Dept. of Electrical and Computer Engineering
http://hdl.handle.net/2142/8887
Fri, 05 Feb 2016 22:30:15 GMT2016-02-05T22:30:15ZLoss in Vertical-Cavity Source-Emitting Lasers as a Result of Impurities
http://hdl.handle.net/2142/88382
Loss in Vertical-Cavity Source-Emitting Lasers as a Result of Impurities
Tai, Charlene
Vertical-cavity source emitting lasers (VCSELs) are useful for optical communication light sources because of their low cost and operating power. An understanding of the cavity optical loss is necessary in order to best optimize the design and performance of the devices. Using a method which measures the sub-threshold emission spectra, the loss can be quantitatively calculated. With this, optical loss of VCSELs with varying doping levels can be evaluated. With a semiconductor parameter analyzer, light output power as a function of injection current is measured for each different set of VCSELs to find the threshold current. The resulting current density can be found for each laser mesa size and the point where loss becomes independent of the aperture size can be identified. Using and optical spectrum analyzer, the spectral separation between the cold-cavity fundamental mode and the first higher-order mode can then be found without thermal effects by measuring the spectral splitting for each VCSEL biased at an injection current of approximately 0.9 times the threshold. The spectral splitting of small diameter lasers can be used to calculate the optical loss using the Helmholtz wave equation with an imaginary refractive index. The field amplitude loss can be extracted from the imaginary part of the resulting wavenumber. Two VCSEL samples with different doping are measured in this study. The calculated size-dependent loss is found to be the same for the two samples and thus it seems that that effects of the different doping are not sufficient to affect the cavity loss. It is, however, clearly shown that the introduction of impurities to create a doped pn junction does indeed create optical loss.
vertical cavity surface emitting laser; semiconductor laser; optical loss; oxide-confined micro-cavity
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883822014-12-01T00:00:00ZTai, CharleneNeutral wind field model estimation and optimal layout determination
http://hdl.handle.net/2142/88381
Neutral wind field model estimation and optimal layout determination
Yue, Chenshuo
The project introduces three different but related processes for neutral wind field estimation and optimization of sensor layout for a network of Fabry–Pérot interferometers, which are instruments for collecting wind speed and temperature of thermospheric wind fields. A mathematical model is presented to estimate the thermospheric neutral wind field. Algorithms are introduced to find the optimal layout for neutral wind field estimation without assuming anything about the original wind field. The fundamental theory behind the algorithm design is based in linear algebra. Further exploration of the optimal layout for simulation of the neutral wind field leads to the development of an alternative method. The fundamental principle underlying this new method comes from advanced linear algebra, regularization, which is a process to solve ill-conditioned or singular linear systems. The quadratic assumption of the neutral wind field form shows great performance if the wind field is very simple. The regularization method to simulate the neutral wind has obvious advantages if the neutral wind field is in different form, since the regularization method could be used to simulate the wind field with undetermined form. Also, the regularization method could be adjusted to simulate the wind field with various preferences, such as smoothness. The test on North American Thermosphere-Ionosphere Observation Network, a network of 5 FPIs in United States, demonstrated the performance of the optimal layout we currently generated by greedy algorithm. In general, the optimal layout shows a pretty good noise bearing ability. Also, with fewer measurements collected by the FPIs, we are still able to get a simulated neutral wind containing a lot of useful information.
thermospheric wind field simulation; sensor layout optimization
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883812014-12-01T00:00:00ZYue, ChenshuoInvestigation on Double Negative Metamaterials
http://hdl.handle.net/2142/88380
Investigation on Double Negative Metamaterials
To, Oscar
Metamaterials, materials that exhibit electromagnetic properties that are not naturally achievable, are currently a hot topic in research. Of the domain of metamaterials, major research efforts have focused on double negative metamaterials (DNM), materials that demonstrate a negative index of refraction, due to their potential applications such as superlens and cloaking. Moreover, passive metamaterials--materials that do not require additional energy to exhibit exotic properties--have demonstrated qualities beneficial as a DNM. Due to the nature of DNM, the atypical electromagnetic properties can only be seen over a finite range of frequencies. Current experimental realizations of DNM have been demonstrated in infrared wavelengths and radio frequency to millimeter waves; there have been no DNMs experimentally validated over the visual frequency spectrum. In this thesis, we propose a design of a DNM that displays negative refraction at the visual spectrum and validate the design experimentally. The design utilizes a layer of close-packed, precisely size-controlled, uniform nanospheres deposited on a layer of thin metallic film. We model the reflectance and transmittance of the design through the 3D finite-difference time-domain (FDTD) and 3D discontinuous Galerkin time-domain (DGTD) method. The theory behind our design and the results of the simulations and experiments will be discussed here.
