Immersive systems such as Augmented Reality (AR) devices are being integrated across multiple domains, from manufacturing to health, environment and education. The Industry 5.0 focus on human-centric interfaces to these systems and the desire to enable them for enhancing the skill-sets of the user, present new challenges. One of these is the accurate positional tracking of objects within the AR user's environment that is relevant in real-time applications. Multiple co-located frame based cameras are typical solutions proposed for object tracking in AR. The advent of commercial neuromorphic vision sensors (NVS) in recent years, introduce a novel, event-based approach to computer vision. Inspired by biological vision, these systems reduce the amount of data transferred and enhance power efficiency when tracking in real-time. This work leverages the strengths of AR and NVS technologies to develop an integrated system for tracking physical objects and providing users with required information to interact with these objects in a virtual environment. The computational pipeline from training on a deep neural network, coordinate transformations from image, to physical and virtual coordinate systems and testing the utility of the system for an AR system user is presented. The NVS system shows significant improvement over frame camera based systems in responding to varied lighting conditions.

The activation of antenna elements located at random positions on a linear aperture with constant currents so as to collaboratively transmit or receive in preferred directions is investigated. Assuming the elements are located in continuum spatially, the objective is to determine a probability distribution function for the element positions and characterize the random features of the resulting beam patterns. Two approaches are presented, one analytical and another that computationally determines the optimal positions using a firefly algorithm, where each firefly models an array with finite number of elements. The analytical model for the probability distribution is constrained to match the expected value of the beam response to the directivity function of a uniformly spaced element Dolph-Chebyshev array with a prescribed side lobe level. The variance in the side lobe amplitudes is shown to decrease as 1/(2√N) where is the number of elements. The firefly algorithm simulates the exploratory and convergent behavior of a swarm of fireflies, attracted to one another through the light they emit, which is emulated through a fitness function. Multiple ensembles from the computational model yield a set of viable arrays that are shown to exhibit a lower variance than the analytical model. The computational model also yields a set of conditional probability distributions that capture the correlation between neighboring inter-element distances. Arrays constructed by sampling from these conditional distributions yield a reduced variance in the side lobe amplitudes, using fewer elements than that required under the independent sampling assumption of the analytical model.

The spatial correlation features of channel impulse responses for an indoor environment are presented. The wide-hand impulse responses of a rectangular enclosure are calculated using the method of images. Bandpass representations of the non line-of-sight multipath responses are obtained for 2.4 GHz ISM band and their spatial cross correlation estimated for various positions of the transmitter and receiver arrays. The receiver and transmitter correlations are shown to exhibit periodic features and a spatial dependence in the decay rate. The correlation minima decreases with transmitter-receiver (T-R) separation and peak-to-peak correlation amplitude exhibits larger values when the transmitter and receivers are positioned close to the corners of the room. The oscillatory features in the spatial correlation may be attributed to the distribution of multipath clusters in time. For certain configurations of the T-R arrays the clusters are well separated resulting in a slower rate of oscillation at the inter-cluster time scale. In the short or intra-cluster time-scale, the correlation can exhibit a slow decay rate. At the corners, these clusters appear at faster rate and individual clusters are of a shorter duration. This feature leads to a faster and more structured oscillation in the correlation features. These observations are important considerations in design and evaluation of the performance of multiple-output multiple-input wireless communications systems.

Variable bit rate (VBR) H.261 encoded video traffic is modeled by a nonlinear time series process. A threshold autoregressive (TAR) process is of particular interest. The TAR model is comprised of a set of autoregressive (AR) processes that are switched according to the amplitude of bit rates of the time series. The TAR model is shown to capture accurately the statistical characteristics of multiplexed video sources. The buffer occupancy in a first-in first-out (FIFO) deterministic service queue using sources modeled by a TAR is compared with Markov chain based processes and it is shown that a TAR model of lower complexity yields comparable results.

A statistical analysis of Internet traffic measurements from a campus site is carried out to examine the influence of the constituent protocols and applications on the characteristics of the aggregate stream and on packet loss statistics. While TCP remains the dominant traffic protocol through all hours of the day, a mixture of both well-known (http, ftp, nntp and smtp) and less known applications contribute significant portions to the TCP traffic mix. Statistical tests show that the aggregate TCP packet arrival process exhibits both non-stationary and nonlinear features. By filtering a subset of the applications found to exhibit non-stationary features from the aggregate process, a stationary traffic stream is derived. This filtered traffic process is modeled using nonlinear threshold autoregressive processes. The traffic model is shown to provide good agreement with the measurement trace in the packet loss statistics. The proposed parametric model allows the design of traffic shapers and provides a simple and accurate approach for simulating Internet data traffic patterns. 1.

This paper presents a source model for VBR video traffic. A finite-state Markov chain is shown to accurately model one- and two-layer video of all activity levels on a per source basis. Our model captures the source dynamics, including the short-term correlations essential for studying network performance. The modeling technique is shown to be applicable for both H.261 and MPEG2 encoded video of a variety of activity levels. The traffic model is shown in a simulation study to be able to accurately characterize both the single-source buffer occupancy over a wide range of buffer sizes and the multiplexing behavior. The VBR video model is also used to model the enhancement layer of two-layer SNR scalable video. We show that two-layer encoding has significantly better statistical multiplexing gains than one-layer video, particularly when the network admits calls based on a leaky-bucket characterization.

A model for multiplexed video sources and the resulting packet delay distribution for a fluid buffer is given. A measurement driven Markov chain based traffic model for a variable bit rate (VBR) source is used in the analysis. A characterization of the queueing delays of a multiplexed stream comprised of statistically identical sources is undertaken. It is shown that inadequate spectral content in the single source generator matrix manifests in an underestimate of the packet delay probabilities for the multiplexed stream. A reduced dimension generator matrix is derived for the multiplexed stream that amply models the spectral content of the multiplexed sources. The simulated and calculated results using the aforementioned model are shown to agree favorably.

Ultrasonic scattering in inhomogeneous fluid media is examined. Of particular interest are those media having a compressibility that varies on a continuum of spatial scales. It is shown that the internal features of such media can be extracted by an analysis of the forward scattered field. The angular distribution of scattered pressure intensity is found to vary with frequency and compressibility contrast. The scattered field data can be rendered stationary to such variations by an application of a similarity transformation. Examples are given for cases where the spatial variation in the compressibility of the media obeys either a power law or exponential correlation function. When these similarity transformations are applied, an estimate of the characteristic radius of the scatterers and the fractal dimension of the medium can be directly obtained.>