Long-Range Indoor Emitter Localization from 433MHz and 2.4GHz WLAN Received Signal Strengths

An improved search method for localizing a radio emitter in a building from its signal strength is proposed and implemented. It starts from floor level determination, which samples the signal strength on each floor and determines the floor level of the emitter. Then the search is conducted iteratively on a specific floor. For each round of search, one-dimensional (1-D) or two-dimensional (2-D) signal strength is collected according to the actual structure of the floor. The signal strength data are processed to fit a 1-D curve or a 2-D surface with regression models to establish an indicator or trend, which can either locate the emitter or provide direction for the next round of search. The main contribution of this thesis is that the data processing results for 2- D signal strength data can locate the emitter or show the direction of the emitter through gradient, which is helpful to future search. Our approach has been implemented with two wireless protocols: 433MHz protocol and 2.4GHz wireless local area network (WLAN) protocol. A 433MHz module with LoRa modulation is chosen to provide long propagation distance. A 2.4GHz WLAN tester is used for close range search where 433MHz signal does not show enough attenuation spread to be effective. 433MHz implementation consists of an emitter, a radio tester and an Android APP on a smartphone. The emitter is a board with an Arduino Uno and a 433MHz transceiver. The radio tester is a board with an Arduino Uno, a 433MHz transceiver and a Bluetooth-to-serial module to communicate with a smartphone. The radio tester and the APP work together to localize the emitter. 2.4GHz WLAN implementation is composed of an emitter, which is emulated with a smartphone, a radio tester which consists of a smartphone, and a router and two Android APPs. Both phones are connected through the router and socket communication is initiated with the radio tester working as a server and the emitter working as a client. The APP on the emitter implements the client functions. The radio tester controls data acquisition process. The APP on the tester establishes the server functions and deals with received data. It compares signal strengths in different locations and finds the position that has the strongest signal strength to locate the emitter. The innovative idea of this thesis is to use 1-D and 2-D signal strength with regression models as it is convenient to provide location or unique search direction of the emitter. 1-D data is processed with linear and polynomial regressions to fit curves in order to find possible location of the emitter in either a narrow strip or a half a plane. 2-D data is processed with multiple regressions to fit contour-line surfaces in order to find either location of the emitter on the top of a surface or a unique search direction of the location of the emitter as indicated by the highest surface gradient. Our approach is compared with the centroid algorithm with an example. The centroid algorithm assumes the emitter is located in the search area and it is also easily influenced by sampling location biases. Our approach has two advantages over the centroid algorithm. The first advantage is that our approach can work even when the emitter is out of the initial search area since it searches iteratively. The second advantage is that when the emitter is in the initial search area, our approach is not influenced by sampling location biases

SSL Framework for Causal Inconsistency between Structures and Representations

The cross-pollination of deep learning and causal discovery has catalyzed a burgeoning field of research seeking to elucidate causal relationships within non-statistical data forms like images, videos, and text. Such data, often being named `indefinite data', exhibit unique challenges-inconsistency between causal structure and representation, which are not common in conventional data forms. To tackle this issue, we theoretically develop intervention strategies suitable for indefinite data and derive causal consistency condition (CCC). Moreover, we design a self-supervised learning (SSL) framework that considers interventions as `views' and CCC as a `philosophy' with two implement examples on Supervised Specialized Models (SSMs) and Large Language Models (LLMs), respectively. To evaluate pure inconsistency manifestations, we have prepared the first high-quality causal dialogue dataset-Causalogue. Evaluations are also performed on three other downstream tasks. Extensive experimentation has substantiated the efficacy of our methodology, illuminating how CCC could potentially play an influential role in various fields

The Algorithmic Phase Transition of Random Graph Alignment Problem

We study the graph alignment problem over two independent Erd\H{o}s-R\'enyi graphs on $n$ vertices, with edge density $p$ falling into two regimes separated by the critical window around $p_c=\sqrt{\log n/n}$. Our result reveals an algorithmic phase transition for this random optimization problem: polynomial-time approximation schemes exist in the sparse regime, while statistical-computational gap emerges in the dense regime. Additionally, we establish a sharp transition on the performance of online algorithms for this problem when $p$ lies in the dense regime, resulting in a $\sqrt{8/9}$ multiplicative constant factor gap between achievable and optimal solutions.Comment: 50 page