metamaterial; nanospheres; FDTA; DGTD; double negative metamaterial
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883802014-12-01T00:00:00ZTo, OscarMethod of Moments (MoM): Application for Solving Augmented Electric Field Integral Equation (AEFIE)
http://hdl.handle.net/2142/88379
Method of Moments (MoM): Application for Solving Augmented Electric Field Integral Equation (AEFIE)
Li, Binye
Surface integral equations (SIEs) are promising candidates for modeling circuits because they
reduce degrees of freedom by restricting physical unknowns on the surface, which simplifies
complex structures. However, there are still challenges related to achieving stability over a broad
frequency band. Specifically, the low frequency breakdown of electrical field integral equation
(EFIE) operator is discussed in this work. In order to solve or alleviate this problem, the
separation of irrotational and solenoidal current must be accomplished. A proposed method, the
Augmented Electrical Field Integral Equation (AEFIE), is intended to separate the current
element by introducing charge as another variable and relate irrotational current and the charge
vector. Finally, the method of moments (MoM) is applied to solve the integral equation by
projecting the current onto RWG basis and performing subspace projections to fill out the
integral equation operator matrix. For complicated circuit structure, MoM can be accelerated
using the fast multipole algorithm (FMA).
Computational Electromagnetics; EFIE; MoM
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883792014-12-01T00:00:00ZLi, BinyeCharacterization of Dynamic Solar Insulation for Photovoltaic Systems
http://hdl.handle.net/2142/88377
Characterization of Dynamic Solar Insulation for Photovoltaic Systems
Serna, Rodrigo
This thesis describes the implementation of an experimental setup to
capture the maximum power point data of photovoltaic (PV) modules. The
experimental setup operated for a one and a half year period, and the data
collected is analyzed in this paper to determine if it is possible to maximize
power conversion by varying the step and interval parameters of maximum
power point tracking (MPPT) algorithms for each day. For each day, short
circuit current data is collected at a high frequency (2 kHz) and full sweeps
of the PV IV curve is done about every three seconds. The large amounts of
data are broken down and each sweep is parameterized and then the perturb
and observe algorithm is run over the data for different interval and step
parameter sets. The conclusion is that it is possible to maximize the power
conversion over the whole year, though further analysis is currently needed to
ascertain to what extent this is possible.
Photovoltaics; Maximum Power Point Tracking; Optimization
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883772014-12-01T00:00:00ZSerna, RodrigoDesigning Radio Frequency Integrated Circuits (RFICs) Based on Low Loss Chip-Scale Lumped Elements
http://hdl.handle.net/2142/88376
Designing Radio Frequency Integrated Circuits (RFICs) Based on Low Loss Chip-Scale Lumped Elements
Li, Zijian
Over the years the thirst for achieving better filter designs with low insertion loss in RFICs has gradually become unquenchable. Today, cell phones manufactured from different brands all require distinct frequency ranges of the transmitted signal. In order to select the wanted frequencies within a certain range while efficiently rejecting and attenuating the unwanted frequencies outside that range, a band-pass filter is needed. It is imperative to choose the correct prototype of band-pass filter when designing. Typical band-pass filters can be classified into four groups: Butterworth, Chebyshev, Elliptic and Bessel. In this paper, Butterworth filter prototype serves as the main design technique.
This thesis revolves around the following general questions that concern the testing, modeling and designing of the band-pass filter:
1. What order of Butterworth filter should be chosen to minimize the insertion loss?
2. What is the difference between ideal reactive components and non-ideal reactive components at high frequencies?
3. Is there an internal relation between Quality Factor, pass band bandwidth and insertion loss? Can this be derived into a closed form equation?
