Information Potential Fields Navigation in Wireless Ad-Hoc Sensor Networks

As wireless sensor networks (WSNs) are increasingly being deployed in some important applications, it becomes imperative that we consider application requirements in in-network processes. We intend to use a WSN to aid information querying and navigation within a dynamic and real-time environment. We propose a novel method that relies on the heat diffusion equation to finish the navigation process conveniently and easily. From the perspective of theoretical analysis, our proposed work holds the lower constraint condition. We use multiple scales to reach the goal of accurate navigation. We present a multi-scale gradient descent method to satisfy users’ requirements in WSNs. Formula derivations and simulations show that the method is accurately and efficiently able to solve typical sensor network configuration information navigation problems. Simultaneously, the structure of heat diffusion equation allows more flexibility and adaptability in searching algorithm designs

Hyperspectral Image Analysis through Unsupervised Deep Learning

Hyperspectral image (HSI) analysis has become an active research area in computer vision field with a wide range of applications. However, in order to yield better recognition and analysis results, we need to address two challenging issues of HSI, i.e., the existence of mixed pixels and its significantly low spatial resolution (LR). In this dissertation, spectral unmixing (SU) and hyperspectral image super-resolution (HSI-SR) approaches are developed to address these two issues with advanced deep learning models in an unsupervised fashion. A specific application, anomaly detection, is also studied, to show the importance of SU.Although deep learning has achieved the state-of-the-art performance on supervised problems, its practice on unsupervised problems has not been fully developed. To address the problem of SU, an untied denoising autoencoder is proposed to decompose the HSI into endmembers and abundances with non-negative and abundance sum-to-one constraints. The denoising capacity is incorporated into the network with a sparsity constraint to boost the performance of endmember extraction and abundance estimation.Moreover, the first attempt is made to solve the problem of HSI-SR using an unsupervised encoder-decoder architecture by fusing the LR HSI with the high-resolution multispectral image (MSI). The architecture is composed of two encoder-decoder networks, coupled through a shared decoder, to preserve the rich spectral information from the HSI network. It encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. And the angular difference between representations are minimized to reduce the spectral distortion.Finally, a novel detection algorithm is proposed through spectral unmixing and dictionary based low-rank decomposition, where the dictionary is constructed with mean-shift clustering and the coefficients of the dictionary is encouraged to be low-rank. Experimental evaluations show significant improvement on the performance of anomaly detection conducted on the abundances (through SU).The effectiveness of the proposed approaches has been evaluated thoroughly by extensive experiments, to achieve the state-of-the-art results

Hybrid Smart Transformer for Enhanced Power System Protection Against DC With Advanced Grid Support

The traditional grid is rapidly transforming into smart substations and grid assets incorporating advanced control equipment with enhanced functionalities and rapid self-healing features. The most important and strategic equipment in the substation is the transformer and is expected to perform a variety of functions beyond mere voltage conversion and isolation. While the concept of smart solid-state transformers (SSTs) is being widely recognized, their respective lifetime and reliability raise concerns, thus hampering the complete replacement of traditional transformers with SSTs. Under this scenario, introducing smart features in conventional transformers utilizing simple, cost-effective, and easy to install modules is a highly desired and logical solution. This dissertation is focused on the design and evaluation of a power electronics-based module integrated between the neutral of power transformers and substation ground. The proposed module transforms conventional transformers into hybrid smart transformers (HST). The HST enhances power system protection against DC flow in grid that could result from solar storms, high-elevation nuclear explosions, monopolar or ground return mode (GRM) operation of high-voltage direct current (HVDC) transmission and non-ideal switching in inverter-based resources (IBRs). The module also introduces a variety of advanced grid-support features in conventional transformers. These include voltage regulation, voltage and impedance balancing, harmonics isolation, power flow control and voltage ride through (VRT) capability for distributed energy resources (DERs) or grid connected IBRs. The dissertation also proposes and evaluates a hybrid bypass switch for HST module and associated transformer protection during high-voltage events at the module output, such as, ground faults, inrush currents, lightning and switching transients. The proposed strategy is evaluated on a scaled hardware prototype utilizing controller hardware-in-the-loop (C-HIL) and power hardware-in-the-loop (P-HIL) techniques. The dissertation also provides guidelines for field implementation and deployment of the proposed HST scheme. The device is proposed as an all-inclusive solution to multiple grid problems as it performs a variety of functions that are currently being performed through separate devices increasing efficiency and justifying its installation

A new blind signal separation algorithm for instantaneous MIMO system

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. In this paper, we propose a new constant modulus (CM)-based algorithm which employ nonlinear function as the de-correlation term. Moreover, it is shown by theoretical analysis that the proposed algorithm has less mean square error (MSE), i.e., better separation performance, in steady state than the cross-correlation and constant modulus algorithm (CC-CMA). Numerical simulations show the effectiveness of the proposed result.<br /

Hierarchical Spatial Gossip for Multiresolution Representations in Sensor Networks

In this paper we propose a lightweight algorithm for constructing multi-resolution data representations for sensor networks. At each sensor node u, we compute, O(logn) aggregates about exponentially enlarging neighborhoods centered at u. The ith aggregate is the aggregated data from nodes approximately within 2 i hops of u. We present a scheme, named the hierarchical spatial gossip algorithm, to extract and construct these aggregates, for all sensors simultaneously, with a total communication cost of O(npolylogn). The hierarchical gossip algorithm adopts atomic communication steps with each node choosing to exchange information with a node distance d away with probability ∼ 1/d 3. The attractiveness of the algorithm attributes to its simplicity, low communication cost, distributed nature and robustness to node failures and link failures. We show in addition that computing multi-resolution aggregates precisely (i.e., each aggregate uses all and only the nodes within 2 i hops) requires a communication cost of Ω(n √ n), which does not scale well with network size. An approximate range in aggregate computation like that introduced by the gossip mechanism is therefore necessary in a scalable efficient algorithm. Besides the natural applications of multi-resolution data summaries in data validation and information mining, we also demonstrate the application of the pre-computed multi-resolution data summaries in answering range queries efficiently

Techniques for Frequency Synthesizer-Based Transmitters.

Internet of Things (IoT) devices are poised to be the largest market for the semiconductor industry. At the heart of a wireless IoT module is the radio and integral to any radio is the transmitter. Transmitters with low power consumption and small area are crucial to the ubiquity of IoT devices. The fairly simple modulation schemes used in IoT systems makes frequency synthesizer-based (also known as PLL-based) transmitters an ideal candidate for these devices. Because of the reduced number of analog blocks and the simple architecture, PLL-based transmitters lend themselves nicely to the highly integrated, low voltage nanometer digital CMOS processes of today. This thesis outlines techniques that not only reduce the power consumption and area, but also significantly improve the performance of PLL-based transmitters.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113385/1/mammad_1.pd