BotSpine - A Generic Simple Development Platform of Smartphones and Sensors or Robotics

The Internet of Things (IoT) emergence leads to an “intelligence” technology revolution in industrial, social, environmental and almost every aspect of life and objectives. Sensor and actuators are heavily employed in industrial production and, under the trend of IoT, smart sensors are in great demand. Smartphones stand out from other computing terminals as a result of their incomparable popularity, mobility and computer comparable computing capability. However, current IoT designs are developed among diverse platforms and systems and are usually specific to applications and patterns. There is no a standardized developing interface between smartphones and sensors/electronics that is facile and rapid for either developers or consumers to connect and control through smartphones. The goal of this thesis is to develop a simple and generic platform interconnecting smartphones and sensors and/or robotics, allowing users to develop, monitor and control all types of sensors, robotics or customer electronics simply over their smartphones through the developed platform. The research is in cooperation with a local company, Environmental Instruments Canada Inc. From the perspective of research and industrial interests, the proposed platform is designed for generally applicable, low cost, low energy, easily programmed, and smartphone based sensor and/or robotic development purposes. I will build a platform interfacing smartphones and sensors including hardware, firmware structures and software application. The platform is named BotSpine and it provides an energy-efficient real-time wireless communication. This thesis also implements BotSpine by redesigning a radon sniffer robot with the developed interface, demonstrated that BotSpine is able to achieve expectations. BotSpine performs a fast and secure connection with smartphones and its command/BASIC program features render controlling and developing robotics and electronics easy and simple

On a neural network approach for solving potential control problem of the semiclassical Schr\"odinger equation

Robust control design for quantum systems is a challenging and key task for practical technology. In this work, we apply neural networks to learn the control problem for the semiclassical Schr\"odinger equation, where the control variable is the potential given by an external field that may contain uncertainties. Inspired by a relevant work [29], we incorporate the sampling-based learning process into the training of networks, while combining with the fast time-splitting spectral method for the Schr\"odinger equation in the semiclassical regime. The numerical results have shown the efficiency and accuracy of our proposed deep learning approach

Path-dependent McKean-Vlasov equation: strong well-posedness and convergence of an interpolated Euler scheme

We consider the path-dependent McKean-Vlasov equation, in which both the drift and the diffusion coefficients are allowed to depend on the whole trajectory of the process up to the current time t, and depend on the corresponding marginal distributions. We prove the strong well-posedness of the equation in the Lp setting, p greater than 2, locally in time. Then, we introduce an interpolated Euler scheme, a key object to simulate numerically the process, and we prove the convergence of this scheme towards the strong solution in the Lp norm. Our result is quantitative and provides an explicit rate. As applications we give results for two mean-field equations arising in biology and neuroscience.Comment: 33 page

Sparse topic modeling via spectral decomposition and thresholding

The probabilistic Latent Semantic Indexing model assumes that the expectation of the corpus matrix is low-rank and can be written as the product of a topic-word matrix and a word-document matrix. In this paper, we study the estimation of the topic-word matrix under the additional assumption that the ordered entries of its columns rapidly decay to zero. This sparsity assumption is motivated by the empirical observation that the word frequencies in a text often adhere to Zipf's law. We introduce a new spectral procedure for estimating the topic-word matrix that thresholds words based on their corpus frequencies, and show that its $\ell_1$-error rate under our sparsity assumption depends on the vocabulary size $p$ only via a logarithmic term. Our error bound is valid for all parameter regimes and in particular for the setting where $p$ is extremely large; this high-dimensional setting is commonly encountered but has not been adequately addressed in prior literature. Furthermore, our procedure also accommodates datasets that violate the separability assumption, which is necessary for most prior approaches in topic modeling. Experiments with synthetic data confirm that our procedure is computationally fast and allows for consistent estimation of the topic-word matrix in a wide variety of parameter regimes. Our procedure also performs well relative to well-established methods when applied to a large corpus of research paper abstracts, as well as the analysis of single-cell and microbiome data where the same statistical model is relevant but the parameter regimes are vastly different

A Natural Wind Defrosting, Nano-coated Antibacterial Self-cleaning Energy-saving Health Air-cooled Refrigerator

The air-cooled frost-free household refrigerator is popular in the market because of its large size and frost-free size. However, the evaporator defrost process consumes a large amount of electrical energy to limit the wide spread of this refrigerator, at the same time because of its structural problems, resulting in its evaporator, air duct can not be artificially cleaned, leading to the growth of bacteria, pollution of food storage. This research has developed a self-cleaning energy-saving health refrigerator that uses indoor natural wind defrosting, ultra-hydrophilic nano-titanium dioxide coating photocatalytic sterilization and sterilization. After experimental comparison, under the same operating time of the same operating conditions, the refrigeration mode saves 1.5%, the defrost process saves 95%, reduces the amount of frosting by 23%, the temperature changes of the freezer is less than 7 ℃ , and the desterilization rate of nano-coated reaches 80%