4. What changes should be made when switching from the theoretical simulation to the hands-on PCB board fabrication?
5. Lastly, is there any mismatch between simulation and real measurement? If yes, why are there mismatches and how can they be fixed?
RF/microwave filter; insertion loss; Butterfield filter
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883762014-12-01T00:00:00ZLi, ZijianDesigning Microstrip Band Filter at 2.4 GHz
http://hdl.handle.net/2142/88375
Designing Microstrip Band Filter at 2.4 GHz
Kim, Doyoun
Bandpass filter’s role in the systems of wireless communication has been highlighted in
current days. The filtration of the signals that are being transferred and received has to take place
at a specific frequency that contains attenuation of a certain amount and a specified bandwidth.
The initial stage of designing a microstrip filter is the performance of the approximated
calculation. This in particular has to be based on the utilization of lumped essentials, for instance
capacitors and inductors. It is through Agilent Advanced Design System that is centered on
specific parameter calculation that the performance of filter computational verification is done.
The printed circuit board is utilized in the fabrication of the transformation of the filter structure
that contains a quarter-wave. The major objective of this research was to use the theoretical and
designed filter experimental values to determine the closeness between the fabricated
measurement and theoretical simulation.
fabrication of printed circuit board; microstrip containing quarter wave transformation and Bandpass filter
Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883752014-12-01T00:00:00ZKim, DoyounA Dynamic Spectral Light Modulation Unit with Optical Feedback Control for Optogenetic Stimulation
http://hdl.handle.net/2142/88361
A Dynamic Spectral Light Modulation Unit with Optical Feedback Control for Optogenetic Stimulation
Yu, Haichuan
With recent advances in optical stimulation delivery to optogenetically modified neurons, individual or groups of neurons can be targeted with incredible spatio-temporal accuracy. It is enough to excite the functional neuronal electrophysiology in its native scale, making possible the study of functional neural circuitry. However, excitation of multiple neurons and distinct combinations of neurons within a neuronal circuit with laser optical stimulation has not yet been demonstrated. This work presents an optical feedback control unit that fills this need. The unit synthesizes user input and optical information in a continuous feedback loop to develop a control mask for the laser excitation. The resulting unit can perform complex real-time patterning of laser stimulation, and can be added on to experiments in neuroscience with some adaptation to native equipment.
optogenetics, neuron, optical, spatial, temporal, feedback, control, light, sculpting, patterning, SLM
Thu, 01 May 2014 00:00:00 GMThttp://hdl.handle.net/2142/883612014-05-01T00:00:00ZYu, HaichuanAdaptive sparse representations and their applications
http://hdl.handle.net/2142/88322
Adaptive sparse representations and their applications
Ravishankar, Saiprasad
The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal processing, image processing, and medical imaging. Analytical sparsifying transforms such as Wavelets and DCT have been widely used in compression standards. Recently, the data-driven learning of synthesis sparsifying dictionaries has become popular especially in applications such as denoising, inpainting, and compressed sensing. While there has been extensive research on learning synthesis dictionaries and some recent work on learning analysis dictionaries, the idea of learning sparsifying transforms has received no attention. In the first part of this thesis, we study the sparsifying transform model and its relationship to prior linear sparse models. Then, we propose novel problem formulations for learning square sparsifying transforms from data. The proposed algorithms for transform learning alternate between a sparse coding step and a transform update step, and are highly efficient.
Specifically, as opposed to sparse coding in the synthesis or noisy analysis models which is NP-hard, the sparse coding step in transform learning can be performed exactly and cheaply by zeroing out all but a certain number of nonzero transform coefficients of largest magnitude.
The transform update step is performed using iterative conjugate gradients.
The proposed algorithms give rise to well-conditioned square sparsifying transforms in practice. We show the superiority of our approach over analytical sparsifying transforms such as the DCT for signal and image representation. We also show promising performance in signal denoising using the learned sparsifying transforms. The proposed approach is much faster than previous approaches involving learned synthesis, or analysis dictionaries.
Next, we explore a specific structure for learned sparsifying transforms, that enables efficient implementations.
Following up on the idea of learning square sparsifying transforms, we propose novel problem formulations for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learned, stored, and implemented efficiently. We show the superior promise of our learned doubly sparse transforms as compared to analytical sparsifying transforms such as the DCT or Wavelets for image representation.
