the breaking of the ionic B-O bonds. The weakest ideal shear strength of 27.5 GPa demonstrates that the t-B4CO4 compound is not a superhard material, but is indeed a hard material. Based on the atomic explanation that the ternary B-C-O compounds cannot acquire high ideal strength, we propose two possible routes to design superhard B-C-O compounds