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DTSTART;TZID=America/New_York:20210804T100000
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DTSTAMP:20260512T144411
CREATED:20210727T185426Z
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UID:26745-1628071200-1628074800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Amirreza Farnoosh
DESCRIPTION:PhD Dissertation Defense: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data \nAmirreza Farnoosh \nLocation: Zoom Link \nAbstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data\, recorded from multimodal sensors of heterogeneous natures\, in various application domains\, ranging from medicine and biology to robotics and traffic control. In this dissertation\, we propose frameworks for learning the underlying representation of these data in an unsupervised manner\, tailored towards several emerging applications\, namely indoor navigation and mapping\, neuroscience hypothesis testing\, time series forecasting\, 3D motion segmentation\, and human action recognition.\nAs such\, (1) we developed an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduced a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially-dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We developed a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically\, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics\, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting. (4) We developed a dynamical deep generative latent model for segmentation of 3D pose data over time that parses the meaningful intrinsic states in the dynamics of these data and enables a low-level dynamical generation and segmentation of skeletal movements. Our model encodes highly correlated skeletal data into a set of few spatial basis of switching temporal processes in a low-dimensional latent framework. We extended this model for human action recognition by decoding from these low-dimensional latents to the motion data and their associated action labels.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-amirreza-farnoosh/
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DTSTART;TZID=America/New_York:20210804T100000
DTEND;TZID=America/New_York:20210804T110000
DTSTAMP:20260512T144411
CREATED:20210729T142123Z
LAST-MODIFIED:20210729T142123Z
UID:26759-1628071200-1628074800@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Owen McElhinney
DESCRIPTION:MS Thesis Defense: On the Application of Spline Functions to Problems in Hyperspectral Imaging \nOwen McElhinney \nLocation: Zoom Link \nAbstract: Hyperspectral Imaging (HSI) is a rapidly growing topic in the field of remote sensing. Hyperspectral cameras trade off a reduction in the spatial resolution of modern imaging for a higher spectral resolution. This allows for the detection of surface materials using the principles of spectroscopy.\nThis work will investigate the application of a class of functions called splines to three different problems in the field of HSI. Splines are a special class of data-fitting functions that guarantee continuity. Common data-fitting techniques like polynomial and piecewise-polynomial fitting are unable to match the complexity of HSI data. Splines provide a robust fitting procedure that matches the physical reality of material spectra. They can additionally be used to smooth data\, calculate derivatives\, interpolate between points\, and more.\nThe first problem will look at the smoothing of noisy data for detecting small materials. When objects are smaller than the pixel\, the observed spectrum will mix the target with background materials. The problem of unmixing removes the influence of these background materials from observed data. If the object is too small\, the estimates from unmixing will be dominated by error. In this scenario\, splines will be used to smooth out these random variations.\nThe second problem will use splines to introduce a new solution to the problem of Temperature Emissivity Separation (TES). The physical quantity captured by the camera is radiance. For the detection of materials\, ground reflectance or emissivity are desired. TES is the process by which ground radiance is converted to material emissivity. Splines will be used to replace estimated roughness in this problem with an analytical solution.\nThe third and final application looks at using splines to detect gases without relying on image statistics. Gas features are sharp and only impact a narrow window of the spectrum. This application attempted to use splines to detect these sharp features by looking at the difference between the collected data and an interpolation across the feature.\nThe first two applications yielded interesting and useful results. The third application yielded some interesting conclusions about the general problem and improved methods for using splines in this space.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-owen-mcelhinney/
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DTSTART;TZID=America/New_York:20210804T130000
DTEND;TZID=America/New_York:20210804T140000
DTSTAMP:20260512T144411
CREATED:20210727T185554Z
LAST-MODIFIED:20210727T185554Z
UID:26747-1628082000-1628085600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Hooman Barati Sedeh