Adapted doubly sparse transforms also generalize better than the ‘unstructured’ (or non-sparse) transform.
We show promising performance and speedups in image denoising using the learned doubly sparse transforms compared to approaches involving learned synthesis dictionaries such as the K-SVD algorithm.
In the third part of this thesis, we further develop the alternating algorithms for learning unstructured (non-sparse) well-conditioned, or orthonormal square sparsifying transforms.
While, in the first part of the thesis, we provided an iterative method involving conjugate gradients for the transform update step, in this part, we instead derive efficient and analytical closed-form solutions for transform update. Importantly, we establish that the proposed algorithms are globally convergent to the set of local minimizers of the non-convex transform learning problems. In practice, our algorithms are shown to be insensitive to initialization.
In the next part of the thesis, we focus on compressed sensing (CS), which exploits the sparsity of images or image patches in a transform domain or synthesis dictionary to reconstruct images from highly undersampled or compressive measurements. Specifically, we focus on the subject of blind compressed sensing, where the underlying sparsifying transform is unknown a priori, and propose a framework to simultaneously reconstruct the underlying image(s)/volume(s) as well as the square sparsifying transform from highly undersampled measurements. The proposed block coordinate descent type algorithms involve highly efficient closed-form optimal updates. Importantly, we prove that although the proposed blind compressed sensing formulations are highly nonconvex, our algorithms converge to the set of critical points of the objectives defining the formulations.
We illustrate the usefulness of the proposed framework for magnetic resonance image (MRI) reconstruction from highly undersampled k-space measurements. As compared to previous state-of-the-art methods involving the synthesis model, our approach is 10x faster for reconstructing 2D MR images, while also providing promising reconstruction quality. The proposed transform-based blind compressed sensing has the potential to revolutionize medical imaging technologies by highly accelerating both the imaging and image reconstruction processes.
In the fifth part of this thesis, we study the design of sampling schemes for compressed sensing MRI.
The (pseudo) random sampling schemes used most often for CS may have good theoretical asymptotic properties; however, with limited data they may be far from optimal. Therefore, we propose a novel framework for improved adaptive sampling schemes for highly undersampled CS MRI.
While the proposed framework is general, we apply it with some recent MRI reconstruction algorithms.
Numerical experiments demonstrate that our adaptive sampling scheme can provide significant improvements in image reconstruction quality for MRI compared to non-adapted methods.
In the next part of the thesis, we develop a methodology for online learning of square sparsifying transforms. Such online learning is particularly useful when dealing with big data, and for signal processing applications such as real-time sparse representation and denoising. The proposed transform learning algorithms are shown to have a much lower computational cost than online synthesis dictionary learning. In practice, the sequential learning of a sparsifying transform typically converges much faster than batch mode transform learning. Preliminary experiments show the usefulness of the proposed schemes for sparse representation (compression), and denoising. We also prove that although the associated optimization problems are non-convex, our online transform learning algorithms are guaranteed to converge to the set of stationary points of the learning problem. The guarantee relies on few (easy to verify) assumptions.
In the seventh part of this thesis, we propose a novel convex formulation for doubly sparse square transform learning. The proposed formulation has similarities to traditional least squares optimization with $\ell_1$ regularization. Our convex learning algorithm is a modification of FISTA, and is guaranteed to converge to a global optimum, and moreover converges quickly. We also study two non-convex variants of the proposed convex formulation, and provide local convergence proof for the algorithm for one of them. These proposed non-convex variants use the $\ell_0$ ``norm" for measuring the sparsity of the transform and/or sparse code.
We show the superior promise of our learned transforms here as compared to analytical sparsifying transforms such as the DCT for image representation. In these examples, the performance is sometimes comparable to the previously proposed non-convex (non guaranteed) doubly sparse transform learning schemes.
While we studied the learning of square transforms in the initial parts of the thesis, in the eighth part of the thesis, we instead briefly study the learning of tall or overcomplete sparsifying transforms from data. We propose various penalties that control the sparsifying ability, condition number, and incoherence of the learned transforms. Our alternating algorithm for overcomplete transform learning converges empirically, and significantly improves the quality of the learned transform over the iterations. We present examples demonstrating the promising performance of adaptive overcomplete transforms over adaptive overcomplete synthesis dictionaries learned using the popular K-SVD algorithm, in the application of image denoising. The overcomplete transforms also denoise better than adaptive square transforms.