Low-Degree Hardness of Detection for Correlated Erd\H{o}s-R\'enyi Graphs

Given two Erd\H{o}s-R\'enyi graphs with $n$ vertices whose edges are correlated through a latent vertex correspondence, we study complexity lower bounds for the associated correlation detection problem for the class of low-degree polynomial algorithms. We provide evidence that any degree-$O(\rho^{-1})$ polynomial algorithm fails for detection, where $\rho$ is the edge correlation. Furthermore, in the sparse regime where the edge density $q=n^{-1+o(1)}$, we provide evidence that any degree-$d$ polynomial algorithm fails for detection, as long as $\log d=o\big( \frac{\log n}{\log nq} \wedge \sqrt{\log n} \big)$ and the correlation $\rho<\sqrt{\alpha}$ where $\alpha\approx 0.338$ is the Otter's constant. Our result suggests that several state-of-the-art algorithms on correlation detection and exact matching recovery may be essentially the best possible.Comment: 40 page

Humanitarian architecture and the creation of schools for disadvantaged communities in China

In the past few decades, there has been increasing global concern for people in developing countries who are suffering from disasters, diseases, poverty, etc. As a result, many charitable organizations, architects, universities, and others have become involved in humanitarian projects to help alleviate these social problems by utilizing architectural design skills. Humanitarian architecture provides solutions in response to natural- and man-made societal or environmental problems and its scope can be broadly classified into two categories: ‘post-disaster’ and ‘socio-economic community development’ projects. Significantly, in recent years there have been also increasing concerns about the roles and responsibilities of architects and the appropriateness of humanitarian architecture. Similar to the global picture, research into post-disaster humanitarian architecture in China has increased significantly in recent years, however, research into ‘non-disaster relief’ projects focused on socio-economic development is rare. The rapidly developing economy in China has resulted in the gap between rich and poor becoming wider particularly in relation to the standard of education and school facilities between urban and rural areas. This situation has particularly affected migrant workers, who are the products of the unique urban-rural system in China. They move to cities for employment opportunities leaving their rural ‘left-behind children’ in their hometowns or villages. The topic of this thesis is Humanitarian Architecture and the Creation of Schools for Disadvantaged Communities in China. The central aim of this PhD research is to understand and help improve the non-disaster relief humanitarian architecture process in the provision of schools in the disadvantaged communities of China. The research in this thesis has approached the phenomenon of humanitarian school projects for underdeveloped communities in China by focusing on three specific case studies, the Bridge School project, the Xiashan Primary School project, and the Xiuning Shuanglong Primary School project. The research methodology of this PhD research is qualitative research method with case studies, where documentations, interviews, participant observation are the main methods to carry out the case studies. This thesis has also articulated the delivery mechanisms for each project, issues and challenges, and the impact on the local community through a comparison of the three projects. The research project’s methodology, research methods application, and the potential gaps and weaknesses has been examined to draw lessons in relation to future of research into humanitarian architecture. This thesis has provided an extensive literature review on humanitarian architecture in relation of schools in the Chinese context at a PhD level. It has provided literature reviews on what is humanitarian architecture, the history of humanitarian architecture, the arguments on humanitarian architecture along the history and how current humanitarian architecture projects are working in terms of both global context and Chinese context and it discussed the potentials to propose an improved model for the practice of humanitarian architecture in China

Implicitizing Rational Curves by the Method of Moving Algebraic Curves

AbstractA functionF(x,y,t)that assigns to each parametertan algebraic curveF(x,y,t)=0is called a moving curve. A moving curveF(x,y,t)is said to follow a rational curvex=x(t)/w(t),y=y(t)/w(t)ifF(x(t)/w(t), y(t)/w(t),t)is identically zero. A new technique for finding the implicit equation of a rational curve based on the notion of moving conics that follow the curve is investigated. For rational curves of degree 2nwith no base points the method of moving conics generates the implicit equation as the determinant of ann×nmatrix, where each entry is a quadratic polynomial inxandy, whereas standard resultant methods generate the implicit equation as the determinant of a 2n× 2nmatrix where each entry is a linear polynomial inxandy. Thus implicitization using moving conics yields more compact representations for the implicit equation than standard resultant techniques, and these compressed expressions may lead to faster evaluation algorithms. Moreover whereas resultants fail in the presence of base points, the method of moving conics actually simplifies, because when base points are present some of the moving conics reduce to moving lines