DESCRIPTION:MS Thesis Defense: Space-Time Graphene Metasurfaces \nHooman Barati Sedeh \nLocation: Zoom Link \nAbstract: The unprecedented growth of the data exchanged between wireless devices and the rapid emergence of high-quality wireless services have raised the demand for communication bandwidth and data transmission rates. This has motivated the migration of wireless networks toward the utilization of carrier waves with higher frequencies beyond the millimeter-wave band. Terahertz (THz) band is envisioned as one of the enabling technologies for future generations of wireless communication mobile networks (such as 6G) to address the needs of high-speed and bandwidth-intensive applications. THz communications are expected to provide broadband services to several wireless devices in the internet of things applications. The recent migration from internet-of-things to the internet of nano-things imposes certain constraints in terms of size\, weight\, and power (SWaP) on the THz antennas responsible for communications\, calling for the development of smart antennas with adaptive response capable of establishing multiple active data links through multi-beam scanning in a multiple-input\, multiple-output (MIMO) network while meeting the demands on the capacity and SWaP. Moreover\, provisioning a reliable communication channel that ensures the security of its users’ information remains vitally essential for the next generation of communication networks.\nMetasurfaces\, consisting of subwavelength elements\, are poised to enable improved free-space optical communications with low SWaP thanks to their small form factor and capability to provide unprecedented control over the wavefront of electromagnetic waves at the subwavelength scale. The recent investigations of active metasurfaces have aimed toward overcoming the fixed response of conventional metasurfaces and developing smart antenna systems with adaptive beamforming capabilities that can point the beam toward the desired users in real-time through pixelated control over the phase of the scattered wave. Moreover\, the adaptive communication by such quasi-static tunable metasurfaces can be secured by encrypting the transmitted data via holography to impose restrictions on the data access from an adversary. Despite the fruitful progress in this area\, quasi-static active metasurface face several challenges to meet the high demands on the capacity of communication due to their reliance on resonant phase shift accumulations which limit the operation bandwidth and hinders the scalability in terms of the number of channels in the account of non-trivial coupling effects between resonant unit cells. Furthermore\, these metasurfaces cannot be used for covert communication as they do not allow for engineering the spectral content of scattered light.\nThis thesis explores the roles of space and time in active metasurfaces for establishing adaptive and secure multichannel communication at low-THz frequency regime. As the primary goal of this work is twofold\, we will tackle each problem separately. At first\, we propose a technique for adaptive multichannel communication through simultaneous and independent multifrequency multibeam scanning via a single time-modulated metasurface consisting of graphene micro-patch antennas whose Fermi energy levels are modulated by radio-frequency biasing signals. To this aim\, we divide the metasurface aperture into interleaved orthogonally modulated sub-array antennas with distinct modulation frequencies\, rendering a shared aperture in space-time. The higher-order frequency harmonics generated by the sub-arrays in such a space-time shared-aperture metasurface are mutually orthogonal in the sense that they do not yield an observable interference pattern and can be separated by spectral filtering. A distinct constant progressive modulation phase delay is then adopted in each sub-array to independently scan its corresponding higher-order frequency harmonics via dispersionless modulation-induced phase gradient with minimal sidelobe level and full angle-of-view over a wide bandwidth. In the second part of this work\, we will propose another technique for establishing active secure communication links over single and multiple orthogonal frequency channels via a metasurface that consists of graphene micro-ribbons. To this aim\, the Fermi energy level of each graphene micro-ribbons is modulated via pseudo-random radio-frequency biasing signals whose DC offsets are adjusted to tilt the reflected beam toward the predefined direction by imposing a spatial phase gradient profile across the surface\, while their waveforms are engineered to expand the incident wave spectrum into a noise-like spectrum with a near-zero power spectral density via random modulation of the reflection phase of each element with respect to its offset phase. This permits for addressing a legitimate mobile user in real-time who can retrieve the incident signal via synchronous demodulation with the pseudo-random key of the metasurface while camouflaging the signal from the adversary by lowering the probability of detection and spectral encryption. The approach is then extended to enable multi-channel secure communication by dividing the metasurface into interleaved sub-arrays modulated with orthogonal pseudo-random keys\, which provides simultaneous and independent control over multiple beams with non-overlapping spread spectra which can be retrieved by independent legitimate users while rejecting unwarranted access by eavesdroppers as well as other users.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-hooman-barati-sedeh/
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