In the final part of the thesis, we explore the idea of learning efficient structured overcomplete sparsifying transforms. Since natural images typically contain diverse textures that cannot be sparsified well by a single transform, we therefore propose a union of sparsifying transforms model. Sparse coding in this model reduces to a form of transform-domain clustering. This makes the model appealing for classification tasks. The proposed model is also equivalent to a structured overcomplete sparsifying transform model with block cosparsity, dubbed OCTOBOS. The alternating algorithm introduced for learning such transforms involves simple closed-form solutions. A theoretical analysis provides a convergence guarantee for this algorithm. It is shown to be globally convergent to the set of partial minimizers of the non-convex OCTOBOS (or, union of transforms) learning problem. We also show that under certain conditions, the algorithm converges to the set of stationary points of the overall objective. When applied to images, the algorithm learns a collection of well-conditioned square transforms, and a good clustering of patches or textures. The resulting sparse representations for the images are much better than those obtained with a single learned transform, or with analytical transforms. We show the promising performance of the proposed approach in image denoising, which compares quite favorably with approaches involving a single learned square transform or an overcomplete synthesis dictionary, or Gaussian mixture models. The proposed denoising method is also faster than the synthesis dictionary based approach.
Inverse problems; Computer vision; Classification; Structured overcomplete transform learning; Union of transforms; Overcomplete transform learning; Structured transforms; Convex formulation; Real-time applications; Big data; Online learning; Adaptive sampling; Image reconstruction; Block Coordinate descent; Blind compressed sensing; Convergence guarantees; Efficient updates; Closed-form solutions; Machine learning; Nonconvex optimization; Alternating minimization; Doubly sparse transform learning; Square transform learning; Adaptive sparse models; Denoising; dictionary learning; Magnetic resonance imaging; Compressed sensing; Sparse representations; Sparsifying transform learning
Fri, 05 Dec 2014 00:00:00 GMThttp://hdl.handle.net/2142/883222014-12-05T00:00:00ZRavishankar, SaiprasadA characteristic mode perturbation approach for antenna loading design
http://hdl.handle.net/2142/88289
A characteristic mode perturbation approach for antenna loading design
Young, Matthew William
Reactive loading is commonly applied to antennas to modify input impedance and radiation pattern properties. However, reactive loading design approaches based on experience, intuition, and modeling are challenged as the demand grows for antennas with increased functionality and performance. New systematic design methods are needed that can manage complicated performance tradeoffs while providing physical insight into the fundamental antenna operation. Characteristic mode theory has shown promise for systematic antenna design, yet significant limitations still exist that restrict its usefulness. The transformations of characteristic modes with respect to frequency or reactive loading are currently understood only qualitatively, and insight into their behavior must be developed through experience. In this thesis, a novel characteristic mode perturbation approach is developed that provides a quantitative description of how mode eigenvalues and eigencurrents transform under reactive loading and frequency variation. Analytical equations are derived using a novel application of eigenvalue perturbation theory to the characteristic mode problem. The equations characterize the effect of impedance loading on the characteristic mode eigenvalues and eigencurrents and reveal the explicit factors governing the mode transformations.
Insight from the perturbation equations suggests a new characteristic mode design paradigm in which loading is used to control the eigencurrent contributions between modes. The new eigencurrent contribution perspective can be used to understand and solve loading problems that traditional characteristic mode theory cannot. The approach is used to design the reactive loading of an Archimedean spiral antenna to produce beam tilt or an endfire radiation pattern while maintaining desirable VSWR properties. Finally, the perturbation approach is used to analyze antenna loss and is applied to the analysis and improvement of antenna radiation efficiency.
Antenna design; Characteristic mode theory; Perturbation theory; Spiral antenna; Reactive loading; Radiation efficiency; Compensation theorem
Thu, 16 Jul 2015 00:00:00 GMThttp://hdl.handle.net/2142/882892015-07-16T00:00:00ZYoung, Matthew William